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References

Published online by Cambridge University Press:  19 August 2019

Stéphane Jacquemoud
Affiliation:
Université Paris Diderot
Susan Ustin
Affiliation:
University of California, Davis
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Publisher: Cambridge University Press
Print publication year: 2019

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References

A.M.S. (1909), The temperature relations of foliage leaves, New Phytologist, 8(2):7780.Google Scholar
Aalto, T., Vesala, T., Mattila, T., Simbierowicz, P. and Hari, P. (1999), A three-dimensional stomatal CO2 exchange model including gaseous phase and leaf mesophyll separated by irregular interfaces, Journal of Theoretical Biology, 196(1):115128.CrossRefGoogle Scholar
Aalto, T. and Juurola, E. (2002), A three-dimensional model of CO2 transport in airspaces and mesophyll cells of a silver birch leaf, Plant, Cell & Environment, 25(11):13991409.CrossRefGoogle Scholar
Abbott, L.A. and Lindenmayer, A. (1981), Models for growth of clones in hexagonal cell arrangements: applications in Drosophila wing disc epithelia and plant epidermal tissues, Journal of Theoretical Biology, 90(4):495544.Google Scholar
Abdel-Rahman, E.M., Fethi, F.B., van den Berg, M. and Way, M.J. (2010), Potential of spectroscopic data sets for sugarcane thrips (Fulmekiola serrata Kobus) damage detection, International Journal of Remote Sensing, 31(15):41994216.CrossRefGoogle Scholar
Aber, J.D., Bolster, K.L., Newman, S.D., Soulia, M. and Martin, M.E. (1994), Analyses of forest foliage. II: measurement of carbon fraction and nitrogen content by end-member analysis, Journal of Near Infrared Spectroscopy, 2(1):1523.Google Scholar
Abera, M.K., Retta, M.A., Verboven, P., Nicolai, B.M., Berghuijs, H. and Struik, P. (2016), Virtual microstructural leaf tissue generation based on cell growth modeling, Acta Horticulturae, 1110:155162.CrossRefGoogle Scholar
Abou-Khaled, A., Hagan, R.M. and Davenport, D.C. (1970), Effects of kaolinite as a reflective antitranspirant on leaf temperature, transpiration, photosynthesis, and water-use efficiency, Water Resources Research, 6(1):280289.Google Scholar
Acem, Z., Parent, G., Monod, B., Jeandel, G. and Boulet, P. (2010), Experimental study in the infrared of the radiative properties of pine needles, Experimental Thermal and Fluid Science, 34(7):893899.Google Scholar
Acevedo, M.F. and Ataroff, M. (2012), Leaf spectra and weight of species in canopy, subcanopy, and understory layers in a Venezuelan Andean cloud forest, Scientifica, 2012: Article ID 839584, 14 pages.Google Scholar
Achard, F.K. (1778), Mémoire sur les couleurs des végétaux. Première partie, Nouveaux Mémoires de l’Académie Royale des Sciences et Belles-Lettres de Berlin, 1:6269 (Archiv der Berlin-Brandenburgischen Akademie der Wissenschaften).Google Scholar
Adams, J. B. and Adams, J.D. (1984), Geologic mapping using Landsat MSS and TM images: removing vegetation by modeling spectral mixtures, in Proc. International Symposium on Remote Sensing of Environment Third Thematic Conference: Remote Sensing for Exploration Geology, Ann Arbor, MI, 16–19 April 1984, ERIM, pp. 615622.Google Scholar
Adams, J.B., Smith, M.O. and Johnson, P.E. (1986), Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander I site, Journal of Geophysical Research, 91(B8):80988112.CrossRefGoogle Scholar
Adams, M.L., Norvell, W.A., Peverly, J.H. and Philpot, W.D. (1993), Fluorescence and reflectance characteristics of manganese deficient soybean leaves: effects of leaf age and choice of leaflet, Plant and Soil, 155–156(1):235238.Google Scholar
Adams, M.L., Philpot, W.D. and Norvell, W.A. (1999), Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation, International Journal of Remote Sensing, 20(18):36633675.Google Scholar
Adams, M.L., Norvell, W.A., Philpot, W.D. and Peverly, J.H. (2000a), Spectral detection of micronutrient deficiency in “Bragg” soybean, Agronomy Journal, 92(2):261268.Google Scholar
Adams, M.L., Norvell, W.A., Philpot, W.D. and Peverly, J.H. (2000b), Toward the discrimination of manganese, zinc, copper, and iron deficiency in “Bragg” soybean using spectral detection methods, Agronomy Journal, 92(2):268274.Google Scholar
Adler, P.M. (1992), Porous Media: Geometry and Transports, Butterworth-Heinemann, 560 pages.Google Scholar
Adler, P.M., Jacquin, C.G. and Thovert, J.F. (1992), The formation factor of reconstructed porous media, Water Resources Research, 28(6):15711576.Google Scholar
Afandi, S.D., Herdiyeni, Y., Prasetyo, L.B., Hasbi, W., Arai, K. and Okumurad, H. (2016), Nitrogen content estimation of rice crop based on near infrared (NIR) reflectance using artificial neural network (ANN), Procedia Environmental Sciences, 33:6369.Google Scholar
Afzal, A. and Mousavi, S.F. (2008), Estimation of moisture in maize leaf by measuring leaf dielectric constant, International Journal of Agriculture & Biology, 10(1):6668.Google Scholar
Afzal, A., Mousavi, S.F. and Khademi, M. (2010), Estimation of leaf moisture content by measuring the capacitance, Journal of Agricultural Science and Technology, 12(3):339346.Google Scholar
Agarwal, U.P. and Atalla, R.H. (2010), Vibrational spectroscopy, in Lignin and Lignans: Advances in Chemistry (Heitner, C., Dimmel, D.R. and Schmidt, J.A., Eds), CRC Press, pp. 103136.CrossRefGoogle Scholar
Agati, G., Fusi, F., Mazzinghi, P. and Dipaolo, M.L. (1993), A simple approach to the evaluation of the reabsorption of chlorophyll fluorescence spectra in intact leaves, Journal of Photochemistry and Photobiology B, 17(2):163171.CrossRefGoogle Scholar
Agati, G., Mazzinghi, P., Fusi, F. and Ambrosini, I. (1995), The F685/F730 chlorophyll fluorescence ratio as a tool in plant physiology: response to physiological and environmental factors, Journal of Plant Physiology, 145(3):228238.Google Scholar
Agati, G., Mazzinghi, P., Lipucci di Paola, M., Fusi, F. and Cecchi, G. (1996), The F685/F730 chlorophyll fluorescence ratio as indicator of chilling stress in plants, Journal of Plant Physiology, 148 (3–4):384390.CrossRefGoogle Scholar
Agati, G. (1998), Response of the in vivo chlorophyll fluorescence spectrum to environmental factors and laser excitation wavelength, Pure and Applied Optics, 7(4):797807.Google Scholar
Agati, G., Cerovic, Z.G. and Moya, I. (2000), The effects of decreasing temperature up to chilling values on the in vivo F685/F735 chlorophyll fluorescence ratio in Phaseolus vulgaris and Pisum sativum: the role of photosystem I contribution to the 735 nm fluorescence band, Photochemistry and Photobiology, 72(1):7584.Google Scholar
Agati, G., Brunetti, C., Di Ferdinando, M., Ferrini, F., Pollastri, S. and Tattini, M. (2013), Functional roles of flavonoids in photoprotection: new evidence, lessons from the past, Plant Physiology and Biochemistry, 72:3545.Google Scholar
Agrios, G. (2004), Plant Pathology, Elsevier, 952 pages.Google Scholar
Ahern, F.J. (1988), The effects of bark beetle stress on the foliar spectral reflectance of lodgepole pine, International Journal of Remote Sensing, 9(9):14511468.CrossRefGoogle Scholar
Ainsworth, E.A., Serbin, S.P., Skoneczka, J.A. and Townsend, P.A. (2014), Using leaf optical properties to detect ozone effects on foliar biochemistry, Photosynthesis Research, 119(1–2):6576.Google Scholar
Al-Abbas, A.H., Barr, R., Hall, J.D., Crane, F.L. and Baumgardner, M.F. (1974), Spectra of normal and nutrient-deficient maize leaves, Agronomy Journal, 66(1):1620.Google Scholar
Alberts, B. (2004), Biologie moléculaire de la cellule, Flammarion Médecine-Sciences, 1472 pages.Google Scholar
Alenius, C.M., Vogelmann, T.C. and Bornman, J.F. (1995), A three-dimensional representation in the relationship between penetration of u.v.-B radiation and u.v.-screening pigments in leaves of Brassica napus, New Phytologist, 131(3):297302.Google Scholar
Ali, A.M., Darvishzadeh, R., Skidmore, A.K., van Duren, I., Heiden, U. and Heurich, M. (2015), PROSPECT inversion for indirect estimation of leaf dry matter content and specific leaf area, in Proc. 36th International Symposium on Remote Sensing of Environment, Berlin, Germany, 11–15 May 2015, ISPRS, pp. 277284.Google Scholar
Ali, A.M., Darvishzadeh, R., Skidmore, A.K., van Duren, I., Heiden, U. and Heurich, M. (2016), Estimating leaf functional traits by inversion of PROSPECT: assessing leaf dry matter content and specific leaf area in mixed mountainous forest, International Journal of Applied Earth Observation and Geoinformation, 45(part A):6676.Google Scholar
Allaby, M. (2006), Dictionary of Plant Sciences, Oxford University Press, 508 pages.Google Scholar
Allen, E. (1964), Fluorescent white dyes: calculation of fluorescence from reflectivity values, Journal of the Optical Society of America, 54(4):506515.Google Scholar
Allen, L.H., Gausman, H.W. and Allen, W.A. (1975), Solar ultraviolet radiation in terrestrial plant communities, Journal of Environmental Quality, 4(3):285294.Google Scholar
Allen, W.A. and Richardson, A.J. (1968), Interaction of light with a plant canopy, Journal of the Optical Society of America, 58(8):10231028.CrossRefGoogle Scholar
Allen, W.A., Gausman, H.W., Richardson, A.J. and Thomas, J.R. (1969), Interaction of isotropic light with a compact plant leaf, Journal of the Optical Society of America, 59(10):13761379.Google Scholar
Allen, W.A., Gausman, H.W. and Richardson, A.J. (1970a), Mean effective optical constants of cotton leaves, Journal of the Optical Society of America, 60(4):542547.CrossRefGoogle Scholar
Allen, W.A., Gausman, H.W. and Richardson, A.J. (1973), Willstätter-Stoll theory of leaf reflectance evaluated by ray tracing, Applied Optics, 12(10):24482453.Google Scholar
Allen, W.A., Gausman, H.W., Richardson, A.J. and Wiegand, C.L. (1970b), Mean effective optical constants of thirteen kinds of plant leaves, Applied Optics, 9(11):25732577.Google Scholar
Aluru, M.R., Bae, H., Wu, D. and Rodermel, S.R. (2001), The Arabidopsis immutans mutation affects plastid differentiation and the morphogenesis of white and green sectors in variegated plants, Plant Physiology, 127(1):6777.Google Scholar
Alvarez-Añorve, M.Y., Quesada, M., Sánchez-Azofeifa, G.A., Avila-Cabadilla, L.D. and Gamon, J.A. (2012), Functional regeneration and spectral reflectance of trees during succession in a highly diverse tropical dry forest ecosystem, American Journal of Botany, 99(5):816826.CrossRefGoogle Scholar
Álvarez-Arenas, T.E.G., Sancho-Knapik, D., Peguero-Pina, J.J. and Gil-Pelegrín, E. (2009a), Determination of plant leaves water status using air-coupled ultrasounds, in Proc. 2009 IEEE International Ultrasonics Symposium (IUS), Rome, Italy, 20–23 September 2009, IEEE, pp. 771774.Google Scholar
Álvarez-Arenas, T.E.G., Sancho-Knapik, D., Peguero-Pina, J.J. and Gil-Pelegrín, E. (2009b), Noncontact and noninvasive study of plant leaves using air-coupled ultrasounds, Applied Physics Letters, 95:193702.Google Scholar
Alves, P.L.C.A., Magalhães, A.C.N. and Barja, P.R. (2002), The phenomenon of photoinhibition of photosynthesis and its importance in reforestation, The Botanical Review, 68(2):193208.Google Scholar
Amigues, S. (2003), Théophraste. Recherches sur les plantes: Livres I-II, Les Belles Lettres, 143 pages.Google Scholar
André, Y.M. (1746), Discours sur les merveilles de l’arc-en-ciel, Ganeau Editeur., Paris, pp. 145198.Google Scholar
Andrieu, B., Baret, F., Schellberg, J. and Rinderle, U. (1988), Estimation de spectres de feuilles à partir de mesures dans les bandes spectrales larges, in Proc. 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France, 18–22 January 1988, ESA, Vol. SP-287, pp. 351356.Google Scholar
Andrieu, B., Kiriakos, S. and Jaggard, K.W. (1992), Estimation de la concentration en chlorophylles de feuilles par mesure de leur réflectance ou par analyse numérique de photographies prises au laboratoire, Agronomie, 12(6):477485.Google Scholar
Ångström, A.J. (1853), Om växternas gröna färg, Öfversigt af Kongl. Vetenskaps-akademiens forhandlingar, 10:246251.Google Scholar
Ångström, A.J. (1854), Ueber die grüne Farbe der Pflanzen, Annalen der Physik, 169(11):475480.Google Scholar
Ansari, A.Q. and Loomis, W.E. (1959), Leaf temperatures, American Journal of Botany, 46(10):713717.Google Scholar
Antonovics, J., Bradshaw, A.D. and Turner, R.G. (1971), Heavy metal tolerance in plants, in Advances in Ecological Research (Cragg, J.B., Ed.), Academic Press, pp. 186.Google Scholar
Antunez, A., Whiting, M.D., Pierce, F.J. and Stöckle, C. (2008), Estimation of sweet cherry tree water status by spectral reflectance, Acta Horticulturae, 795:711715.Google Scholar
Aoki, M., Yabuki, K. and Totsuka, T. (1980), Remote sensing of the physiological functions of plants by infrared color aerial photography. I: Relation between leaf reflectivity ratio, bi-band ratio and photosynthetic function of leaves in several woody plants, Research Report from the National Institute for Environmental Studies, 11:225237.Google Scholar
Aoki, M., Yabuki, K. and Totsuka, T. (1988), Effective spectral characteristics of leaf for the remote sensing of leaf water content Journal of Agricultural Meteorology, 44(2):111117 (in Japanese).Google Scholar
Aoki, M., Yabuki, K., Totsuka, T. and Nishida, M. (1986), Remote sensing of chlorophyll content of leaf. I: Effective spectral reflection characteristics of leaf for the evaluation of chlorophyll content in leaves of Dicotyledons, Environmental Control in Biology, 24(1):2126.Google Scholar
Archetti, M. (2000), The origin of autumn colours by coevolution, Journal of Theoretical Biology, 205(4):625630.Google Scholar
Archetti, M. (2009a), Classification of hypotheses on the evolution of autumn colours, Oikos, 118(3):328333.Google Scholar
Archetti, M. (2009b), Phylogenetic analysis reveals a scattered distribution of autumn colours, Annals of Botany, 103(5):703713.Google Scholar
Archetti, M. and Brown, S.P. (2004), The coevolution theory of autumn colours, Proceedings of the Royal Society of London. Series B, 271(1545):12191223.Google Scholar
Archetti, M. and Leather, S.R. (2005), A test of the coevolution theory of autumn colours: colour preference of Rhopalosiphum padi on Prunus padus, Oikos, 110(2):339343.Google Scholar
Archetti, M., Döring, T.F., Hagen, S.BHughes, N.M., Leather, S.R., Lee, D.W., et al. (2009c), Unravelling the evolution of autumn colours: an interdisciplinary approach, Trends in Ecology & Evolution, 24(3):166173.Google Scholar
Archetti, M., Richardson, A.D., O’Keefe, J. and Delpierre, N. (2013), Predicting climate change impacts on the amount and duration of autumn colors in a New England forest, PLOS One 8(3):e57373.Google Scholar
Arnold, L., Gillet, S., Lardière, O., Riaud, P. and Schneider, J. (2002), A test for the search for life on extrasolar planets, Astronomy & Astrophysics, 392(1):231237.Google Scholar
Arroyo-Mora, J.P., Kalacska, M., Caraballo, B.L., Trujillo, J.E. and Vargas, O. (2008), Spectral expression of gender: a pilot study with two dioecious neotropical tree species, in Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests (Kalacska, M. and Sánchez-Azofeifa, G.A., Eds), CRC Press, pp. 125140.Google Scholar
Ashourloo, D., Mobasheri, M.R. and Huete, A. (2014), Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina), Remote Sensing, 6(6):47234740.Google Scholar
Askenasy, E. (1875), Ueber die Temperatur welche Pflanzen im Sonnenlicht annehmen, Botanische Zeitung, 33:442443.Google Scholar
Asmail, C. (1991), Bidirectional Scattering Distribution Function (BSDF): a systematized bibliography, Journal of Research of the National Institute of Standards and Technology, 96(2):215223.Google Scholar
Asner, G.P., Wessman, C.A., Schimel, D.S. and Archer, S. (1998), Variability in leaf and litter optical properties: implications for BRDF model inversions using AVHRR, MODIS, and MISR, Remote Sensing of Environment, 63(3):243257.Google Scholar
Asner, G.P. (2008), Hyperspectral remote sensing of canopy chemistry, physiology, and biodiversity in tropical rainforests, in Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests (Kalacska, M. and Sánchez-Azofeifa, G.A., Eds), CRC Press, pp. 261296.Google Scholar
Asner, G.P., Jones, M.O., Martin, R.E., Knapp, D.E. and Hughes, R.F. (2008a), Remote sensing of native and invasive species in Hawaiian forests, Remote Sensing of Environment, 112(5):19121926.Google Scholar
Asner, G.P. and Martin, R.E. 2008b), Spectral and chemical analysis of tropical forests: caling from leaf to canopy levels, Remote Sensing of Environment, 112(10):39583970.CrossRefGoogle Scholar
Asner, G.P. and Martin, R.E. (2009), Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests, Frontiers in Ecology and the Environment, 7(5):269276.Google Scholar
Asner, G.P. and Martin, R.E. (2011), Canopy phylogenetic, chemical and spectral assembly in a lowland Amazonian forest, New Phytologist, 189(4):9991012.Google Scholar
Asner, G.P. and Martin, R.E. (2016), Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing, Global Ecology and Conservation, 8:212219.Google Scholar
Asner, G.P., Martin, R.E., Ford, A.J., Metcalfe, D.J. and Liddell, M.J. (2009), Leaf chemical and spectral diversity in Australian tropical forests, Ecological Applications, 19(1):236253.Google Scholar
Asner, G.P., Martin, R.E., Knapp, D.E., Tupayachi, R., Anderson, C., Carranza, L., et al. (2011a), Spectroscopy of canopy chemicals in humid tropical forests, Remote Sensing of Environment, 115(12):35873598.Google Scholar
Asner, G.P., Martin, R.E., Tupayachi, R., Emerson, R., Martinez, P., Sinca, F., et al. (2011b), Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests, Ecological Applications, 21(1):8598.Google Scholar
Asner, G.P., Martin, R.E. and Bin Suhaili, A. (2012), Sources of canopy chemical and spectral diversity in lowland Bornean forest, Ecosystems, 15(3):504517.Google Scholar
Asner, G.P. (2014), A chemical-evolutionary basis for remote sensing of tropical forest diversity, in Forests and Global Change (Coomes, D.A., Burslem, D.F.R.P. and Simonson, D., Eds), Cambridge University Press, pp. 343358.Google Scholar
Asner, G.P., Martin, R.E., Carranza-Jimenez, L., Sinca, F., Tupayachi, R., Anderson, C.B., et al. (2014), Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region, New Phytologist, 204(1):127139.CrossRefGoogle ScholarPubMed
Asrar, G. (1989), Theory and Applications of Optical Remote Sensing, Wiley Interscience, 734 pages.Google Scholar
Atkinson, M.D., Jervis, A.P. and Sangha, R.S. (1997), Discrimination between Betula pendula, Betula pubescens, and their hybrids using near-infrared reflectance spectroscopy, Canadian Journal of Forest Research, 27(11):18961900.Google Scholar
Atrashevskii, Y.I., Sikorskii, A.V., Sikorskii, V.V., Stel’makh, G.F. and Shuplyak, V.I. (1998), Spectral polarization characteristics of optical radiation reflected from leaves subjected to unfavorable ecological factors, Journal of Applied Spectroscopy, 65 (1):110116 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 65(1):107113, 1998).Google Scholar
Atrashevskii, Y.I., Sikorskii, A.V., Sikorskii, V.V. and Stel’makh, G.F. (1999), The reflection and scattering of light by a plant leaf, Journal of Applied Spectroscopy, 66(1):105114 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 66(1):101108, 1999).Google Scholar
Ausmus, B.S. and Hilty, J.W. (1972), Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves, Remote Sensing of Environment, 2:7781.Google Scholar
Avalos, G., Mulkey, S.S. and Kitajima, K. (1999), Leaf optical properties of trees and lianas in the outer canopy of a tropical dry forest, Biotropica, 31(3):517520.CrossRefGoogle Scholar
Averill, B. and Eldredge, P. (2011), General Chemistry: Principles, Patterns, and Applications, Saylor Foundation, https://open.umn.edu/opentextbooks/BookDetail.aspx?bookId=69.Google Scholar
Azia, F. and Stewart, K.A. (2001), Relationships between extractable chlorophyll and SPAD values in muskmelon leaves, Journal of Plant Nutrition, 24(6):961966.CrossRefGoogle Scholar
Bacci, L., Benincasa, F. and Rapi, B. (1996), Effect of growth temperature on the spectrocolorimetric characteristics of sorghum plants (Sorghum bicolor (L.) Moench.): indices of stress, European Journal of Agronomy, 5(1–2):4557.Google Scholar
Bacci, L., De Vincenzi, M., Rapi, B., Arca, B. and Benincasa, F. (1998), Two methods for the analysis of colorimetric components applied to plant stress monitoring, Computers and Electronics in Agriculture, 19(2):167186.Google Scholar
Bacour, C., Jacquemoud, S., Leroy, M., et al. (2002a), Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data, Agronomie, 22:555565.Google Scholar
Bacour, C., Jacquemoud, S., Tourbier, Y., Dechambre, M. and Frangi, J.P. (2002b), Design and analysis of numerical experiments to compare four canopy reflectance models, Remote Sensing of Environment, 79(1):7283.Google Scholar
Baffioni, C., Careri, G. and Giansanti, A. (1985), Leaf tissue desiccation process: a dielectric-gravimetric study, Lettere Al Nuovo Cimento, 42(6):295298.Google Scholar
Bagard, M., Le Thiec, D., Delacote, E., Hasenfratz-Sauder, M.P., Banvoy, J., Gerard, J., et al. (2008), Ozone-induced changes in photosynthesis and photorespiration of hybrid poplar in relation to the developmental stage of the leaves, Physiologia Plantarum, 134(4):559574.Google Scholar
Bailey, L.H. (1963), How Plants Get Their Names, Dover Publications, 181. pages.Google Scholar
Bailey, S., Walters, R.G., Jansson, S. and Horton, P. (2001), Acclimation of Arabidopsis thaliana to the light environment: the existence of separate low light and high light responses, Planta, 213(5):794801.Google Scholar
Baldini, E., Facini, O., Nerozzi, F., Rossi, F. and Rotondi, A. (1997), Leaf characteristics and optical properties of different woody species, Trees – Structure and Function, 12(2):7381.CrossRefGoogle Scholar
Baldy, R.W., de Benedictis, J.A., Johnson, L.F., Weber, E., Baldy, M.W., Osborn, B.P et al. (1996), Leaf color and vine size are related to yield in a phylloxera-infested vineyard, Vitis, 35(4):201205.Google Scholar
Bálint, J., Nagy, B.V. and Fail, J. (2013), Correlations between colonization of onion thrips and leaf reflectance measures across six cabbage varieties, PLOS One, 8(9):e73848.Google Scholar
Ball, A., Sanchez-Azofeifa, A.G., Portillo-Quintero, C., Rivard, B., Castro-Contreras, S. and Fernandes, G. (2015), Patterns of leaf biochemical and structural properties of Cerrado life forms: implications for remote sensing, PLOS One, 10(2):e0117659.Google Scholar
Baker, E.A. (1982), Chemistry and morphology of plant epicuticular waxes, in The Plant Cuticle (Cutler, D.F., Alvin, K.L. and Price, C.E., Eds), Academic Press, pp. 139165.Google Scholar
Baltzer, J.L. and Thomas, S.C. (2005), Leaf optical responses to light and soil nutrient availability in temperate deciduous trees, American Journal of Botany, 92(2):214223.Google Scholar
Banchoff, T. (1996), La quatrième dimension – Voyage dans les dimensions supérieures, Pour la Science, Paris, 206 pages.Google Scholar
Bandaru, V., Hansen, D.J., Codling, E.E., Daughtry, C.S., White-Hansen, S. and Green, C.E. (2010), Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing, International Journal of Remote Sensing, 31(15):41634177.Google Scholar
Baranoski, G.V.B. and Rokne, J.G. (1997), An algorithmic reflectance and transmittance model for plant tissue, Computer Graphics Forum, 16(3):C141C150.Google Scholar
Baranoski, G.V.B. and Rokne, J.G. (1999), A non-deterministic reconstruction approach for isotropic reflectances and transmittances, Journal of Visualization and Computer Animation, 10(4):225231.Google Scholar
Baranoski, G.V.B. and Rokne, J.G. (2001), Efficiently simulating scattering of light by leaves, Visual Computer, 17(8):491505.Google Scholar
Baranoski, G.V.B. and Rokne, J.G. (2004), Light Interaction with Plants – A Computer Graphics Perspective, Horwood Publishing, 154 pages.Google Scholar
Baranoski, G.V.B. and Rokne, J.G. (2005), A practical approach for estimating the red edge position of plant leaf reflectance, International Journal of Remote Sensing, 26(3):503521.Google Scholar
Baranoski, G.V.G. (2006), Modeling the interaction of infrared radiation (750 to 2500 nm) with bifacial and unifacial plant leaves, Remote Sensing of Environment, 100(3):335347.Google Scholar
Baranoski, G.V.G. and Eng, D. (2007), An investigation on sieve and detour effects affecting the interaction of collimated and diffuse infrared radiation (750 to 2500 nm) with plant leaves, IEEE Transactions on Geoscience and Remote Sensing, 45(8):25932599.Google Scholar
Baret, F., Andrieu, B. and Guyot, G. (1988), A simple model for leaf optical properties in visible and near infrared: application to the analysis of spectral shifts determinism, in Applications of Chlorophyll Fluorescence in Photosynthesis Research, Stress Physiology, Hydrobiology and Remote Sensing (Lichtenthaler, H.K., Ed), Kluwer Academic Publishers, Dordrecht, pp. 345351.CrossRefGoogle Scholar
Baret, F., Jacquemoud, S., Guyot, G. and Leprieur, C. (1992), Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands, Remote Sensing of Environment, 41(2–3):133142.Google Scholar
Baret, F., Vanderbilt, V.C., Steven, M.D. and Jacquemoud, S. (1994), Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties, Remote Sensing of Environment, 48(2):253260.Google Scholar
Baret, F. and Fourty, T. (1997), Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements, Agronomie, 17(9–10):455464.Google Scholar
Baret, F. and Buis, S. (2008), Estimating canopy characteristics from remote sensing observations: review of methods and associated problems, in Advances in Land Remote Sensing – System, Modeling, Inversion and Application (Liang, S., Ed.), Springer, pp. 173201.Google Scholar
Barnes, J.D. and Cardoso-Vilhena, J. (1996), Interaction between electromagnetic radiation and the plant cuticle, in Plant Cuticles: an Integrated Functional Approach (Kerstiens, G.K., Ed), BIOS Scientific Publishers Limited, Oxford, pp. 157174.Google Scholar
Barnes, P.W., Flint, S.D., Slusser, J.R., Gao, W. and Ryel, R.J. (2008), Diurnal changes in epidermal UV transmittance of plants in naturally high UV environments, Physiologia Plantarum, 133(2):363372.Google Scholar
Barry, K.M., Newnham, G.J. and Stone, C. (2009), Estimation of chlorophyll content in Eucalyptus globulus foliage with the leaf reflectance model PROSPECT, Agricultural and Forest Meteorology, 149(6–7):12091213.Google Scholar
Barry, K.M., Corkrey, R., Thi, H.P., Ridge, S. and Mohammed, C.L. (2011), Spectral characterization of necrosis from reflectance of Eucalyptus globulus leaves with Mycosphaerella leaf disease or subjected to artificial lesions, International Journal of Remote Sensing, 32(24):92439259.Google Scholar
Barthlott, W., Mutke, J., Rafiqpoor, D., Kier, G. and Kreft, H. (2005), Global centers of vascular plant diversity, Nova Acta Leopoldina, 92(342):6183.Google Scholar
Bartlett, M.K., Ollinger, S.V., Hollinger, D.Y., Wicklein, H.F. and Richardson, A.D. (2011), Canopy-scale relationships between foliar nitrogen and albedo are not observed in leaf reflectance and transmittance within temperate deciduous tree species, Botany-Botanique, 89(7):491497.Google Scholar
Barton, C.V.M. (2001), A theoretical analysis of the influence of heterogeneity in chlorophyll distribution on leaf reflectance, Tree Physiology, 21(12–13):789795.CrossRefGoogle ScholarPubMed
Basayigit, L., Albayrak, S. and Senol, H. (2014), Analysis of VNIR reflectance for prediction of macro and micro nutrient and chlorophyll contents in apple trees (Malus communis), Asian Journal of Chemistry, 21(2):13021308.Google Scholar
Basu, S.K. (2006), Realistic Rendering of Plant Leaves, University of North Carolina, Chapel Hill, NC, 9 May 2006, 7 pages.Google Scholar
Bauer, S.D., Korč, F. and Förstner, W. (2011), The potential of automatic methods of classification to identify leaf diseases from multispectral images, Precision Agriculture, 12(3):361377.Google Scholar
Bauerle, W.L., Weston, D.J., Bowdena, J.D., Dudley, J.B. and Toler, J.E. (2004), Leaf absorptance of photosynthetically active radiation in relation to chlorophyll meter estimates among woody plant species, Scientia Horticulturae, 101(1–2):169178.Google Scholar
Bawhey, C.I. and Grant, R.H. (2003), Effect of epicuticular wax on UV scattering of sorghum leaves and canopies, in Proc. Ultraviolet Ground- and Space-based Measurements, Models, and Effects III (Slusser J.R., Herman J.R. and Gao W., Eds), San Diego, CA, 4 August 2003, SPIE, Vol. 5156, pp. 236244.Google Scholar
Bayer, A. (2004), Painters of Reality: the Legacy of Leonardo and Caravaggio in Lombardy, Metropolitan Museum of Art, 257 pages.Google Scholar
Baynes, J. (2007), The reflectance signature of canopy components: implications for the interpretation of remotely sensed images, Annals of Tropical Research, 29(1):2131.Google Scholar
Becher, J.J. (1669), Physica subterranea, Georg Ernst Stahl, Leipzig (1738), 503 pages.Google Scholar
Becquerel, E. (1868), La lumière, ses causes et ses effets, Librairie de Firmin Didot Frères, 377 pages.Google Scholar
Behera, R.K. and Choudhury, N.K. (2002), High irradiance induced pigment degradation and loss of photochemical activity of wheat chloroplasts, Biologia Plantarum, 45(1):4549.Google Scholar
Behmann, J., Mahlein, A.K., Rumpf, T., Römer, C. and Plümer, L. (2015), A review of advanced machine learning methods for the detection of biotic stress in precision crop protection, Precision Agriculture, 16(3):239260.Google Scholar
Belanger, M.J. (1990), A seasonal Perspective of several leaf developmental characteristics as related to the red edge of plant leaf reflectance, Master of Science Thesis, Faculty of Graduate Studies, York University, North York, ON, 110 pages.Google Scholar
Belanger, M.J., Miller, J.R. and Boyer, G. (1995), Comparative relationships between some red edge parameters and seasonal leaf chlorophyll concentrations, Canadian Journal of Remote Sensing, 21(1):1621.Google Scholar
Bell, I.E. and Baranoski, G.V.G. (2004), Reducing the dimensionality of plant spectral databases, IEEE Transactions on Geoscience and Remote Sensing, 42(3):570576.Google Scholar
Bellante, G.J., Powell, S.L., Lawrence, R.L., Repasky, K.S. and Dougher, T. (2014), Hyperspectral detection of a subsurface CO2 leak in the presence of water stressed vegetation, PLOS One, 9(10):e108299.Google Scholar
Belov, N.P., Sherstobitova, A.S., Yaskov, A.D. (2011), Diffuse reflection of light by cellulose pulp and optical absorption of aqueous residual lignin solutions, Journal of Applied Spectroscopy, 78(1):138140.Google Scholar
Belyaev, B.I., Belyaev, Y.V., Chumakov, A.V., Nekrasov, V.P. and Shuplyak, V.I. (2000), Spectral and spectral-polarization characteristics of potato leaves, Journal of Applied Spectroscopy, 67(4):723730 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 67(4):524529).Google Scholar
Ben-Dor, E., Inbar, Y. and Chen, Y. (1997), The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process, Remote Sensing of Environment, 61(1):115.Google Scholar
Benedict, H.M. and Swidler, R. (1961), Nondestructive method for estimating chlorophyll content of leaves, Science, 133(3469):20152016.Google Scholar
Benford, F. (1923), Reflection and transmission by parallel plates, Journal of the Optical Society of America, 7(11):10171025.Google Scholar
Benford, F. (1946), Radiation in diffuse medium, Journal of the Optical Society of America, 36(9):524554.Google Scholar
Benoist, D., Tourbier, Y. and Germain-Tourbier, S. (1994), Plans d’expériences : construction et analyse, Lavoisier, Paris, 693 pages.Google Scholar
Berdnik, V.V. and Loiko, V.A. (1999), Modeling of radiative transfer in disperse layers of a medium with a highly stretched phase function, Journal of Quantitative Spectroscopy & Radiative Transfer, 61(1):4957.Google Scholar
Berdnik, V.V. and Mukhamed’yarov, R.D. (2001), Radiation transfer in plant leaves, Optics and Spectroscopy, 90(4):580591 (cover-to-cover translation from Optika i Spektroskopiya, 90(4):652663).CrossRefGoogle Scholar
Berg, V.S. (1985), Physical, chemical and spectral properties of leaf surfaces of Berberis aquifolium (Pursh.), Plant, Cell & Environment, 8(9):631638.Google Scholar
Berghuijs, H.N.C., Yin, X.Y., Ho, Q.T., Driever, S.M., Retta, M.A., Nicolai, B.M. et al. (2016), Mesophyll conductance and reaction-diffusion for CO2 transport in C3 leaves; needs, opportunities and challenges, Plant Science, 252:6275.Google Scholar
Berghuijs, H.N.C., Yin, X.Y., Ho, Q.T., Retta, M.A., Verboven, P., Nicolaï, B.M., et al. (2017), Localization of (photo)respiration and CO2 re-assimilation in tomato leaves investigated with a reaction-diffusion model, PLOS One, 12(9):e0183746.Google Scholar
Bernays, E.A. and Chapman, R.F. (1994), Host-Plant Selection by Phytophageous Insects, Springer, 312 pages.Google Scholar
Berthier, S. (2006), Iridescences: the Physical Colors of Insects, Springer, 160 pages.Google Scholar
Bertoluzza, A., Bottura, G., Lucchi, P., Marchetti, L. and Zechini D’Aulerio, A. (1999), Molecular monitoring of horse chestnut leaves affected with biotic and abiotic disorders, Journal of Plant Pathology, 81(2):8994.Google Scholar
Berzelius, J. (1837a), Ueber die gelbe Farbe der Blätter im Herbste, Annalen der Pharmacie, 21(3):257262.Google Scholar
Berzelius, J. (1837b), Einige Untersuchungen über die Farbe, welche das Laub verschiedener Baumgattungen im Herbste vor dem Abfallen annimmt, Annalen der Physik, 118(11):422433.Google Scholar
Berzelius, J. (1837c), Ueber den rothen Farbstoff der Beeren und Blätter im Herbst, Annalen der Pharmacie, 21(3):262267.Google Scholar
Beysens, D. (1995), The formation of dew, Atmospheric Research, 39(1–3):215237.Google Scholar
Bhushan, B. and Jung, Y.C. (2006), Micro- and nanoscale characterization of hydrophobic and hydrophilic leaf surfaces, Nanotechnology, 17(11):27582772.Google Scholar
Bianchi, G. (1995), Plant waxes, in Waxes: Chemistry, Molecular Biology and Functions (Hamilton, R.D., Ed), The Oily Press, pp. 177222.Google Scholar
Bilger, W., Björkman, O. and Thayer, S.S. (1989), Light-induced spectral absorbance changes in relation to photosynthesis and the epoxidation state of xanthophyll cycle components in cotton leaves, Plant Physiology, 91(2):542551.Google Scholar
Biliouris, D., Verstraeten, W.W., Dutré, P., van Aardt, J.A.N., Muys, B. and Coppin, P. (2007), A compact laboratory spectro-goniometer (CLabSpeG) to assess the BRDF of materials. Presentation, calibration and implementation on Fagus sylvatica L. leaves, Sensors, 7(9):18461870.Google Scholar
Biliouris, D., van der Zande, D., Verstraeten, W.W., et al. (2009), RPV model parameters based on hyperspectral bidirectional reflectance measurements of Fagus sylvatica L. leaves, Remote Sensing, 1(2):92106.Google Scholar
Billings, W.D. and Morris, R.J. (1951), Reflection of visible and infrared radiation from leaves of different ecological groups, American Journal of Botany, 38(5):327331.Google Scholar
Binsfeld, R., Gamboa, J. and Walter, M. (2011), Visual patterns in the plant kingdom, in Proc. 24th Graphics, Patterns and Images (Lewiner T. and Torres R., Eds), Maceio, Brazil, 28–31 August 2011, IEEE, pp. 8692.Google Scholar
Birch, H. (2009), The artificial leaf, Chemistry World, May 2009, pp. 4245.Google Scholar
Birkebak, R. and Birkebak, R. (1964), Solar radiation characteristics of tree leaves, Ecology, 45(3):646649.Google Scholar
Birth, G.S. and Hecht, H.G. (1987), The physics of near-infrared reflectance, in Near-Infrared Technology in the Agricultural and Food Industries (Norris, P.W.K., Ed.), American Association of Cereal Chemists, St Paul, MN, pp. 115.Google Scholar
Bisba, A., Petropoulou, Y. and Manetas, Y. (1997), The transiently pubescent young leaves of plane (Platanus orientalis) are deficient in photodissipative capacity, Physiologia Plantarum, 101(2):373378.Google Scholar
Biswal, B. (1995), Carotenoid catabolism during leaf senescence and its control by light, Journal of Photochemistry and Photobiology B: Biology, 30(1):313.Google Scholar
Björn, L.O. (1986), Reply to comment on “Measurement of light gradients and spectral regime in plant tissue with fibre optic probe”, Physiologia Plantarum, 67(3):494497.Google Scholar
Björn, L.O. and Vogelmann, T.C. (1996), Quantifying light and ultraviolet radiation in plant biology, Photochemistry and Photobiology, 64(3):403406.Google Scholar
Björn, L.O. and Li, S. (2011), Near-surface silica does not increase radiative heat dissipation from plant leaves, Applied Physics Letters, 99(2):024104.Google Scholar
Blackburn, G.A. (1998a), Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches, Remote Sensing of Environment, 66(3):273285.Google Scholar
Blackburn, G.A. (1998b), Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves, International Journal of Remote Sensing, 19(4):657675.Google Scholar
Blackburn, G.A. (1999), Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves, Remote Sensing of Environment, 70(2):224237.Google Scholar
Blackburn, G.A. (2004), Wavelet decomposition of hyperspectral reflectance data for quantifying photosynthetic pigment concentrations in vegetation, in Proc. XXth ISPRS Congress: Geo-Imagery Bridging Continents (Altan O., Ed), Istanbul, Turkey, 12–23 July 2004, ISPRS, pp. 878882.Google Scholar
Blackburn, G.A. (2007a), Hyperspectral remote sensing of plant pigments, Journal of Experimental Botany, 58(4):855867.Google Scholar
Blackburn, G.A. (2007b), Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation, International Journal of Remote Sensing, 28(12):28312855.Google Scholar
Blackburn, G.A. and Ferwerda, J.G. (2008), Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis, Remote Sensing of Environment, 112(4):16141632.Google Scholar
Blackmer, T.M., Schepers, J.S. and Vigil, M.F. (1993), Chlorophyll meter readings in corn as affected by plant spacing, Communications in Soil Science and Plant Analysis, 24(17–18):25072516.Google Scholar
Blackmer, T.M., Shepers, J.S. and Varvel, G.A. (1994), Light reflectance compared with other nitrogen stress measurements in corn leaves, Agronomy Journal, 86(6):934938.Google Scholar
Blackmer, T.M. and Shepers, J.S. (1995), Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn, Journal of Production Agriculture, 8(1):5660.Google Scholar
Blanc, P. (2002), Etre plante à l’ombre des forêts tropicales, Nathan, 432 pages.Google Scholar
Blanchfield, A.L., Robinson, S.A., Renzullo, L.J. and Powell, K.S. (2006), Phylloxera-infested grapevines have reduced chlorophyll and increased photoprotective pigment content-can leaf pigment composition aid pest detection? Functional Plant Biology, 33(5):507514.Google Scholar
Blank, F. (1947), The anthocyanin pigments of plants, Botanical Review, 13(5):241317.Google Scholar
Blazquez, C.H. and Edwards, G.J. (1986), Spectral reflectance of healthy and diseased watermelon leaves, Annals of Applied Biology, 108(2):243249.Google Scholar
Blinn, J.F. (1977), Models of light reflection for computer synthesized pictures, ACM SIGGRAPH Computer Graphics, 11(2):192198.Google Scholar
Blonder, B., De Carlo, F., Moore, J., Rivers, M. and Enquist, B.J. (2012), X-ray imaging of leaf venation networks, New Phytologist, 196(4):12741282.Google Scholar
Boeckx, T., Winters, A.L., Webb, K.J. and Kingston-Smith, A.H. (2015), Polyphenol oxidase in leaves: is there any significance to the chloroplastic localization? Journal of Experimental Botany, 66(12):35713579.Google Scholar
Bojinski, S., Schaepman, M., Schläpfer, D., Itten, K. (2003), SPECCHIO: a spectrum database for remote sensing applications, Computers & Geosciences, 29(1):2738.Google Scholar
Bolster, K.L., Martin, M.E. and Aber, J.D. (1996), Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance: a comparison of statistical methods, Canadian Journal of Forest Research, 26(4):590600.Google Scholar
Bone, R.A., Lee, D.W. and Norman, J.M. (1985), Epidermal cells functioning as lenses in leaves of tropical rain forest shade plants, Applied Optics, 24(10):14081414.Google Scholar
Bonfil, D.J., Karnieli, A., Raz, M., et al. (2005), Rapid assessing of water and nitrogen status in wheat flag leaves, Journal of Food, Agriculture & Environment, 3(2):148153.Google Scholar
Bonham, J.S. (1986), Fluorescence and Kubelka-Munk theory, Color Research and Applications, 11(3):223230.Google Scholar
Bonham-Carter, G. F. (1988), Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data, Computers & Geosciences, 14(3):339356.Google Scholar
Boochs, F., Kupfer, G., Dokter, K. and Kühbauch, W. (1990), Shape of the red edge as vitality indicator for plants, International Journal of Remote Sensing, 11(10):17411753.Google Scholar
Borel, C.C. and McIntosh, R.E. (1988), Leaf backscattering measurements and modelling at 94 GHz, in Proc. 8th International Geoscience and Remote Sensing Symposium (IGARSS’88), Edinburgh, Scotland, 13–16 September 1988, ESA, Vol. SP-284, pp. 12771278.Google Scholar
Born, N., Behringer, D., Liepelt, S., et al. (2014a), Monitoring plant drought stress response using terahertz time-domain spectroscopy, Plant Physiology, 164(4):15711577.Google Scholar
Born, N., Gente, R., Behringer, D., et al. (2014b), Monitoring the water status of plants using THz radiation, in Proc. 39th International Conference on Infrared, Millimeter, and THz Waves, Tucson, AZ, 14–19 September 2014. 2 pages.Google Scholar
Bornman, J.F. and Vogelmann, T.C. (1988), Penetration of blue and UV radiation measured by fiber optics in spruce and fir needles, Physiologia Plantarum, 72(4):699705.Google Scholar
Bornman, J.F. and Vogelmann, T.C. (1991), Effect of UV-B radiation on leaf optical properties measured with fibre optics, Journal of Experimental Botany, 42(237):547554.Google Scholar
Bornman, J.F., Vogelmann, T.C. and Martin, G. (1991), Measurement of chlorophyll fluorescence within leaves using a fibreoptic microprobe, Plant, Cell & Environment, 14(7):719725.Google Scholar
Bortolot, Z.J. and Wynne, R.H. (2003), A method for predicting fresh green leaf nitrogen concentrations from shortwave infrared reflectance spectra acquired at the canopy level that requires no in situ nitrogen data, International Journal of Remote Sensing, 24(3):619624.Google Scholar
Bostock, J. and Riley, H.T. (1855), The Natural History of Pliny, H.G. Bohn, London, 543 pages.Google Scholar
Botha, E., Zebarth, B. and Leblon, B. (2006), Non-destructive estimation of potato leaf chlorophyll and protein contents from hyperspectral measurements using the PROSPECT radiative transfer model, Canadian Journal of Plant Science, 86(1):279291.Google Scholar
Boulet, P., Parent, G., Collin, A., et al. (2009), Spectral emission of flames from laboratory-scale vegetation fires, International Journal of Wildland Fire, 18(7):875884.Google Scholar
Boulet, P., Parent, G., Acem, Z., Collin, A. and Séro-Guillaume, O. (2011), On the emission of radiation by flames and corresponding absorption by vegetation in forest fires, Fire Safety Journal, 46(1–2):2126.Google Scholar
Bousquet, L., Lachérade, S., Jacquemoud, S. and Moya, I. (2005), Leaf BRDF measurement and model for specular and diffuse component differentiation, Remote Sensing of Environment, 98(2–3):201211.Google Scholar
Bousquet, L. (2007), Mesure et modélisation des propriétés optiques spectrales et directionnelles des feuilles, PhD Thesis, Departement of Physics, Paris Diderot University, Paris, 203 pages.Google Scholar
Bowmaker, J.K. and Dartnall, H.J. (1980), Visual pigments of rods and cones in a human retina, The Journal of Physiology, 298(1):501511.Google Scholar
Bowman, W.D. (1989), The relationships between leaf water status, gas exchange, and spectral reflectance in cotton leaves, Remote Sensing of Environment, 30(3):249255.Google Scholar
Bowyer, P. and Danson, F.M. (2004), Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level, Remote Sensing of Environment, 92(3):297308.Google Scholar
Boye, C.L. and Rife, D.C. (1938), Genetic studies of Coleus. I: Leaf color I, Journal of Heredity, 29(2):5560.Google Scholar
Boyer, M., Miller, J.R., Belanger, M., Hare, E. and Wu, J. (1988), Senescence and spectral reflectance in leaves in northern pin oak (Quercus palustris Muenchh.), Remote Sensing of Environment, 25(1):7187.Google Scholar
Boyle, R. (1664), Experiments and Considerations Touching Colours, H. Herringman, London, 423 pages.Google Scholar
Braitmaier, M., Diepstraten, J. and Ertl, T. (2004), Real-time rendering of seasonal influenced trees, in Proc. Theory and Practice of Computer Graphics, Bournemouth, UK, 10 June 2004, IEEE, pp. 152159.Google Scholar
Brakke, T.W. and Smith, J.A. (1987), A ray tracing model for leaf bidirectional scattering studies, in Proc. 7th International Geoscience and Remote Sensing Symposium (IGARSS’87), Ann Arbor, MI, 18–21 May 1987, IEEE, pp. 643648.Google Scholar
Brakke, T.W., Smith, J.A. and Harnden, J.M. (1989), Bidirectional scattering of light from tree leaves, Remote Sensing of Environment, 29(2):175183.Google Scholar
Brakke, T.W. (1992), Goniometric measurements of light scattered in the principal plane from leaves, in Proc. 12th International Geoscience and Remote Sensing Symposium (IGARSS’92), Houston, TX, 26–29 May 1992, IEEE, pp. 508510.Google Scholar
Brakke, T.W. (1994), Specular and diffuse components of radiation scattered by leaves, Agricultural and Forest Meteorology, 71(3–4):283295.Google Scholar
Brakke, T.W., Wergin, W.P., Erbe, E.F. and Harnden, J.M. (1993), Seasonal variation in the structure and red reflectance of leaves from yellow poplar red oak, and red maple, Remote Sensing of Environment, 43(2):115130.Google Scholar
Brandt, A.B. and Tageyeva, S.V. (1967), Optical Parameters of Plant Organisms, Nauka Publishers, Moskva (in Russian).Google Scholar
Breece, H.T. and Holmes, R.A. (1971), Bidirectional scattering characteristics of healthy green soybeans and corn leaves in vivo, Applied Optics, 10(1):119127.Google Scholar
Breitenstein, B., Scheller, M., Shakfa, M.K., Kinder, T., Müller-Wirts, T., Koch, M., et al. (2011), Introducing terahertz technology into plant biology: a novel method to monitor changes in leaf water status, Journal of Applied Botany and Food Quality, 84(2):158161.Google Scholar
Brewer, C.A., Smith, W.K. and Vogelmann, T.C. (1991), Functional interaction between leaf trichomes, leaf wettability and the optical properties of water droplets, Plant, Cell & Environment, 14(9):955962.Google Scholar
Brewster, D.K.H. (1834), On the colours of natural bodies, Transactions of the Royal Society of Edinburgh, 12:538545.Google Scholar
Brewster, D.K.H. (1855), Memoirs of the Life, Writings and Discoveries of Sir Isaac Newton, Thomas Constable and Co., Edinburgh, 564 pages.Google Scholar
Briot, D., Arnold, L., Jacquemoud, S., Schneider, J., Agabi, A., Aristidi, E., et al. B2010), The LUCAS experiment: Spectroscopy of Earthshine in Antarctica for detection of life, Proc. 3rd ARENA Conference: “An Astronomical Observatory at CONCORDIA (Dome C, Antarctica)”, EAS Publications Series, Vol. 40, pp. 361365.Google Scholar
Briot, D. (2013a), The creator of astrobotany, Gavriil Adrianovich Tikhov, in Astrobiology, History, and Society (Vakoch, D.A., Ed), Springer-Verlag, pp. 175185.Google Scholar
Briot, D. (2013b), Elements for the history of a long quest: search for life in the Universe, International Journal of Astrobiology, 12(3):254258.Google Scholar
Briottet, X., Hosgood, B., Meister, G., Sandmeier, S. and Serrot, G. (2004), Laboratory measurement of bi-directional reflectance, in Reflection Properties of Vegetation and Soil – With a BRDF Database (von Schönermark, M., Geiger, B. and Röser, H.P., Eds), Wissenschaft und technik Verlag, Berlin, pp. 173194.Google Scholar
Brito, F.A. and Freire, M.L.F. (2006), A domain wall model for spectral reflectance of plant leaves, Quantitative Biology, 4 pp.Google Scholar
Broadhurst, M.G., Chiang, C.K., Wahlstrand, K.J., Hill, R.M. and Dissado, L.A. (1987), The dielectric properties of biological tissue (Crassula portulacea) from 102 to 109 Hz, Journal of Molecular Liquids, 36:6573.Google Scholar
Brodersen, C.R. and Vogelmann, T.C. (2007), Do epidermal lens cells facilitate the absorptance of diffuse light? American Journal of Botany, 94(7):10611066.Google Scholar
Brodersen, C.R., Vogelmann, T.C., Williams, W.E. and Gorton, H.L. (2008), A new paradigm in leaf-level photosynthesis: direct and diffuse lights are not equal, Plant, Cell & Environment, 31(1):159164.Google Scholar
Brodersen, C.R. and Vogelmann, T.C. (2010), Do changes in light direction affect absorption profiles in leaves? Functional Plant Biology, 37(5):403412.Google Scholar
Brodersen, C.R. and Roddy, A.B. (2016), New frontiers in the three-dimensional visualization of plant structure and function, American Journal of Botany, 103(2):184188.Google Scholar
Brodribb, T.J. and Feild, T.S. (2010), Leaf hydraulic evolution led a surge in leaf photosynthetic capacity during early angiosperm diversification, Ecology Letters, 13(2):175183.Google Scholar
Broglia, M. (1993), Blue-green laser-induced fluorescence from intact leaves: actinic light sensitivity and subcellular origins, Applied Optics, 32(3):334338.Google Scholar
Brown, F.B.H. (1920), The refraction of light in plant tissues, Bulletin of the Torrey Botanic Club, 47(6):243260.Google Scholar
Brown, H.T. (1905), The reception and utilization of energy by a green leaf, Scientific American, 60:2467824679.Google Scholar
Brown, H.T. and Escombe, F. (1905), Researches on some of the physiological processes of green leaves, with special reference to the interchange of energy between the leaf and its surroundings, Proceedings of the Royal Society of London. Series B, 76(507):29111.Google Scholar
Brown, J.M., Thomas, J.F., Cofer, G.P. and Johnson, G.A. (1988), Magnetic resonance microscopy of stem tissue of Pelargonium hortorum, Botanical Gazette, 149(3):253259.Google Scholar
Brümmer, F., Pfannkuchen, M., Baltz, A., Hauser, T. and Thiel, V. (2008), Light inside sponges, Journal of Experimental Marine Biology and Ecology, 367(2):6164.Google Scholar
Brunner, U. and Eller, M. (1977), Spectral properties of juvenile and adult leaves of Piper beetle and their ecological significance, Physiologia Plantarum, 41(2):2224.Google Scholar
Buddenbaum, H., Pueschel, P., Stellmes, M., Werner, W. and Hill, J. (2011), Measuring water and chlorophyll content on the leaf and canopy scale, EARSeL eProceedings, 10(1):6672.Google Scholar
Buddenbaum, H. and Hill, J. (2015), PROSPECT inversions of leaf laboratory imaging spectroscopy – A comparison of spectral range and inversion technique influences PROSPECT, Photogrammetrie – Fernerkundung – Geoinformation, 2015(3):231240.Google Scholar
Buriol, G.A., Menoux, Y. and de Parcevaux, S. (1984a), Détermination de la masse d’eau et des propriétés optiques d’une feuille à partir de modifications de son bilan énergétique. II: Applications en conditions artificielles et naturelles, Agronomie, 4(6):501506.Google Scholar
Buriol, G.A., Santibanez, F., Menoux, Y., de Parcevaux, S. and Bertolini, J.M. (1984b), Détermination de la masse d’eau et des propriétés optiques d’une feuille à partir de modifications de son bilan énergétique. I: Bases théoriques de la méthode et technique de mesure, Agronomie, 4(6):493500.Google Scholar
Burkholder, A., Warner, T.A., Culp, M. and Landenberger, R. (2011), Seasonal trends in separability of leaf reflectance spectra for Ailanthus altissima and four other tree species, Photogrammetric Engineering & Remote Sensing, 77(8):793804.Google Scholar
Burkinshaw, S.M., Hallas, G. and Towns, A.D. (1996), Infrared camouflage, Coloration Technology, 26(1):4753.Google Scholar
Burns, D.A. and Ciurczak, E.W. (2007), Handbook of Near-infrared Analysis, 3rd Edition, CRC Press, 834 pages.Google Scholar
Burns, K.C., Cazetta, E., Galetti, M., Valido, A. and Schaefer, H.M. (2009), Geographic patterns in fruit colour diversity: do leaves constrain the colour of fleshy fruits? Oecologia, 159(2):337343.Google Scholar
Burns, K.C. (2010), Is crypsis a common defensive strategy in plants? Speculation on signal deception in the New Zealand flora, Plant Signaling & Behavior, 5(1):913.Google Scholar
Buscaglia, H.J. and Varco, J.J. (2002), Early detection of cotton leaf nitrogen status using leaf reflectance, Journal of Plant Nutrition, 25(9):20672080.Google Scholar
Buschmann, C. and Lichtenthaler, H.K. (1988), Reflectance and chlorophyll fluorescence signatures of leaves, in Applications of Chlorophyll Fluorescence in Photosynthesis Research, Stress Physiology, Hydrobiology and Remote Sensing (Lichtenthaler, H.K., Ed), Kluwer Academic Publishers, Dordrecht, pp. 325332.Google Scholar
Buschmann, C., Nagel, E., Rang, S. and Stober, F. (1990), Interpretation of reflectance spectra of terrestrial vegetation based on specifical plant test systems, in Proc. 10th International Geoscience and Remote Sensing Symposium, College Park, MD, 20–24 May 1990, IEEE, Vol. 3, pp. 19271930.Google Scholar
Buschmann, C. and Nagel, E. (1993a), In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation, International Journal of Remote Sensing, 14(4):711722.Google Scholar
Buschmann, C. and Nagel, E. (1993b), Variation of reflectance signatures of a leaf as indication of physiological activity, in Proc. 13th International Geoscience and Remote Sensing Symposium (IGARSS’93), Tokyo, Japan, 18–21 August 1993, IEEE, Vol. 2, pp. 522524.Google Scholar
Buschmann, C., Nagel, E., Szabo, K. and Kocsanyi, L. (1994), Spectrometer for fast measurements of in vivo reflectance, absorptance, and fluorescence in the visible and near-infrared, Remote Sensing of Environment, 48(1):1824.Google Scholar
Buschmann, C., Lenk, S. and Lichtenthaler, H.K. (2012), Reflectance spectra and images of green leaves with different tissue structure and chlorophyll content, Israel Journal of Plant Sciences, 60(1–2):4964.Google Scholar
Butler, W.L. (1964), Absorption spectroscopy in vivo: theory and application, Annual Review of Plant Physiology, 15(1):451470.Google Scholar
Calcante, A., Mena, A. and Mazzetto, F. (2012), Evaluation of “ground sensing” optical sensors for diagnosis of Plasmopara viticola on vines, Spanish Journal of Agricultural Research, 10(3):619630.Google Scholar
Caldwell, M.M. (1968), Solar ultraviolet radiation as an ecological factor for alpine plants, Ecological Monographs, 38(3):243268.Google Scholar
Caldwell, M.M., Robberecht, R. and Flint, S.D. (1983), Internal filters: prospects for UV-acclimation in higher plants, Physiologia Plantarum, 58(3):445450.Google Scholar
Calvo-Alvarado, J.C., Kalacska, M., Sánchez-Azofeifa, G.A. and Bell, L.S. (2008), Effect of soil type on plant growth leaf nutrient/chlorophyll concentration, and leaf reflectance of tropical tree and grass species, in Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests (Kalacska, M. and Sanchez-Azofeifa, G.A., Eds), CRC Press, pp. 87123.Google Scholar
Cameron, R.J. (1970), Light intensity and the growth of Eucalyptus seedlings. II: The effect of cuticular waxes on light absorption in leaves of Eucalyptus species, Australian Journal of Botany, 18(3):275284.Google Scholar
Campbell, G.S. (2000), Editorial. Monteith symposium: topics in environmental physics, Agricultural and Forest Meteorology, 104(1):14.Google Scholar
Campbell, P.K.E., Middleton, E.M., McMurtrey, J.E., Corp, L.A. and Chappelle, E.W. (2007), Assessment of vegetation stress using reflectance or fluorescence measurements, Journal of Environmental Quality, 36(3):832845.Google Scholar
Cannon, H.L. (1971), The use of plant indicators in ground water surveys, geologic mapping, and mineral prospecting, Taxon, 20(2–3):227256.Google Scholar
Cao, K.F. (2000), Leaf anatomy and chlorophyll content of 12 woody species in contrasting light conditions in a Bornean heath forest, Canadian Journal of Botany, 78(10):12451253.Google Scholar
Card, D.H., Peterson, D.L., Matson, P.A. and Aber, J.D. (1988), Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy, Remote Sensing of Environment, 26(2):123147.Google Scholar
Cardenas, R., Gausman, H.W., Allen, W.A. and Schupp, M. (1970), The influence of ammonia-induced cellular discoloration within cotton leaves (Gossypium hirsutum L.) on light reflectance, transmittance and absorptance, Remote Sensing of Environment, 1(3):199202.Google Scholar
Cardenas, R., Gausman, H.W. and Thomas, C.T. (1972), Photographic previsual detection of watermelon mosaic virus in cucumber, Journal of the Rio Grande Valley Horticultural Society, 26:7375.Google Scholar
Carlson, R.E. and Yarger, D.N. (1971a), An evaluation of two methods for obtaining leaf transmissivity from leaf reflectivity measurements, Agronomy Journal, 63(1):7881.Google Scholar
Carlson, R.E., Yarger, D.N. and Shaw, R.H. (1971), Factors affecting the spectral properties of leaves with special emphasis on leaf water status, Agronomy Journal, 63(3):486489.Google Scholar
Carpita, N.C. and McCann, M. (2000), The cell wall, in Biochemistry and Molecular Biology of Plants (Buchanan, B., Gruissem, W. and Jones, R.L., Eds), American Society of Plant Physiologists, pp. 52108.Google Scholar
Carter, D.L. and Myers, V.I. (1963), Light reflectance and chlorophyll and carotene contents of grapefruit leaves as affected by Na2SO4, NaCl and CaCl2, Proceeding of the American Society for Horticultural Science, 82:217221.Google Scholar
Carter, G.A., Paliwal, K., Pathre, U., Green, T.H., Mitchell, R.J. and Gjerstad, D.H. (1989), Effect of competition and leaf age on visible and infrared reflectance in pine foliage, Plant, Cell and Environment, 12(3):309315.Google Scholar
Carter, G.A. (1991), Primary and secondary effects of water-content on the spectral reflectance of leaves, American Journal of Botany, 78(7):916924.Google Scholar
Carter, G.A. (1993), Response of leaf spectral reflectance to plant stress, American Journal of Botany, 80(3):239243.Google Scholar
Carter, G.A. (1994), Ratios of leaf reflectances in narrow wavebands as indicators of plant stress, International Journal of Remote Sensing, 15(3):697703.Google Scholar
Carter, G.A., Mitchell, R.J., Chappelka, A.H. and Brewer, C.H. (1992), Response of leaf spectral reflectance in loblolly pine to increased atmospheric ozone and precipitation acidity, Journal of Experimental Botany, 43(249):577584.Google Scholar
Carter, G.A. and McCain, D.C. (1993), Relationship of leaf spectral reflectance to chloroplast water content determined using NMR microscopy, Remote Sensing of Environment, 46(3):305310.Google Scholar
Carter, G.A., Rebbeck, J. and Percy, K.E. (1995), Leaf optical properties in Liriodendron tulipifera and Pinus strobus as influenced by increased atmospheric ozone and carbon dioxide, Canadian Journal of Forest Research, 25(3):407412.Google Scholar
Carter, G.A., Bahadur, R. and Norby, R.J. (2000), Effects of elevated atmospheric CO2 and temperature on leaf optical properties in Acer saccharum, Environmental and Experimental Botany, 43(3):267273.Google Scholar
Carter, G.A. and Spiering, B.A. (2002), Optical properties of intact leaves for estimating chlorophyll concentration, Journal of Environmental Quality, 31(5):14241432.Google Scholar
Casa, R., Castaldi, F., Pascucci, S. and Pignatti, S. (2015), Chlorophyll estimation in field crops: an assessment of handheld leaf meters and spectral reflectance measurements, Journal of Agricultural Science, 153(5):876890.Google Scholar
Castillo, R., Contreras, D., Freer, J., Ruiz, F. and Valenzuela, S. (2008), Supervised pattern recognition techniques for classification of Eucalyptus species from leaves NIR spectra, Journal of the Chilean Chemical Society, 53(4):17091713.Google Scholar
Castle, E.S. (1933), The refractive indices of whole cells, Journal of General Physiology, 17(1):4147.Google Scholar
Castro-Díez, P., Puyravaud, J.P., and Cornelissen, J.H.C. (2000), Leaf structure and anatomy as related to leaf mass per area variation in seedlings of a wide range of woody plant species and types, Oecologia, 124(4):476486.Google Scholar
Castro-Esau, K.L., Sánchez-Azofeifa, G.A. and Caelli, T. (2004), Discrimination of lianas and trees with leaf-level hyperspectral data, Remote Sensing of Environment, 90(3):353372.Google Scholar
Castro-Esau, K.L., Sánchez-Azofeifa, G.A. and Rivard, B. (2006a), Comparison of spectral indices obtained using multiple spectroradiometers, Remote Sensing of Environment, 103(3):276288.Google Scholar
Castro-Esau, K.L., Sánchez-Azofeifa, G.A., Rivard, B., Wright, S.J. and Quesada, M. (2006b), Variability in leaf optical properties of Mesoamerican trees and the potential for species classification, American Journal of Botany, 93(4):517530.Google Scholar
Castro, K.L. and Sánchez-Azofeifa, G.A. (2008), Changes in spectral properties, chlorophyll content and internal mesophyll structure of senescing Populus balsamifera and Populus tremuloides leaves, Sensors, 8(1):5169.Google Scholar
Cavender-Bares, J., Meireles, J.E., Couture, J.J., Kaproth, M.A., Kingdon, C.C., Singh, A., et al. (2016), Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity, Remote Sensing, 8(3):221.Google Scholar
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S. and Grégoire, J.M. (2001), Detecting vegetation water content using reflectance in the optical domain, Remote Sensing of Environment, 77(1):2233.Google Scholar
Ceccato, P., Gobron, N., Flasse, S., Pinty, B. and Tarantola, S. (2002), Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1: theoretical approach, Remote Sensing of Environment, 82(2−3):188197.Google Scholar
Cen, Y.P. and Bornman, J.F. (1993), The effect of exposure to enhanced UV-B radiation on the penetration of monochromatic and polychromatic UV-B radiation in leaves of Brassica napus, Physiologia Plantarum, 87(3):249255.Google Scholar
Cerovic, Z.G., Samson, G., Morales, F., Tremblay, N. and Moya, I. (1999), Ultraviolet-induced fluorescence for plant monitoring: present state and prospects, Agronomie, 19(7):543578.Google Scholar
Cerovic, Z.G., Ounis, A., Cartelat, A., et al (2002), The use of chlorophyll fluorescence excitation spectra for the non-destructive in situ assessment of UV-absorbing compounds in leaves, Plant, Cell & Environment, 25(12):16631676.Google Scholar
Cerovic, Z.G., Masdoumier, G., Ben Ghozlen, N. and Latouche, G. (2012), A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids, Physiologia Plantarum, 146(3):251260.Google Scholar
Chaerle, L., van Caeneghem, W., Messens, E., Lambers, H., van Montagu, M. and van der Straeten, D. (1999), Presymptomatic visualization of plant-virus interactions by thermography, Nature Biotechnology, 17(8):813816.Google Scholar
Chaerle, L. and van der Straeten, D. (2000), Imaging techniques and the early detection of plant stress, Trends in Plant Science, 5(11):495501.Google Scholar
Chaerle, L., De Boever, F., Van Montagu, M. and Van Der Straeten, D. (2001), Thermographic visualization of cell death in tobacco and Arabidopsis, Plant, Cell & Environment, 24(1):1525.Google Scholar
Chaerle, L., vande Ven, M., Valcke, R. and Van Der Straeten, D. (2002), Visualization of early stress responses in plant leaves, in Proc. Thermosense XXIV (Maldague X.P. and Rozlosnik A.E., Eds), Orlando, FL, 1–5 April 2002, SPIE, Vol. 4710, pp. 417423.Google Scholar
Chaerle, L., Hagenbeek, D., De Bruyne, E., Valcke, R. and Van Der Straeten, D. (2004), Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage, Plant & Cell Physiology, 45(7):887896.Google Scholar
Chan, S.S. and Middleton, E.M. (2000), BOREAS TE-10 Leaf Optical Properties, NASA Goddard Space Flight Center, Greenbelt, MD, October 2000, NASA/TM-2000–209891/vol 162, 28 pages.Google Scholar
Chandrasekhar, S. (1960), Radiative Transfer, Dover, New York, 416 pages.Google Scholar
Chang, S.H. and Collins, W. (1983), Confirmation of the airborne biogeophysical mineral exploration technique using laboratory methods, Economic Geology, 78(4):723736.Google Scholar
Chappelle, E.W., Kim, M.S. and McMurtrey, J.E. (1992), Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves, Remote Sensing of Environment, 39(3):239247.Google Scholar
Charney, E. and Brackett, F.S. (1961), The spectral dependence of scattering from a spherical alga and its implication for the state of organization of the light-accepting pigments, Archives of Biochemistry and Biophysics, 92(1):112.Google Scholar
Chautard, J. (1872), Recherches sur les raies de la chlorophylle, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 75:18361839.Google Scholar
Chavana-Bryant, C., Malhi, Y., Wu, J., Asner, G.P., Anastasiou, A., Enquist, B.J., et al. (2017), Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements, New Phytologist, 214(3):10491063.Google Scholar
Chelle, M. (2006), Could plant leaves be treated as Lambertian surfaces in dense crop canopies to estimate light absorption? Ecological Modelling, 198(1–2):219228.Google Scholar
Chen, B., Li, S., Wang, K., Zhou, G. and Bai, J. (2012a), Evaluating the severity level of cotton Verticillium using spectral signature analysis, International Journal of Remote Sensing, 33(9):27062724.Google Scholar
Chen, C.T., Chen, S., Hsieh, K.W., Yang, H.C., Hsiao, S. and Yang, I.C. (2007a), Estimation of leaf nitrogen content using artificial neural network with cross-learning scheme and significant wavelengths, Transactions of the ASABE, 50(1):295301.Google Scholar
Chen, L., Huang, J.F., Wang, F.M. and Tang, Y.L. (2007b), Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data, International Journal of Remote Sensing, 28(16):34573478.Google Scholar
Chen, M., Glaz, B., Gilbert, R.A., Daroub, S.H., Barton, F.E. and Wan, Y. (2002), Near-infrared reflectance spectroscopy analysis of phosphorus in sugarcane leaves, Agronomy Journal, 94(6):13241331.Google Scholar
Chen, M. and Weng, F. (2012), Kramers-Kronig analysis of leaf refractive index with the PROSPECT leaf optical property model, Journal of Geophysical Research – Atmospheres, 117:D18106.Google Scholar
Chen, Q., Zhao, J., Huang, X., Zhang, H. and Liu, M. (2006), Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy, Microchemical Journal, 83(1):4247.Google Scholar
Chen, Q., Zhao, J., Liu, M., Cai, J. and Liu, J. (2008), Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms, Journal of Pharmaceutical and Biomedical Analysis, 46(3):568573.Google Scholar
Chen, X., Han, W. and Li, M. (2012b), Spectroscopic determination of leaf water content using linear regression and an artificial neural network, African Journal of Biotechnology, 11(10):25182527.Google Scholar
Cheng, Q., Tang, D. and Wu, X. (2008), Reflectance properties and physiological responses of Bulrush in Wetland Park to heavy metal contamination in Proc. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII (Michel U., Civco D.L., Ehlers M. and Kaufmann H.J., Eds), Cardiff, UK, 15 September 2008, SPIE, Vol. 71101 R, 9 pages.Google Scholar
Cheng, T., Rivard, B., Sánchez-Azofeifa, G.A., Feng, J. and Calvo-Polanco, M. (2010), Continuous wavelet analysis for the detection of green attack due to mountain pine beetle infestation, Remote Sensing of Environment, 114(4):899910.Google Scholar
Cheng, T., Rivard, B. and Sánchez-Azofeifa, G.A. (2011), Spectroscopic determination of leaf water content using continuous wavelet analysis, Remote Sensing of Environment, 115(2):659670.Google Scholar
Cheng, T., Rivard, B., Sánchez-Azofeifa, G.A., Féret, J.B., Jacquemoud, S. and Ustin, S.L. (2012), Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis, Journal of Plant Physiology, 169(12):11341142.Google Scholar
Cheng, T., Rivard, B., Sánchez-Azofeifa, G.A., Féret, J.B., Jacquemoud, S. and Ustin, S.L. (2014), Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis, ISPRS, Journal of Photogrammetry and Remote Sensing, 87:2838.Google Scholar
Cheynier, V. (2005), Polyphenols in foods are more complex than often thought, American Journal of Clinical Nutrition, 81(1):223S229S.Google Scholar
Chi, G.Y., Chen, X., Shi, Y. and Liu, X.H. (2009a), Spectral response of rice (Oryza sativa L.) leaves to Fe2+ stress, Science in China Series C: Life Sciences, 52(8):747753.Google Scholar
Chi, X.Y., Sheng, B., Yang, M., Chen, Y.Y. and Wu, E.H. (2009b), Simulation of autumn leaves, Journal of Software, 20(3):702712.Google Scholar
Chiba, N., Ohshida, K., Muraoka, K. and Saito, N. (1996), Visual simulation of leaf arrangement and autumn colours, Journal of Visualization and Computer Animation, 7(2):7993.Google Scholar
Cho, M.A. and Skidmore, A.K. (2006), A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method, Remote Sensing of Environment, 101(2):181193.Google Scholar
Christie, C., Mann, P. and Cloutis, E.A. (2013), Spectral characteristics of natural and laboratory-induced leaf senescence in four common North American tree species, International Journal of Remote Sensing Applications, 3(2):7585.Google Scholar
Christie, J.M. (2007), Phototropin blue-light receptors, Annual Review of Plant Biology, 58:2145.Google Scholar
Chuah, H.T., Lee, K.Y. and Lau, T.W. (1995), Dielectric constants of rubber and oil palm leaf samples at X-band, IEEE Transactions on Geoscience and Remote Sensing, 33(1):221223.Google Scholar
Chuah, H.T., Kam, S.W. and Chye, Y.H. (1997), Microwave dielectric properties of rubber and oil palm leaf samples: measurement and modelling, International Journal of Remote Sensing, 18(12):26232639.Google Scholar
Chudek, J.A. and Hunter, G. (1997), Magnetic resonance imaging of plants, Progress in Nuclear Magnetic Resonance Spectroscopy, 31(1):4362.Google Scholar
Chukhlantsev, A.A. (2006), Microwave Radiometry of Vegetation Canopies, Springer, 287 pages.Google Scholar
Chung, B.K. (2006), A convenient method for complex permittivity measurement of thin materials at microwave frequencies, Journal of Physics D: Applied Physics, 39(9):19261931.Google Scholar
Chung, B.K. (2007), Dielectric constant measurement for thin material at microwave frequencies, Progress in Electromagnetics Research, 75:239252.Google Scholar
Chwirot, S. and Slevin, J. (1986), Comment on “Measurement of light gradients and spectral regime in plant tissue with a fibre optic probe”, Physiologia Plantarum, 67(3):493494.Google Scholar
Cibula, W.G. and Carter, G.A. (1992), Identification of a far-red reflectance response to ectomycorrhizae in slash pine, International Journal of Remote Sensing, 13(5):925932.Google Scholar
Cipar, J., Cooley, T. and Lockwood, R. (2008), Summer to autumn changes in vegetation spectral indices of deciduous trees, in Proc. Remote Sensing and Modeling of Ecosystems for Sustainability V (Gao W. and Wang H., Eds), San Diego, CA, 13 August 2008, SPIE, Vol. 7083, 708306.Google Scholar
Clark, D.H., Mayland, H.F. and Lamb, R.C. (1987), Mineral analysis of forages with near infrared reflectance spectroscopy, Agronomy Journal, 79(3):485490.Google Scholar
Clark, J.B. and Lister, G.R. (1975), Photosynthetic action spectra of trees. Part II: The relationship of cuticle structure to the visible and ultraviolet spectral properties of needles from four coniferous species, Plant Physiology, 55(2):407413.Google Scholar
Clark, M.L., Roberts, D.A. and Clark, D.B. (2005), Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales, Remote Sensing of Environment, 96(3–4):375398.Google Scholar
Clark, R.N. and Roush, T.L. (1984), Reflectance spectroscopy – Quantitative-analysis techniques for remote sensing applications, Journal of Geophysical Research, 89(B7):63296340.Google Scholar
Clarke, F.J.J. and Larkin, J.A. (1985), Measurement of total reflectance, transmittance and emissivity over the thermal IR spectrum, Infrared Physics, 25(1–2):359367.Google Scholar
Clijsters, H. and Van Assche, F. (1985), Inhibition of photosynthesis by heavy metals, Photosynthesis Research, 7(1):3140.Google Scholar
Close, D.C. and Beadle, C.L. (2003), The ecophysiology of foliar anthocyanin, The Botanical Review, 69(2):149161.Google Scholar
Close, D.C., Davidson, N.J., Shields, C.B. and Wiltshire, R. (2007), Reflectance and phenolics of green and glaucous leaves of Eucalyptus urnigera, Australian Journal of Botany, 55(5):561567.Google Scholar
Clum, H.H. (1926), The effect of transpiration and environmental factors on leaf temperatures. I: Transpiration. American Journal of Botany, 13(3):194216.Google Scholar
Coblentz, W.W. (1913), The diffuse reflecting power of various substances, Bulletin of the Bureau of Standards, 9(2):283325.Google Scholar
Cochran, G.W., Welkie, G.W. and Chidester, J.L. (1960), Direct infra-red spectral analysis of the cucumber mosaic virus Infection process, Nature, 187:10491050.Google Scholar
Cochrane, M.A. (2000), Using vegetation reflectance variability for species level classification of hyperspectral data, International Journal of Remote Sensing, 21(10):20752087.Google Scholar
Cockell, S.S. and Knowland, J. (1999), Ultraviolet radiation screening compounds, Biological Reviews of the Cambridge Philosophical Society, 74(3):311345.Google Scholar
Cohen, W.B. (1991), Temporal versus spatial variation in leaf reflectance under changing water stress conditions, International Journal of Remote Sensing, 12(9):18651876.Google Scholar
Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y. and Tsipris, J. (2005), Estimation of leaf water potential by thermal imagery and spatial analysis, Journal of Experimental Botany, 56(417):18431852.Google Scholar
Cohen, Y., Alchanatis, V., Zusman, Y., et al. (2010), Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENµS satellite, Precision Agriculture, 11(5):520537.Google Scholar
Colombo, R., Meroni, M., Marchesi, A., et al. (2008), Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling, Remote Sensing of Environment, 112(4):18201834.Google Scholar
Colwell, R.N. (1956), Determining the prevalence of certain cereal crop diseases by means of aerial photography, Hilgardia, 26(5):223286.Google Scholar
Comar, A., Baret, F., Viénot, F., Yan, L. and de Solan, B. (2012), Wheat leaf bidirectional reflectance measurements: description and quantification of the volume, specular and hot-spot scattering features, Remote Sensing of Environment, 121:2635.Google Scholar
Comar, A., Baret, F., Obein, G., et al. (2014), ACT: a leaf BRDF model taking into account the azimuthal anisotropy of monocotyledonous leaf surface, Remote Sensing of Environment, 143:112121.Google Scholar
Combal, B., Baret, F., Weiss, M., et al. (2002), Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem, Remote Sensing of Environment, 84(1):115.Google Scholar
Combes, D., Bousquet, L., Jacquemoud, S., Sinoquet, H., Varlet-Grancher, C. and Moya, I. (2007), A new spectrogoniophotometer to measure leaf spectral and directional optical properties, Remote Sensing of Environment, 109(1):107117.Google Scholar
Conejo, E., Frangi, J.P. and de Rosny, G. (2010), Biophotonic in situ sensor for plant leaves, Applied Optics, 49(10):16871697.Google Scholar
Conejo, E., Frangi, J.P. and de Rosny, G. (2015), Neural network implementation for a reversal procedure for water and dry matter estimation on plant leaves using selected LED wavelengths, Applied Optics, 54(17):54535460.Google Scholar
Conel, J.E., Bosch, J.V.D. and Grove, C.I. (1993a), Application of a two-stream radiative transfer model for leaf lignin and cellulose concentrations from spectral reflectance measurements. Part 1, in Proc. 4th Annual JPL Geoscience Workshop: AVIRIS (Green R.O., Ed), Washington, DC, 25–29 October 1993, JPL Publication, Vol. 93–26, pp. 3943.Google Scholar
Conel, J.E., Bosch, J.V.D. and Grove, C.I. (1993b), Application of a two-stream radiative transfer model for leaf lignin and cellulose concentrations from spectral reflectance measurements. Part 2, in Proc. 4th Annual JPL Geoscience Workshop: AVIRIS (Green R.O., Ed), Washington, DC, 25–29 October 1993, JPL Publication, Vol. 93–26, pp. 4551.Google Scholar
Cook, R.L. and Torrance, K.E. (1981), A reflectance model for computer graphics, Computer Graphics, 15(3):307316.Google Scholar
Coops, N.C., Dury, S., Smith, M.L., Martin, M. and Ollinger, S. (2002), Comparison of green leaf eucalypt spectra using spectral decomposition, Australian Journal of Botany, 50(5):567576.Google Scholar
Coops, N.C. and Stone, C. (2005), A comparison of field-based and modelled reflectance spectra from damaged Pinus radiata foliage, Australian Journal of Botany, 53(5):417429.Google Scholar
Cordon, G.B. and Lagorio, M.G. (2007a), Absorption and scattering coefficients: a biophysical-chemistry experiment using reflectance spectroscopy, Journal of Chemical Education, 84(7):11671170.Google Scholar
Cordon, G.B. and Lagorio, M.G. (2007b), Optical properties of the adaxial and abaxial faces of leaves. Chlorophyll fluorescence, absorption and scattering coefficients, Photochemical & Photobiological Sciences, 6(8):873882.Google Scholar
Cornard, J.P. and Merlin, J.C. (2002), Spectroscopic and structural study of complexes of quercetin with Al(III), Journal of Inorganic Biochemistry, 92(1):1927.Google Scholar
Cortazar, B., Koydemir, H.C., Tseng, D., Fenga, S. and Ozcan, A. (2015), Quantification of plant chlorophyll content using Google Glass, Lab on a Chip, 15:17081716.Google Scholar
Coward, J.L. (2010), FTIR spectroscopy of synthesized racemic nonacosan-10-ol: a model compound for plant epicuticular waxes, Journal of Biological Physics, 36(4):405425.Google Scholar
Croft, H., Chen, J.M., Zhang, Y., et al. (2015), Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework, ISPRS Journal of Photogrammetry and Remote Sensing, 102:8595.Google Scholar
Cui, M., Vogelmann, T.C. and Smith, W.K. (1991), Chlorophyll and light gradients in sun and shade leaves of Spinacia oleracea, Plant, Cell & Environment, 14(5):493500.Google Scholar
Cunningham, S.A. and Floyd, R.B. (2004), Leaf compositional differences predict variation in Hypsipyla robusta damage to Toona ciliata in field trials, Canadian Journal of Forest Research, 34(3):642648.Google Scholar
Curran, P.J. (1989), Remote sensing of foliar chemistry, Remote Sensing of Environment, 30(3):271278.Google Scholar
Curran, P.J., Dungan, J.L. and Gholz, H.L. (1990), Exploring the relationship between reflectance red-edge and chlorophyll content in slash pine, Tree Physiology, 7(1–4):3348.Google Scholar
Curran, P.J., Dungan, J.L., Macler, B.A. and Plummer, S.E. (1991), The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration, Remote Sensing of Environment, 35(1):6976.Google Scholar
Curran, P.J., Dungan, J.L., Macler, B.A., Plummer, S.E. and Peterson, D.L. (1992), Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration, Remote Sensing of Environment, 39(2):153166.Google Scholar
Curran, P.J., Windham, W.R. and Gholz, H.L. (1995), Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves, Tree Physiology, 15(3):203206.Google Scholar
Curran, P.J., Dungan, J.L. and Peterson, D.L. (2001), Estimating the biochemical concentration of leaves with reflectance spectrometry. Testing the Kokaly and Clark methodologies, Remote Sensing of Environment, 76(3):349359.Google Scholar
Cutler, D.F., Alvin, K.L. and Price, C.E., Eds (1982), The Plant Cuticle, Academic Press, New York, 461 pages.Google Scholar
Dadykin, V.P. and Bedenko, V.P. (1960), Concerning the geographic variability of optical properties in plant leaves, Doklady Botanical Sciences Sections, 130(3):6–8 (cover-to-cover translation from Doklady Akademii Nauk SSSR, 130(3):674677).Google Scholar
Dahm, D.J. and Dahm, K.D. (1999), Representative layer theory for diffuse reflectance, Applied Spectroscopy, 53(6):647654.Google Scholar
Danson, F.M. and Bowyer, P. (2004), Estimating live fuel moisture content from remotely sensed reflectance, Remote Sensing of Environment, 92(3):309321.Google Scholar
Danson, F.M., Steven, M.D., Malthus, T.J. and Clark, J.A. (1992), High-spectral resolution data for determining leaf water content, International Journal of Remote Sensing, 13(3):461470.Google Scholar
Darwin, C.R. (1862), On the Various Contrivances by which British and Foreign Orchids are Fertilised by Insects, and on the Good Effects of Intercrossing, John Murray, London, 360 pages.Google Scholar
Dash, J. and Curran, P.J. (2004), The MERIS terrestrial chlorophyll index, International Journal of Remote Sensing, 25(33):54035413.Google Scholar
Datt, B. (1998), Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in Eucalyptus leaves, Remote Sensing of Environment, 66(2):111121.Google Scholar
Datt, B. (1999a), A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves, Journal of Plant Physiology, 154(1):3036.Google Scholar
Datt, B. (1999b), Remote sensing of water content in Eucalyptus leaves, Australian Journal of Botany, 47(6):909923.Google Scholar
Datt, B. (1999c), Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves, International Journal of Remote Sensing, 20(14):27412759.Google Scholar
Daughtry, C.S.T., Biehl, L.L. and Ranson, K.J. (1989), A new technique to measure the spectral properties of conifer needles, Remote Sensing of Environment, 27(1):8191.Google Scholar
Daughtry, C.S.T. and Walthall, C.L. (1998), Spectral discrimination of Cannabis sativa L. leaves and canopies, Remote Sensing of Environment, 64(2):192201.Google Scholar
Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de Colstoun, E. and McMurtrey, J.E. (2000), Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance, Remote Sensing of Environment, 74(2):229239.Google Scholar
Dauriac, F. (2004), Suivi multi-échelle par télédétection et spectroscopie de l’état hydrique de la végétation méditerranéenne pour la prévention du risque de feu de forêt, Thèse de doctorat Sciences de l’eau, École Nationale du Génie Rural, des Eaux et des Forêts, Montpellier, France, 288 pages.Google Scholar
Davids, C. and Tyler, A.N. (2003), Detecting contamination-induced tree stress within the Chernobyl exclusion zone, Remote Sensing of Environment, 85(1):3038.Google Scholar
Davis, P.A., Caylor, S., Whippo, C.W. and Hangarter, R.P. (2011), Changes in leaf optical properties associated with light-dependent chloroplast movements, Plant, Cell & Environment, 34(12):20472059.Google Scholar
Dawson, T.P., Curran, P.J. and Plummer, S.E. (1995), Modelling the spectral response of coniferous leaf structures for the estimation of biochemical concentrations, in Proc. 21st Annual Conference of the Remote Sensing Society: Remote Sensing in Action, 11–14 September 1995, University of Southampton, UK, Remote Sensing Society pp. 587594.Google Scholar
Dawson, T.P., Curran, P.J. and Plummer, S.E. (1998a), LIBERTY – Modeling the effects of leaf biochemical concentration on reflectance spectra, Remote Sensing of Environment, 65(1):5060.Google Scholar
Dawson, T.P., Curran, P.J. and Plummer, S.E. (1998b), The biochemical decomposition of slash pine needles from reflectance spectra using neural networks, International Journal of Remote Sensing, 19(7):14331438.Google Scholar
Dawson, T.P. and Curran, P.J. (1998), A new technique for interpolating the reflectance red edge position, International Journal of Remote Sensing, 19(11):21332139.Google Scholar
Dawson, T.P., Curran, P.J., North, P.R.J. and Plummer, S.E. (1999), The propagation of foliar biochemical absorption features in forest canopy reflectance: a theoretical analysis, Remote Sensing of Environment, 67(2):147159.Google Scholar
Day, T.A., Vogelmann, T.C. and De Lucia, E.H. (1992), Are some plant life forms more effective than others in screening out ultraviolet-B radiation? Oecologia, 92(4):513519.Google Scholar
Day, T.A., Howells, B.W. and Rice, W.J. (1994), Ultraviolet absorption and epidermal-transmittance spectra in foliage, Physiologia Plantarum, 92(2):207218.Google Scholar
Deák, S., Márk, G.I., Szabó, T. and Füleky, G. (2007), Spectral properties of strawberry plants, International Journal of Horticultural Science, 13(2):1722.Google Scholar
Dean, K.G., Kodama, Y. and Wendler, G. (1986), Comparison of leaf and canopy reflectance of subarctic forests, Photogrammetric Engineering & Remote Sensing, 52(6):809811.Google Scholar
De Boer, T.S., Baker, A.C., Erdmann, M.V., Ambariyanto, Jones P.R. and Barber, P.H. (2012), Patterns of Symbiodinium distribution in three giant clam species across the biodiverse Bird’s Head region of Indonesia, Marine Ecology Progress Series, 444:117132.Google Scholar
Degl’innocenti, E., Guidi, L., Pardossi, A. and Tognoni, F. (2005), Biochemical study of leaf browning in minimally processed leaves of lettuce (Lactuca sativa L. Var. Acephala), Journal of Agricultural and Food Chemistry, 53(26):99809984.Google Scholar
Delafolie (1774), Examen d’une Terre verte que l’on trouve abondamment aux environs du Pont-Audemer en Normandie, & probablement en d’autres endroits : avec diverses expériences qui paraissent démontrer que les couleurs variées de toutes les plantes ne font que le résultat des précipités ferrugineux, Observations sur la physique, sur l’histoire naturelle et sur les arts, Tome IV, pp. 349359.Google Scholar
Delalieux, S., van Aardt, J., Keulemans, W., Schrevens, E. and Coppin, P. (2007), Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications, European Journal of Agronomy, 27(1):130143.Google Scholar
Delalieux, S., Somers, B., Verstraeten, W.W., van Aardt, J.A.N., Keulemans, W. and Coppin, P. (2009), Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology, International Journal of Remote Sensing, 30(8):18871912.CrossRefGoogle Scholar
Del’Arco Sanches, I., Souza Filho, C.R. and Kokaly, R.F. (2014), Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal, ISPRS Journal of Photogrammetry and Remote Sensing, 97:111122.Google Scholar
Delaval, E.H. (1774), Recherches expérimentales sur la cause des changements de couleur dans les corps opaques naturellement colorés, Nouveaux Mémoires de l’Académie Royale des Sciences et Belles-Lettres de Berlin, 1:154194 (Archiv der Berlin-Brandenburgischen Akademie der Wissenschaften).Google Scholar
Delozier, G., Eckard, K., Greene, M. and Lord, E.M. (1987), A computer graphics program for the three-dimensional reconstruction of plant organs from serial sections, American Journal of Botany, 74(1):136140.Google Scholar
DeLucia, E.H., Shenoi, H.D., Naidu, S.L. and Day, T.A. (1991), Photosynthetic symmetry of sun and shade leaves of different orientations, Oecologia, 87(1):5157.Google Scholar
DeLucia, E.H., Day, T.A. and Vogelman, T.C. (1992), Ultraviolet-B and visible light penetration into needles of two species of subalpine conifers during foliar development, Plant, Cell & Environment, 15(8):921929.Google Scholar
Demarez, V. and Gastellu-Etchegorry, J.-P. (2000), A modeling approach for studying forest chlorophyll content, Remote Sensing of Environment, 71(2):226238.Google Scholar
Demetriades-Shah, T.H., Steven, M.D. and Clark, J.A. (1990), High resolution derivative spectra in remote sensing, Remote Sensing of Environment, 33(1):5564.Google Scholar
Demmig-Adams, B., Winter, K., Krüger, A. and Czygan, F.C. (1987), Photoinhibition and zeaxanthin formation in intact leaves: a possible role of the xanthophyll cycle in the dissipation of excess light energy, Plant Physiology, 84(2):118224.Google Scholar
Demmig-Adams, B. (1990), Carotenoids and photoprotection in plants: a role for the xanthophyll zeaxanthin, Biochimica et Biophysica Acta (BBA)-Bioenergetics, 1020(1):124.Google Scholar
Demmig-Adams, B., Gilmore, A.M. and Adams III, W.W. (1996), Carotenoids 3: in vivo functions of carotenoids in higher plants, The FASEB Journal, 10(4):403412.Google Scholar
Dengler, N. and Kang, J. (2001), Vascular patterning and leaf shape, Current Opinion in Plant Biology, 4(1):5056.Google Scholar
Dennison, P.E. and Roberts, D.A. (2003a), Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE, Remote Sensing of Environment, 87(2–3):123135.Google Scholar
Dennison, P.E. and Roberts, D.A. (2003b), The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral, Remote Sensing of Environment, 87(2–3):295309.Google Scholar
Dennison, P.E., Halligan, K.Q. and Roberts, D.A. (2004), A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper, Remote Sensing of Environment, 93(3):359367.Google Scholar
De Parcevaux, S., Bertolini, J.M. and Katerji, N. (1995), Masse en eau des feuilles: détermination non destructive en mileu naturel, Agronomie, 15(9–10):547556.Google Scholar
Derkacheva, O. and Sukhov, D. (2005), Investigation of lignins by FTIR spectroscopy, Macromolecular Symposia, 265(1):6168.Google Scholar
Deroin, J.P. and Deroin, T. (1996), The relationships among vegetation, geology and spectral reponse: a quantitative approach on the European beech (Fagus sylvatica, Fagaceae), Fragmentu Floristica et Geobotanica, 41(2):791801.Google Scholar
Deroin, J.P. and Deroin, T. (1990), Effet d’une déficience en manganèse des sols sur la réponse spectrale des feuilles de hêtre (Fagus sylvatica L.), Comptes Rendus de l’Académie des Sciences Paris, t. 311, Série II, pp. 605611.Google Scholar
de Rosny, G., Vanderhaghen, R., Baret, F., Equer, B. and Frangi, J.P. (1995), A device for in situ measurements of leaf chlorophyll and carotenoid concentrations, in Proc. International Colloquium on Photosynthesis and Remote Sensing (G. Guyot, Ed), Montpellier, France, 28–30 August 1995, pp. 135141.Google Scholar
de Saussure, N.T. (1804), Recherches chimiques sur la végétation, Nyon, Paris, 327 pages.Google Scholar
Desbenoit, B., Galin, E., Akkouche, S. and Grosjean, J. (2006), Modeling autumn sceneries, in Proc. 26th International Conference on Eurographics (Fellner D.W. and Hansen C., Eds), Vienna, Austria, 4–8 September 2006, The Eurographics Association, pp. 107110.Google Scholar
Deussen, O. and Lintermann, B. (2005), Digital Design of Nature, Springer, 295 pages.Google Scholar
de Vries, J., Rauch, C., Christa, G. and Gould, S.B. (2014a), A sea slug’s guide to plastid symbiosis, Acta Societatis Botanicorum Poloniae, 83(4):415421.Google Scholar
de Vries, J., Christa, G. and Gould, S.B. (2014b), Plastid survival in the cytosol of animal cells, Trends in Plant Science, 19(6):347350.Google Scholar
DeWitt, D.P. and Nutter, G.D. (1988), Radiation Thermometry, John Wiley & Sons, 1138 pages.Google Scholar
Dey, P.M. and Harborne, J.B. (1997), Plant Biochemistry, Academic Press, 554 pages.Google Scholar
Diago, M.P., Fernandes, A.M., Millan, B., Tardaguila, J. and Melo-Pinto, P. (2013), Identification of grapevine varieties using leaf spectroscopy and partial least squares, Computers and Electronics in Agriculture, 99:713.Google Scholar
Diah, S.Z.M., Karman, S.B. and Gebeshuber, I.C. (2014), Nanostructural colouration in Malaysian plants: lessons for biomimetics and biomaterials, Journal of Nanomaterials, 2014:878409.Google Scholar
Dijkstra, P. (1989), Cause and effect of differences in specific leaf area, in Causes and Consequences of Variation in Growth Rate and Productivity of Higher Plants (Lambers, H., Ed), Academic Publishing, The Hague, pp. 125140.Google Scholar
Dillen, S.Y., Op de Beeck, M., Hufkens, K., Buonanduci, M. and Phillips, N.G. (2012), Seasonal patterns of foliar reflectance in relation to photosynthetic capacity and color index in two co-occurring tree species, Quercus rubra and Betula papyrifera, Agricultural and Forest Meteorology, 160:6068.Google Scholar
Di Vittorio, A.V. (2009), Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance, Remote Sensing of Environment, 113(9):19481966.Google Scholar
Vittorio, A.V. and Biging, G.S. (2009), Spectral identification of ozone-damaged pine needles, International Journal of Remote Sensing, 30(12):30413073.Google Scholar
Djurišić, A.B. and Stanić, B.V. (1998), Modeling the wavelength dependence of the index of refraction of water in the range 200 nm to 200 µm, Applied Optics, 37(13):26962698.Google Scholar
Dlugunovich, V.A., Zaitseva, V.A. and Tsaryuk, O.V. (2001a), Changes in the polarization characteristics of the He-Ne laser radiation reflected from plant leaves, Journal of Applied Spectroscopy, 68(1):94100.Google Scholar
Dlugunovich, V.A., Zaitseva, V.A., Sergeichik, S.A. and Tsaryuk, O.V. (2001b), Polarization characteristics of the He–Ne laser radiation reflected from rhododendron leaves subjected to acid treatment, Journal of Applied Spectroscopy, 68(6):10201025.Google Scholar
Dobek, A., Paillotin, G., Gapinski, J., Breton, J., Leibl, W. and Trissl, H.W. (1994), Amplitude and polarity of the light gradient photovoltage from chloroplasts, Journal of Theoretical Biology, 170(2):129143.Google Scholar
Dobrowski, S.Z., Pushnik, J.C., Zarco-Tejeda, P.J. and Ustin, S.L. (2005), Simple reflectance indices track heat and water stressed induced changes in steady state chlorophyll fluorescence at the canopy scale, Remote Sensing of Environment, 97(3):403414.Google Scholar
Doi, H. and Takahashi, M. (2008), Latitudinal patterns in the phenological responses of leaf colouring and leaf fall to climate change in Japan, Global Ecology and Biogeography, 17(4):556561.Google Scholar
Dominy, N.J. and Lucas, P.W. (2001), Ecological importance of trichromatic vision to primates, Nature, 410:363366.Google Scholar
Donner, C. and Jensen, H.W. (2005), Light diffusion in multi-layered translucent materials, ACM Transactions on Graphics, 24(3):10321039.Google Scholar
Döring, T.F., Archetti, M. and Hardie, J. (2009), Autumn leaves seen through herbivore eyes, Proceedings of the Royal Society of London. Series B, Biological Sciences, 276(1654):121127.Google Scholar
Doughty, C.E., Asner, G.P. and Martin, R.E. (2011), Predicting tropical plant physiology from leaf and canopy spectroscopy, Oecologia, 165(2):289299.Google Scholar
Du, L.J. and Peake, W.H. (1969), Rayleigh scattering from leaves, Proceedings of the IEEE, 57(6):12271229.Google Scholar
Dubis, E.N., Dubis, A.T., and Morzycki, J.W. (1999), Comparative analysis of plant cuticular waxes using HATR FT-IR reflection technique, Journal of Molecular Structure, 511–512:173179.Google Scholar
Dubis, E.N., Dubis, A.T, and Poplawski, J. (2001), Determination of the aromatic compounds in plant cuticular waxes using FT-IR spectroscopy, Journal of Molecular Structure, 596(1–3):8388.Google Scholar
Dunagan, S.C., Gilmore, M.S. and Varekamp, J.C. (2007), Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.), Environmental Pollution, 148(1):301311.Google Scholar
Dunn, A. and Richards-Kortum, R. (1996), Three-dimensional computation of light scattering from cells, IEEE Journal of Quantum Electronics, 2(4):898905.Google Scholar
Durgante, F.M., Higuchi, N., Almeida, A. and Vicentini, A. (2013), Species spectral signature: discriminating closely related plant species in the Amazon with near-infrared leaf-spectroscopy, Forest Ecology and Management, 291:240248.Google Scholar
Dutton, H.J., Manning, W.M. and Duggar, B.M. (1943), Chlorophyll fluorescence and energy transfer in the diatom, Nitzschia closterium, Journal of Physical Chemistry, 47(4):308313.Google Scholar
Dwyer, L.M., Tollenaar, M. and Houwing, L. (1991), A nondestructive method to monitor leaf greenness in corn, Canadian Journal of Plant Science, 71(2):505509.Google Scholar
Earl, H.J. and Tollenaar, M. (1997), Maize leaf absorptance of photosynthetically active radiation and its estimation using a chlorophyll meter, Crop Science, 37(2):436440.Google Scholar
Edwards, G.J., Schehl, T. and DuCharme, E.P. (1975), Multispectral sensing of Citrus young tree decline, Photogrammetric Engineering, 41(5):653657.Google Scholar
Egan, W.G. (1970), Optical stokes parameters for farm crop identification, Remote Sensing of Environment, 1(3):165180.Google Scholar
Egbert, K.J. and Martin, C.E. (2000), Light penetration via leaf windows does not increase photosynthesis in three species of desert succulents, Journal of Plant Physiology, 157(5):521525.Google Scholar
Egbert, K.J., Martin, C.E. and Vogelmann, T.C. (2008), The influence of epidermal windows on the light environment within the leaves of six succulents, Journal of Experimental Botany, 59(7):18631873.Google Scholar
Eggli, U. and Newton, L.E. (2004), Etymological Dictionary of Succulent Plant Names, Springer, 266 pages.Google Scholar
Egri, A., Horváth, A., Kriska, G. and Horváth, G. (2010), Optics of sunlit water drops on leaves: conditions under which sunburn is possible, New Phytologist, 185(4):979987.Google Scholar
Ehleringer, J.R. (1976), Leaf absorptance and photosynthesis as affected by pubescence in the genus Encelia, Carnegie Institution of Washington Year Book, 75:413418.Google Scholar
Ehleringer, J., Bjorkman, O. and Mooney, H.A. (1976a), Leaf pubescence: effects on absorptance and photosynthesis in a desert shrub, Science, 192(4237):376377.Google Scholar
Ehleringer, J.R. and Björkman, O. (1978), Pubescence and leaf spectral characteristics in a desert shrub, Encelia farinosa, Oecologia, 36(2):151162.Google Scholar
Ehleringer, J.R. and Mooney, H.A. (1978), Leaf hairs: effects on physiological activity and adaptive value to a desert shrub, Oecologia, 37(2):183200.Google Scholar
Ehleringer, J.R. (1981), Leaf absorptances of Mohave and Sonoran plants, Oecologia, 49(3):366370.Google Scholar
Ehleringer, J.R. (1984), Ecology and ecophysiology of leaf pubescence in North American desert plants, in Biology and Chemistry of Plant Trichomes (Rodriquez, E., Healey, P. and Mehta, I., Eds), Plenum Press, New York, pp. 113132.Google Scholar
Ehleringer, J.R. (1986), Modifications of solar-radiation absorption patterns and implications for carbon gain at the leaf level, in On the Economy of Plant Form and Function (Givnish, T.J., Ed), Cambridge University Press, London, pp. 5781.Google Scholar
Ehleringer, J.R. (1988), Changes in leaf characteristics of species along elevational gradients in the Wasatch Front, Utah, American Journal of Botany, 75(5):680689.Google Scholar
Ehleringer, J.R. and Cook, C.S. (1990), Characteristics of Encelia species differing in leaf reflectance and transpiration rate under common garden conditions, Oecologia, 82(4):484489.Google Scholar
Elias, H. (1971), Three-dimensional structure identified from single sections, Science, 174(4013):9931000.Google Scholar
Elias, M., and Lafait, J. (2006), La couleur. Lumière, vision et matériaux, Belin, 352 pages.Google Scholar
Eller, B.M. and Brunner, U. (1975), Der Einfluß von Straßenstaub auf die Strahlungsabsorption durch Blätter, Archiv für Meteorologie, Geophysik und Bioklimatologie. Serie B: Klimatologie, Umweltmeteorologie, Strahlungsforschung, 23(1–2):137146 (in German: Road-dust changed absorption of solar radiation by plant leaves).Google Scholar
Eller, B.M. (1977a), Leaf pubescence: the significance of lower surface hairs for the spectral properties of the upper surface, Journal of Experimental Botany, 28(105):10541059.Google Scholar
Eller, B.M. (1977b), Road dust induced increase of leaf temperature, Environmental Pollution, 13(2):99107.Google Scholar
Eller, B.M. (1977c), Beeinflussung der Energiebilanz von Blättern durch Straßenstaub, Angewandte Botanik, 51(1–2):915 (in German: Road dust changed energy balance of leaves).Google Scholar
Eller, B.M. and Willi, P. (1977a), Globalstrahlungsabsorption von Hedera helix L. unter Straßenstaubimmissionen, Gartenbauwissenschaft, 42(2):4953 (in German: Road dust emissions on Hedera helix L. and changed absorption of global radiation).Google Scholar
Eller, B.M. and Willi, P. (1977b), Die Bedeutung der Wachsausblühungen auf Blaättern von Kalanchoe pumila Baker für die Absorption der Globalstrahlung, Flora, 166(5):461474 (in German: Significance of wax bloom on leaves of Kalanchoe pumila Baker for absorption of global radiation).Google Scholar
Eller, B.M. and Willi, P. (1977c), The significance of leaf pubescence for the absorption of global radiation by Tussilago farfara L., Oecologia, 29(2):179187.Google Scholar
Eller, B.M., Brinckmann, E. and von Willert, D.J. (1983), Optical properties and succulence of plants in the arid Richtersveld (Cp., Rep. South Africa), Botanica Helvetica, 93(1):4755.Google Scholar
El-Rayes, M.A. and Ulaby, F.T. (1987), Microwave dielectric spectrum of vegetation-Part I: experimental observations, IEEE Transactions on Geoscience and Remote Sensing, 25(5):541549.Google Scholar
Elser, J.J., Fagan, W.F., Kerkhoff, A.J., Swenson, N.G. and Enquist, B. J. (2010), Biological stoichiometry of plant production: metabolism, scaling and ecological response to global change, New Phytologist, 186(3):593608.Google Scholar
Elvidge, C.D. (1988), Thermal infrared reflectance of dry plant materials: 2.5–20.0 µm, Remote Sensing of Environment, 26(3):265285.Google Scholar
Elvidge, C.D. (1990), Visible and near infrared reflectance characteristics of dry plant materials, International Journal of Remote Sensing, 11(10):17751795.Google Scholar
Emberson, L. (2003), Air pollution impacts on crops and forests: an introduction, in Air Pollution Impacts on Crops and Forests: a Global Assessment (Emberson, L., Ashmore, M.R. and Murray, F., Eds), World Scientific Publishing Company, pp. 334.Google Scholar
Emengini, E.J., Blackburn, G.A. and Theobald, J.C. (2013), Early detection of oil-induced stress in crops using spectral and thermal responses, Journal of Applied Remote Sensing, 7(1):073596.Google Scholar
Emengini, E.J. and Ugbelase, V.N. (2013), Mapping the effects of hydrocarbon spillage on plant spectral properties, International Journal of Environmental Science, Management and Engineering Research, 2(1):3038.Google Scholar
Emmel, P., and Hersch, (1998), Spectral colour prediction model for a transparent fluorescent ink on paper, in Proc. 6th Color Imaging Conference: Color Science, Systems and Applications, 17–20 November 1998, Scottsdale, AZ, pp. 116122.Google Scholar
Emmel, P. (2000), Nouvelle formulation du modèle de Kubelka et Munk avec applications aux encres fluorescentes, in Actes de l’Ecole de Printemps 2000 – Le Pays d’Apt en Couleurs, 14–18 mars 2000, Roussillon en Provence, France, pp. 8796.Google Scholar
Emmel, P. (2003), Physical models for color prediction, in Digital Color Imaging Handbook (Sharma, G., Ed), CRC Press, 66 pages.Google Scholar
Engemann, K., Sandel, B., Morueta-Holme, N., Enquist, B.J., Peet, R.K., Wiser, S. et al. (2016), Patterns and drivers of plant functional group dominance across the Western Hemisphere – A macroecological re-assessment based on a massive botanical dataset, Botanical Journal of the Linnaean Society, 180(2):141–60.Google Scholar
Essery, C.I. and Morse, A.P. (1992), The impact of ozone and acid mist on the spectral reflectance of young Norway spruce trees, International Journal of Remote Sensing, 13(16):30453054.Google Scholar
Esteban, R., Fernández-Marín, B., Olano, J.M., Becerril, J.M. and García-Plazaola, J.I. (2014), Does plant colour matter? Wax accumulation as an indicator of decline in Juniperus thurifera, Tree Physiology, 34(3):267274.Google Scholar
Estrella, N. and Menzel, A. (2006), Responses of leaf colouring in four deciduous tree species to climate and weather in Germany, Climate Research, 32(3):253267.Google Scholar
Evans, J.R. (1989), Photosynthesis and nitrogen relationships in leaves of C3 plants, Oecologia, 78(1):919.Google Scholar
Evans, J.R. (1999), Leaf anatomy enables more equal access to light and CO2 between chloroplasts, New Phytologist, 143(1):93104.Google Scholar
Evans, J.R., Jakobsen, I., Ogren, E. (1993), Photosynthetic light-response curves. 2: gradients of light absorption and photosynthetic capacity, Planta, 189(2):191200.Google Scholar
Evans, J.R. and Vogelmann, T.C. (2003), Profiles of 14C fixation through spinach leaves in relation to light absorption and photosynthetic capacity, Plant, Cell & Environment, 26(4):547560.Google Scholar
Evans, J.R. and Vogelmann, T.C. (2006), Photosynthesis within isobilateral Eucalyptus pauciflora leaves, New Phytologist, 171(4):771782.Google Scholar
Evans, J.R., Vogelmann, T.C., Williams, W.E. and Gorton, H.L. (2004), Chloroplast to leaf, in Photosynthetic Adaptation: Chloroplast to Landscape (Smith, W.K., Vogelmann, T.C. and Critchley, C., Eds), Ecological Studies 178, Springer, pp. 1541.Google Scholar
Fabre, S., Lesaignoux, A., Olioso, A. and Briottet, X. (2011), Influence of water content on spectral reflectance of leaves in the 3–15 µm domain, IEEE Geoscience and Remote Sensing Letters, 8(1):143147.Google Scholar
Fadzly, N., Jack, C., Schaefer, H.M. and Burns, K.C. (2009), Ontogenetic colour changes in an insular tree species: signalling to extinct browsing birds? New Phytologist, 184(2):495501.Google Scholar
Fan, D.Y., Wang, Q., Li, M. and Gao, R.F. (2002), The lens effect of the epidermic cell layer of the leaf of Euonymus japonicus T. on the light gradients within leaf, Acta Phytoecologica Sinica, 26(5):594598.Google Scholar
Fan, D.Y., Han, T., Li, J., Wang, Q. and Gao, R.F. (2003), Profile of absolute light utilization efficiency within leaves of Euonymus japonicus, Acta Botanica Sinica, 45(2):169176.Google Scholar
Fan, Q., Wang, Y., Sun, P., Liu, S. and Li, Y. (2010), Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis, Talanta, 80(3):12451250.Google Scholar
Fang, M.H. and Ju, W.M. (2015), A inversion model for remote sensing of leaf water content based on the leaf optical property, Spectroscopy and Spectral Analysis, 35 (1):167171 (in Chinese).Google Scholar
Fariñas, M.D., Sancho-Knapik, D., Peguero-Pina, J.J., Gil-Pelegrín, E. and Gomez Álvarez-Arenas, T.E. (2013), Shear waves in vegetal tissues at ultrasonic frequencies, Applied Physics Letters, 102(10):103702.Google Scholar
Fariñas, M.D., Sancho-Knapik, D., Peguero-Pina, J.J., Gil-Pelegrín, E. and Gomez Álvarez-Arenas, T.E. (2014a), Monitoring plant response to environmental stimuli by ultrasonic sensing of the leaves, Ultrasound in Medicine & Biology, 40(9):21832194.Google Scholar
Fariñas, M.D. and Gomez Álvarez-Arenas, T.E. (2014b), Ultrasonic assessment of the elastic functional design of component tissues of Phormium tenax leaves, Journal of the Mechanical Behavior of Biomedical Materials, 39:304315.Google Scholar
Farmer, A.M. (1993), The effects of dust on vegetation – A review, Environmental Pollution, 79(1): 6375.Google Scholar
Farquhar, G.D. and Roderick, M.L. (2003), Pinatubo, diffuse light, and the carbon cycle, Science, 299(5615):19971998.Google Scholar
Farrar, J.F. and Mapunda, O.P. (1977), Optical properties of the leaves of some African crop plants, Applied Optics, 16(1):248251.Google Scholar
Faruque, A., Bahadur, R. and Carter, G.A. (2001), Application of artificial neural networks for the classification of remote sensing spectral reflectance data of fungal infected soybean leaf, Journal of the Mississippi Academy of Sciences, 46(2):119119.Google Scholar
Favier, J.F., Ross, D.W., Tsheko, R., Kennedy, D.D., Muir, A.Y. and Fleming, J. (1998), Discrimination of weeds in brassica crops using optical spectral reflectance and leaf texture analysis, in Proc. Precision Agriculture and Biological Quality (Meyer G.E. and DeShazer J.A., Eds), Boston, MA, 3 November 1998, SPIE, Vol. 3543, pp. 311318.Google Scholar
Federici, J.F. (2012), Review of moisture and liquid detection and mapping using terahertz imaging, Journal of Infrared, Millimeter, and Terahertz Waves, 33(2):97126.Google Scholar
Feilhauer, H., Asner, G.P. and Martin, R.E. (2015), Multi-method ensemble selection of spectral bands related to leaf biochemistry, Remote Sensing of Environment, 164:5765.Google Scholar
Feng, H., Li, Z., Jin, X., Yang, G., Wan, P., Guo, J., et al. (2016), Estimating equivalent water thickness of apple leaves using hyperspectral data based on EFAST and PLS, Transactions of the Chinese Society of Agricultural Engineering, 32 (12):165171 (in Chinese).Google Scholar
Féret, J.B., François, C., Asner, G.P., Gitelson, A.A., Martin, R.E., Bidel, L.P.R., et al (2008), PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments, Remote Sensing of Environment, 112(6):30303043.Google Scholar
Féret, J.B. and Asner, G.P. (2011), Spectroscopic classification of tropical forest species using radiative transfer modeling, Remote Sensing of Environment, 115(9):24152422.Google Scholar
Féret, J.B., François, C., Gitelson, A., Asner, G.P., Barry, K.M., Panigada, C., et al. (2011), Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling, Remote Sensing of Environment, 115(10):27422750.Google Scholar
Féret, J.B., Gitelson, A.A., Noble, S.D. and Jacquemoud, S. (2017), PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle, Remote Sensing of Environment, 193:204215.Google Scholar
Fernandes, A.M., Melo-Pinto, P., Millan, B., Tardaguila, J. and Diago, M.P. (2015), Automatic discrimination of grapevine (Vitis vinifera L.) clones using leaf hyperspectral imaging and partial least squares, The Journal of Agricultural Science, 153(3):455465.Google Scholar
Field, C.B. and Mooney, H.A. (1986), The photosynthesis – nitrogen relationship, in On the Economy of Plant Form and Function (Givnish, T.J., Ed), Cambridge University Press, New York, pp. 2555.Google Scholar
Field, C.B., Chapin, F.S., Matson, P.A. and Mooney, H.A. (1992), Responses of terrestrial ecosystems to the changing atmosphere: A resource-based approach, Annual Review of Ecology and Systematics, 23:201235.Google Scholar
Filella, I. and Peñuelas, J. (1999), Altitudinal differences in UV absorbance, UV reflectance and related morphological traits of Quercus ilex and Rhododendron ferrugineum in the Mediterranean region, Plant Ecology, 145(1):157165.Google Scholar
Filella, I., Porcar-Castell, A., Munné-Bosch, S., Bäck, J., Garbulsky, M.F. and Peñuelas, J. (2009), PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle, International Journal of Remote Sensing, 30(17):44434455.Google Scholar
Filhol, M.E. (1865), Recherche sur les propriétés chimiques de la chlorophylle, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 61:371373.Google Scholar
Fisher, D.G. (1986), Ultrastructure, plasmodesmatal frequency, and solute concentration in green areas of variegated Coleus blumei Benth. leaves, Planta, 169(2):141152.Google Scholar
Flint, S.D., Jordan, P.W. and Caldwell, M.M. (1985), Plant protective response to enhanced UV-B radiation under field conditions: leaf optical properties and photosynthesis, Photochemistry and Photobiology, 41(1):9599.Google Scholar
Foley, S., Rivard, B., Sánchez-Azofeifa, G.A. and Calvo, J. (2006), Foliar spectral properties following leaf clipping and implications for handling techniques, Remote Sensing of Environment, 103(3):265275.Google Scholar
Foley, W.J., McIlwee, A., Lawler, I., Aragones, L., Woolnough, A.P. and Berding, N. (1998), Ecological applications of near infrared reflectance spectroscopy – A tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance, Oecologia, 116(3):293305.Google Scholar
Fondom, N.Y., Castro-Nava, S. and Huerta, A.J. (2009), Photoprotective mechanisms during leaf ontogeny: cuticular development and anthocyanin deposition in two morphs of Agave striata that differ in leaf coloration, Botany, 87(12):11861197.Google Scholar
Font, R., del Río-Celestino, M., and De Haro, A. (2002), Use of near infrared spectroscopy to evaluate heavy metal content in Brassica juncea cultivated on the polluted soils of the Guadiamar River area, Fresenius Environmental Bulletin, 11(10):777781.Google Scholar
Font, R., del Río-Celestino, M., Vélez, D., Montoro, R., and de Haro-Bailón, A. (2004), Use of near-infrared spectroscopy for determining the total arsenic content in prostrate amaranth, Science of the Total Environment, 327(1–3):93104.Google Scholar
Font, R., Vélez, D., del Río-Celestino, M., de Haro-Bailón, A., and Montoro, R. (2005), Screening inorganic arsenic in rice by visible and near-infrared spectroscopy, Microchimica Acta, 151(3):231239.Google Scholar
Font, R., del Río-Celestino, M. and de Haro-Bailón, A. (2007), Near-infrared reflectance spectroscopy: methodology and potential for predicting trace elements in plants. in Phytoremediation, Methods and Reviews (Willey, N., Ed), Humana Press, Inc., pp. 205217.Google Scholar
Fooshee, W.C. and Henny, R.J. (1990), Chlorophyll leaves and anatomy of variegated and nonvarigated areas of Aglaonema nitidum leaves, Proceedings of the Florida State Horticultural Society, 103:170172.Google Scholar
Foster, A.J., Kakani, V.G., Ge, J. and Mosali, J. (2012), Discrimination of switchgrass cultivars and nitrogen treatments using pigment profiles and hyperspectral leaf reflectance data, Remote Sensing, 4(9):25762594.Google Scholar
Fourty, T., Baret, F., Jacquemoud, S., Schmuck, G. and Verdebout, J. (1996), Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems, Remote Sensing of Environment, 56(2):104117.Google Scholar
Fourty, T. and Baret, F. (1998), On spectral estimates of fresh leaf biochemistry, International Journal of Remote Sensing, 19(7):12831297.Google Scholar
Fox, D.L. and Wells, J.R. (1971), Schemochromic blue leaf-surfaces of Selaginella, American Fern Journal, 61(3):137139.Google Scholar
Franklin, B. (1751), Experiments and Observations on Electricity, E. Cave, London, 86 pages.Google Scholar
Franz, E., Gebhardt, M.R. and Unklesbay, K.B. (1991), The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images, Transactions of the ASAE, 34(2):682687.Google Scholar
Franzke, O. and Deussen, O. (2003), Accurate graphical representation of plant leaves, in Proc. Plant Growth Modeling, Simulation, Visualization, and Their Applications (Hu, B.G. and Jaeger, M., Eds), Beijing, China, 13–16 October 2003, 16 pages.Google Scholar
Fraser, A.B. (1994), The sylvanshine: retroreflection from dew-covered trees, Applied Optics, 33(21):45394547.Google Scholar
Fremy, M.E. (1860), Recherches sur la matière colorante verte des feuilles, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 50:405411.Google Scholar
Fremy, M.E. (1865), Recherches chimiques sur la matière verte des feuilles, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 61:188192.Google Scholar
Fridgen, J.L. and Varco, J.J. (2004), Dependency of cotton leaf nitrogen, chlorophyll, and reflectance on nitrogen and potassium availability, Agronomy Journal, 96(1):6369.Google Scholar
Fry, E.S. (2000), Visible and near-ultraviolet absorption spectrum of liquid water: comment, Applied Optics, 39(16):27432744.Google Scholar
Fuchs, L. (1542), De historia stirpium commentarii insignes, Isingrin, Basel, 896 pages.Google Scholar
Fuchs, M. and Tanner, C.B. (1966), Infrared thermometry of vegetation, Agronomy Journal, 58(6):597601.Google Scholar
Fuhrer, M., Jensen, H.W. and Prusinkiewicz, P. (2004), Modeling Hairy Plants, in Proc. 12th Pacific Conference on Computer Graphics and Applications, Seoul, Republic of Korea, 6–8 October 2004, pp. 217226.Google Scholar
Fujino, M., Endo, R. and Omasa, K. (2002), Nondestructive instrumentation of water-stressed cucumber leaves – Comparison between changes in spectral reflectance, stomatal conductance, PSII yield and shape, Agricultural Information Research, 11 (2):161170 (in Japanese).Google Scholar
Fukshansky, L. (1978), On the theory of light absorption in non-homogeneous objects: the sieve-effect in one-component suspensions, Journal of Mathematical Biology, 6(2):177196.Google Scholar
Fukshansky, L. (1981), Optical properties of plants, in Plant and the Daylight Spectrum (Smith, H., Ed), Academic Press, London, pp. 2140.Google Scholar
Fukshansky, L. (1987), Absorption statistics in turbid media, Journal of Quantitative Spectroscopy & Radiative Transfer, 38(5):389406.Google Scholar
Fukshansky, L. and Kazarinova, N. (1980), Extension of the Kubelka-Munk theory of light propagation in intensely scattering materials to fluorescent media, Journal of the Optical Society of America, 70(9):11011111.Google Scholar
Fukshansky, L., Kazarinova, N. and Martinez von Remisowsky, A. (1991), Estimation of optical parameters in a living tissue by solving the inverse problem of the multiflux radiative transfer, Applied Optics, 30(22):31453153.Google Scholar
Fukshansky, L. and Martinez von Remisowsky, A. (1992), A theoretical study of the light microenvironment in a leaf in relation to photosynthesis, Plant Science, 86(2):167182.Google Scholar
Fukshansky, L., Martinez von Remisowsky, A., McClendon, J., Ritterbusch, A., Richter, T. and Mohr, H. (1993), Absorption spectra of leaves corrected for scattering and distributional error: a radiative transfer and absorption statistics treatment, Photochemistry and Photobiology, 57(3):538555.Google Scholar
Fukuhara, M., Degawa, T., Okushima, L., Matsuo, K. and Honma, T. (2000), Physical characteristics of tea leaves by ultrasonic transmission method, Tea Research Journal, 90: 108109 (in Japanese).Google Scholar
Fukuhara, M. (2002), Acoustic characteristics of botanical leaves using ultrasonic transmission waves, Plant Science, 162(4):521528.Google Scholar
Fukuhara, M., Okushima, L., Matsuo, K. and Homma, T. (2005), Acoustic characteristics of fresh tea leaves, Japan Agricultural Research Quarterly, 39(1):4549.Google Scholar
Fukuhara, M., Dutta Gupta, S. and Okushima, L. (2006), Acoustic characteristics of plant leaves using ultrasonic transmission waves, in Plant Tissue Culture Engineering (Dutta Gupta, S. and Ibaraki, Y., Eds), Springer, pp. 427439.Google Scholar
Fung, T., Ma, F.Y. and Siu, W.L. (1998), Hyperspectral data analysis for subtropical tree species recognition, in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS’98), Seattle, WA, 6–10 July 1998 IEEE, Vol. 3, pp. 12981300.Google Scholar
Fung, T., Fung, H., Ma, Y. and Siu, W.L. (2003), Band selection using hyperspectral data of subtropical tree species, Geocarto International, 18(4):311.Google Scholar
Furuya, S. (1986), Growth diagnosis of rice plants by means of leaf color, Japan Agricultural Research Quarterly, 20(3):147153.Google Scholar
Gäb, M., Hoffmann, K., Lobe, M., Metzger, R., van Ooyen, S., Elbers, G. et al. (2006), NIR-spectroscopic investigation of foliage of ozone-stressed Fagus sylvatica trees, Journal of Forest Research, 11(2):6975.Google Scholar
Gabrys-Mizera, H. (1976), Model considerations of the light conditions in noncylindrical plant cells, Photochemistry and Photobiology, 24(5):453461.Google Scholar
Gal, A., Brumfeld, V., Weiner, S., Addadi, L. and Oron, D. (2012), Certain biominerals in leaves function as light scatterers, Advanced Optical Materials, 24(10):OP77OP83.Google Scholar
Galtie, J.F. and Lescure, M. (2009), Dispositif de mesure optoélectronique de l’hydratation d’un végétal dans son environnement naturel, Patent WO/2009/007269, 15 January 2009.Google Scholar
Galvez-Sola, L., García-Sánchez, F., Pérez-Pérez, J.G., et al. (2015), Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy, Frontiers in Plant Science, 6:571.Google Scholar
Gamon, J.A., Field, C.B., Bilger, W., Björkman, O., Fredeen, A.L. and Peñuelas, J. (1990), Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies, Oecologia, 85(1):17.Google Scholar
Gamon, J.A., Peñuelas, J. and Field, C.B. (1992), A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency, Remote Sensing of Environment, 41(1):3544.Google Scholar
Gamon, J.A., Filella, I. and Peñuelas, J. (1993), The dynamic 531-nanometer Delta reflectance signal: a survey of twenty Angiosperm species, in Photosynthetic Responses to the Environment (Yamamoto, H.Y. and Smith, C.M., Eds), American Society of Plant Physiologists, pp. 172177.Google Scholar
Gamon, J.A., Serrano, L. and Surfus, J.S. (1997), The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels, Oecologia, 112(4):492501.Google Scholar
Gamon, J.A. and Qiu, H.L. (1999), Ecological applications of remote sensing at different scales, in Handbook of Functional Plant Ecology (Pugnaire, F. and Valladares, F., Eds), CRC Press, pp. 805846.Google Scholar
Gamon, J.A. and Surfus, J.S. (1999), Assessing leaf pigment content and activity with a reflectometer, New Phytologist, 143(1):105117.Google Scholar
Gamon, J.A., Field, C.B., Fredeen, A.L. and Thayer, S. (2001), Assessing photosynthetic downregulation in sunflower stands with an optically-based model, Photosynthesis Research, 67(1–2):113125.Google Scholar
Gamon, J.A. and Berry, J.A. (2012), Facultative and constitutive pigment effects on the Photochemical Reflectance Index (PRI) in sun and shade conifer needles, Israel Journal of Plant Sciences, 60(1–2):8595.Google Scholar
Ganapol, B.D., Johnson, L.F., Hammer, P.D., Hlavka, C.A. and Peterson, D.L. (1998), LEAFMOD: a new within-leaf radiative transfer model, Remote Sensing of Environment, 63(2):182193.Google Scholar
Ganapol, B.D., Johnson, L.F., Hlavka, C.A., Peterson, D.L. and Bond, B. (1999), LCM2: A coupled leaf/canopy radiative transfer model, Remote Sensing of Environment, 70(2):153166.Google Scholar
Gapinski, J., Dobek, A., Paillotin, G., Breton, J., Leibl, W. and Trissl, H.W. (1994), Light gradient in photosynthetic systems: theory and experiment, Laser Physics, 4:191198.Google Scholar
Garbe, C.S., Schurr, U. and Jähne, B. (2002), Thermographic measurements on plant leaves, in Proc. Thermosense XXIV (Maldague X.P. and Rozlosnik A.E., Eds), Orlando, FL, 1–5 April 2002, SPIE, Vol. 4710, pp. 407416.Google Scholar
Garbulsky, M.F., Peñuelas, J., Gamon, J., Inoue, Y. and Filella, I. (2011), The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis, Remote Sensing of Environment, 115(2):281297.Google Scholar
Garcia Ciudad, A., Fernandez Santos, B., Vazquez de Aldana, B.R., Zabalgogeazcoa, I., Gutierrez, M.Y. and Garcia Criado, B. (2004), Use of near infrared reflectance spectroscopy to assess forage quality of a Mediterranean shrub, Communications in Soil Science and Plant Analysis, 35 (5–6):665678.Google Scholar
Gardner, C.M., Jacques, S.L. and Welch, A.J. (1996), Fluorescence spectroscopy of tissue: recovery of intrinsic fluorescence from measured fluorescence, Applied Optics, 35(10):17801792.Google Scholar
Garlaschi, F.M., Zucchelli, G. and Jennings, R.C. (1989), Studies on light absorption and photochemical activity changes in chloroplast suspensions and leaves due to light scattering and light filtration across chloroplast and vegetation layers, Photosynthesis Research, 20(3):207220.Google Scholar
Garnier, E., Shipley, B., Roumet, C. and Laurent, G. (2001), A standardized protocol for the determination of specific leaf area and leaf dry matter content, Functional Ecology, 15(5):688695.Google Scholar
Garriga, M., Retamales, J.B., Romero-Bravo, S., Caligari, P.D.S. and Lobos, G.A. (2014), Chlorophyll, anthocyanin, and gas exchange changes assessed by spectroradiometry in Fragaria chiloensis under salt stress, Journal of Integrative Plant Biology, 56(5):505515.Google Scholar
Gates, D.M. and Tantraporn, W. (1952), The reflectivity of deciduous trees and herbaceous plants in the infrared to 25 microns, Science, 115(2997):613616.Google Scholar
Gates, D.M. (1962), Leaf temperature and energy exchange, Archiv für Meteorologie, Geophysik und Bioklimatologie, 12(2):321336.Google Scholar
Gates, D.M. (1964), Leaf temperature and transpiration, Agronomy Journal, 56(3):273277.Google Scholar
Gates, D.M. (1965), Energy, plants, and ecology, Ecology, 46(1–2):113.Google Scholar
Gates, D.M. (1966), Transpiration and energy exchange, The Quarterly Review of Biology, 41(4):353364.Google Scholar
Gates, D.M. (1967), Remote sensing for the biologist, BioScience, 17(5):303307.Google Scholar
Gates, D.M. (1968), Energy exchange and ecology, BioScience, 18(2):9095.Google Scholar
Gates, D.M. (1976), Energy exchange and transpiration, in Water and Plant Life (Lange, O.L., Kappen, L. and Schulze, E.D., Eds), Springer-Verlag, pp. 137147.Google Scholar
Gates, D.M. (1980a), Biophysical Ecology, Springer Verlag, 611 pages.Google Scholar
Gates, D.M. (1980b), Energy budgets of plants, in Biophysical Ecology (Reichle, D.E., Ed), Springer-Verlag, New York, pp. 345381.Google Scholar
Gates, D.M. (2003), Biophysical Ecology, Dover, 656 pages.Google Scholar
Gates, D.M., Keegan, H.J., Schleter, V.R. and Weidner, V.R. (1965), Spectral properties of plants, Applied Optics, 4(1):1120.Google Scholar
Gates, D.M., Alderfer, R. and Taylor, E. (1968), Leaf temperatures of desert plants, Science, 159(3818):994995.Google Scholar
Gauslaa, Y. (1984), Heat resistance and energy budget in different Scandinavian plants. 1. Infrared and visible reflectance in different alpine vascular plants, Holarctic Ecology, 7(1):712.Google Scholar
Gausman, H.W., Allen, W.A. and Cardenas, R. (1969a), Reflectance of cotton leaves and their structure, Remote Sensing of Environment, 1(1):1922.Google Scholar
Gausman, H.W., Allen, W.A., Cardenas, R. and Richardson, A.J. (1969b), Relation of light reflectance to cotton leaf maturity (Gossypium hirsutum L.), in Proc. 6th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, 13–16 October 1969, ERIM, Vol. 2, pp. 11231141.Google Scholar
Gausman, H.W., Allen, W.A., Cardenas, R. and Richardson, A.J. (1970c), Relation of light reflectance to histological and physical evaluations of cotton leaf maturity, Applied Optics, 9(3):545552.Google Scholar
Gausman, H.W., Allen, W.A., Cardenas, R. and Richardson, A.J. (1971a), Effects of leaf nodal position on absorption and scattering coefficients and infinite reflectance of cotton leaves, Gossypium hirsutum L., Agronomy Journal, 63(1):8791.Google Scholar
Gausman, H.W., Allen, W.A., Cardenas, R. and Richardson, A.J. (1972), Effects of leaf age for four growth stages of cotton and corn plants on leaf reflectance, structure, thickness, water and chlorophyll concentrations and selection of wavelengths for crop discrimination, in Proc. Remote Sensing of Earth Resources: Technical papers selected from the Conference on Earth Resources Observation and Information Analysis System (Shahrokhi F., Ed), Tullahoma, TN, 13–14 March 1972, Vol. 1, pp. 2551.Google Scholar
Gausman, H.W., Allen, W.A., Myers, V.I. and Cardenas, R. (1969c), Reflectance and internal structure of cotton leaves, Gossypium hirsutum L., Agronomy Journal, 61(3):374376.Google Scholar
Gausman, H.W. and Cardenas, R. (1969), Effect of leaf pubescence of Gynura aurantiaca on light reflectance, Botanical Gazette, 130(3):158162.Google Scholar
Gausman, H.W. and Cardenas, R. (1973), Light reflectance by leaflets of pubescent, normal, and glabrous soybean lines, Agronomy Journal, 65(5):837838.Google Scholar
Gausman, H.W., Allen, W.A., Cardenas, R. and Bowen, R.L. (1970a), Color photos, cotton leaves, and soil salinity, Photogrammetric Engineering, 36(5):454459.Google Scholar
Gausman, H.W., Allen, W.A., Cardenas, R. and Bowen, R.L. (1970b), Detection of foot rot disease of grapefruit trees with infrared color film, Journal of the Rio Grande Valley Horticultural Society, 24:3642.Google Scholar
Gausman, H.W., Allen, W.A., Myers, V.I., Cardenas, R. and Leamer, R.W. (1970d), Reflectance of single leaves and field plots of Cycocel-treated cotton (Gossypium hirsutum L.) in relation to leaf structure, Remote Sensing of Environment, 1(2):103107.Google Scholar
Gausman, H.W., Allen, W.A., Escobar, D.E., Rodriguez, R.R. and Cardenas, R. (1971b), Age effects of cotton leaves on light reflectance, transmittance, and absorptance and on water content and thickness, Agronomy Journal, 63(3):465469.Google Scholar
Gausman, H.W., Allen, W.A., Wiegand, C.L., Escobar, D.E. and Rodriguez, R.R. (1971c), Leaf light reflectance, transmittance, absorptance, and optical and geometrical parameters for eleven plant genera with different leaf mesophyll arrangements, in Proc. 7th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, 17–21 May 1971, Vol. 3, pp. 15991625.Google Scholar
Gausman, H.W., Allen, W.A., Wiegand, C.L., Escobar, D.E., Rodriguez, R.R. and Richardson, A.J. (1973), The Leaf Mesophylls of Twenty Crops, their Light Spectra, and Optical and Geometrical Parameters, USDA, Technical Bulletin 1465, 59 pages.Google Scholar
Gausman, H.W. (1974), Leaf reflectance of near-infrared, Photogrammetric Engineering, 40(2):183191.Google Scholar
Gausman, H.W., Allen, W.A. and Escobar, D.E. (1974a), Refractive index of plant cell walls, Applied Optics, 13(1):109111.Google Scholar
Gausman, H.W., Cardenas, R. and Berumen, A. (1974b), Effects of leaf age within growth stages of pepper and sorghum plants on leaf thickness, water, chlorophyll, and light reflectance, in Proc. Remote Sensing of Earth Resources: Technical papers selected from the Conference on Earth Resources Observation and Information Analysis System (Shahrokhi F., Ed), Tullahoma, TN, 25–27 March 1974, Vol. 3, pp. 3956.Google Scholar
Gausman, H.W. and Hart, W.G. (1974a), Reflectance of four levels of sooty-mold deposits produced from the honeydew of three insect species, Journal of the Rio Grande Valley Horticultural Society, 28:131136.Google Scholar
Gausman, H.W. and Hart, W.G. (1974b), Reflectance of sooty mold fungus on citrus leaves over the 2.5 to 40-micrometer wavelength interval, Journal of Economic Entomology, 67(4):479480.Google Scholar
Gausman, H.W., Heald, C.M. and Escobar, D.E. (1975a), Effect of Rotylenchulus reniformis on reflectance of cotton plant leaves, Journal of Nematology, 7(4):368374.Google Scholar
Gausman, H.W., Heald, C.M. and Escobar, D.E. (1975b), Ultraviolet radiation reflectance, transmittance, and absorptance by plant leaf epidermises, Agronomy Journal, 67(5):720724.Google Scholar
Gausman, H.W., Rodriguez, R.R. and Richardson, A.J. (1976), Infinite reflectance of dead compared with live vegetation, Agronomy Journal, 68(2):295296.Google Scholar
Gausman, H.W., Escobar, D.E. and Wiegand, C.L. (1977), Reflectance and photographic characteristics of three citrus varieties for discrimination purpose, in Proc. Remote Sensing of Earth Resources: Technical papers selected from the Sixth Annual Remote Sensing of Earth Resources Conference (Shahrokhi F., Ed), Tullahoma, TN, 29–31 March 1977, Vol. 6, pp. 341355.Google Scholar
Gausman, H.W., Escobar, D.E., Everitt, J.H., Richardson, A.J. and Rodriguez, R.R. (1978a), Distinguishing succulent plants from crop and woody plants, Photogrammetric Engineering & Remote Sensing, 44(4):487491.Google Scholar
Gausman, H.W., Escobar, D.E., Rodriguez, R.R., Thomas, C.E. and Bowen, R.L. (1978b), Ozone damage detection in cantaloupe plants, Photogrammetric Engineering & Remote Sensing, 44(4):481485.Google Scholar
Gausman, H.W., Everitt, J.H. and Escobar, D.E. (1979), Seasonal nitrogen concentration and reflectance of seven woody plant species, Journal of the Rio Grande Valley Horticultural Society, 33:101104.Google Scholar
Gausman, H.W., Menges, R.M., Richardson, A.J., Walter, H., Rodriguez, R.R. and Tamez, S. (1981), Optical parameters of leaves of seven weed species, Weed Science, 29(1):2426.Google Scholar
Gausman, H.W. (1984), Evaluation of factors causing reflectance differences between sun and shade leaves, Remote Sensing of Environment, 15(2):177181.Google Scholar
Gauthier, A. (1906), Sur la coloration rouge éventuelle de certaines feuilles et sur la couleur des feuilles d’automne, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 143:490491.Google Scholar
Gay, A., Thomas, H., Roca, M., et al. (2008), Nondestructive analysis of senescence in mesophyll cells by spectral resolution of protein synthesis-dependent pigment metabolism, New Phytologist, 179(3):663674.Google Scholar
Gayford, M. (2011), A Bigger Message: Conversations with David Hockney, Thames & Hudson Ltd, 248 pages.Google Scholar
Gazala, I.F.S., Sahoo, R.N., Pandey, R., Mandal, B., Gupta, V.K., Singh, R., et al. (2013), Spectral reflectance pattern in soybean for assessing yellow mosaic disease, Indian Journal of Virology, 24(2):242249.Google Scholar
Ge, H., Lu, S. and Zhao, Y.S. (2012), Effects of leaf hair on leaf reflectance and hyperspectral vegetation indices, Spectroscopy and Spectral Analysis, 32 (2):439444 (in Chinese).Google Scholar
Gebeshuber, I.C. and Lee, D.W. (2012), Nanostructures for coloration (Organisms other than animals), in Encyclopedia of Nanotechnology, Springer, pp. 17901803.Google Scholar
Geiger, B. (1993), Three-dimensional modeling of human organs and its application to diagnosis and surgical planning, in INRIA, Programme 4. Robotique, Image et Vision, 119 pages.Google Scholar
Gemmell, F.M. and Colls, J.J. (1992), The effects of sulphur dioxide on the spectral characteristics of leaves of Vicia faba L., International Journal of Remote Sensing, 13(14):25472563.Google Scholar
Gente, R., Born, N., Voß, N., Sannemann, W., Léon, J., Koch, M. and Castro-Camus, E. (2013), Determination of leaf water content from terahertz time-domain spectroscopic data, Journal of Infrared, Millimeter, and Terahertz Waves, 34(3–4):316323.Google Scholar
Gente, R. and Koch, M. (2015), Monitoring leaf water content with THz and sub-THz waves, Plant Methods, 11:15.Google Scholar
Geoffroy, C.J. (1707), Observations sur les huiles essentielles, avec quelques conjectures sur sur la cause des couleurs des feuilles et des fleurs des plantes, Histoire de l’Académie royale des sciences, Tome I, pp. 517526.Google Scholar
Georgiev, G.T., Gatebe, C.K., Butler, J.J. and King, M.D. (2007), BRDF calibration of natural samples in support of remote sensing, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’07), Barcelona, Spain, 23–28 July 2007, IEEE, pp. 28772880.Google Scholar
Georgiev, G.T., Gatebe, C.K., Butler, J.J. and King, M.D. (2009), BRDF analysis of savanna vegetation and salt-pan samples, IEEE Transactions on Geoscience and Remote Sensing, 47(8):25462556.Google Scholar
Gerber, F., Marion, R., Olioso, A., Jacquemoud, S., Ribeiro da Luz, B. and Fabre, S. (2011), Modeling directional-hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 µm to 5.7 µm with the PROSPECT-VISIR model, Remote Sensing of Environment, 115(1):404414.Google Scholar
Gianoli, E. and Carrasco-Urra, F. (2014), Leaf mimicry in a climbing plant protects against herbivory, Current Biology, 24(9):984987.Google Scholar
Gibeaut, D.M. and Thomson, W.W. (1989), Stereology of the internal structures of leaves in Peperomia obtusifolia, P. camptotricha, and P. scandens, Botanical Gazette, 150(2):115121.Google Scholar
Gibson, L.J. and Ashby, M.F. (1997), Cellular Solids: Structure and Properties, Cambridge University Press, 532 pages.Google Scholar
Gillon, D., Joffre, R. and Dardenne, P. (1993), Predicting the stage of decay of decomposing leaves by near-infrared reflectance spectroscopy, Canadian Journal of Forest Research, 23(12):25522559.Google Scholar
Gillon, D., Hernando, C., Valette, J.C. and Joffre, R. (1997), Fast estimation of the calorific values of forest fuels by near-infrared reflectance spectroscopy, Canadian Journal of Forest Research, 27(5):760765.Google Scholar
Gillon, D., Houssard, C. and Joffre, R. (1999a), Using near-infrared reflectance spectroscopy to predict carbon, nitrogen and phosphorus content in heterogeneous plant material, Oecologia, 118(2):173182.Google Scholar
Gillon, D., Joffre, R. and Ibrahima, A. (1999b), Can litter decomposability be predicted by near infrared reflectance spectroscopy? Ecology, 80(1):175186.Google Scholar
Gillon, D., Dauriac, F., Deshayes, M., Valette, J.C. and Moro, C. (2002), Foliage moisture content and spectral characteristics using near infrared reflectance spectroscopy (NIRS), in Proc. 4th International Conference on Forest Fire Research (Viegas D.X., Ed), Coimbra, Portugal, 18–23 November 2002, 13 pages.Google Scholar
Gillon, D., Dauriac, F., Deshayes, M., Valette, J.C. and Moroc, C. (2004), Estimation of foliage moisture content using near infrared reflectance spectroscopy, Agricultural and Forest Meteorology, 124(1–2):5162.Google Scholar
Gilmore, A.M., Hazlett, T.L. and Govindjee, (1995), Xanthophyll cycle-dependent quenching of photosystem II chlorophyll a fluorescence: formation of a quenching complex with a short fluorescence lifetime, Proceedings of the National Academy of Sciences, 92(6):22732277.Google Scholar
Gislum, R., Micklander, E. and Nielsen, J.P. (2004), Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics, Field Crops Research, 88(2–3):269277.Google Scholar
Gitelson, A. and Merzlyak, M.N. (1994a), Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves, Journal of Photochemistry and Photobiology. B, Biology, 22(3):247252.Google Scholar
Gitelson, A. and Merzlyak, M.N. (1994b), Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation, Journal of Plant Physiology, 143(3–4):286292.Google Scholar
Gitelson, A. and Merzlyak, M.N. (1996), Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll, Journal of Plant Physiology, 148(3–4):494500.Google Scholar
Gitelson, A. and Merzlyak, M.N. (1997), Remote estimation of chlorophyll content in higher plant leaves, International Journal of Remote Sensing, 18(12):26912697.Google Scholar
Gitelson, A.A., Merzlyak, M.N. and Lichtenthaler, H.K. (1996), Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm, Journal of Plant Physiology, 148(3–4):501508.Google Scholar
Gitelson, A.A., Merzlyak, M.N. and Lichtenthaler, H.K. (1998), Leaf chlorophyll fluorescence corrected for re-absorption by means of absorption and reflectance measurements, Journal of Plant Physiology, 152(2–3):283296.Google Scholar
Gitelson, A.A., Merzlyak, M.N. and Lichtenthaler, H.K. (1999), The chlorophyll fluorescence ratio R735/F700 as an accurate measure of the chlorophyll content in plants, Remote Sensing of Environment, 69(3):296302.Google Scholar
Gitelson, A.A., Merzlyak, M.N. and Chivkunova, O.B. (2001), Optical properties and nondestructive estimation of anthocyanin content in plant leaves, Photochemistry and Photobiology, 74(1):3845.Google Scholar
Gitelson, A.A., Zur, Y., Chivkunova, O.B. and Merzlyak, M.N. (2002), Assessing carotenoid content in plant leaves with reflectance spectroscopy, Photochemistry and Photobiology, 75(3):272291.Google Scholar
Gitelson, A.A., Gritz, Y. and Merzlyak, M.N. (2003), Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology, 160(3):271282.Google Scholar
Gitelson, A.A., Keydan, G.P. and Merzlyak, M.N. (2006), Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves, Geophysical Research Letters, 33:L11402.Google Scholar
Gitelson, A.A., Gamon, J.A. and Solovchenko, A. (2017a), Multiple drivers of seasonal change in PRI: implications for photosynthesis. 1: Leaf level, Remote Sensing of Environment, 191:110116.Google Scholar
Gitelson, A.A., Gamon, J.A. and Solovchenko, A. (2017b), Multiple drivers of seasonal change in PRI: implications for photosynthesis 2. Stand, Remote Sensing of Environment, 190:198206.Google Scholar
Giusti, M.M. and Wrolstad, R.R. (2001), Characterization and measurement of anthocyanins by UV-visible spectroscopy, in Current Protocols in Food Analytical Chemistry, John Wiley & Sons, pp. F1.2.1-F1.2.13.Google Scholar
Giusti, M.M. and Jing, P. (2008), Analysis of anthocyanins, in Food Colorants: Chemical and Functional Properties, CRC Press, pp. 479506.Google Scholar
Givnish, T.J. (1990), Leaf mottling: relation to growth form and leaf phenology and possible role as camouflage, Functional Ecology, 4(4):463474.Google Scholar
Glassner, A.S. (1989), Surface physics for ray tracing, in An Introduction to Ray Tracing, The Morgan Kaufmann Series in Computer Graphics, Morgan Kaufmann, pp. 121160.Google Scholar
Gledhill, D. (1985), The Names of Plants, 2nd Edition, Cambridge University Press, 202 pages.Google Scholar
Glover, B.J. and Whitney, H.M. (2010), Structural colour and iridescence in plants: the poorly studied relations of pigment colour, Annals of Botany, 105(4):505511.Google Scholar
Goetz, A.F.H., Gao, B.C., Wessman, C.A. and Bowman, W.D. (1990), Estimation of biochemical constituents from fresh, green leaves by spectrum matching techniques, in Proc. 10th International Geoscience and Remote Sensing Symposium (IGARSS’90), College Park, MD, 20–24 May 1990, IEEE, pp. 971974.Google Scholar
Goldstein, D.H. and Cox, J.L. (2004), Spectropolarimetric properties of vegetation, in Proc. Polarization: Measurement, Analysis, and Remote Sensing I (Goldstein D.H. and Chenault D.B., Eds), Orlando, FL, 12 April 2004, SPIE, Vol. 5432, pp. 5362.Google Scholar
Gonzales, R., Paul, N.D., Percy, K.E., Ambrose, M., McLaughlin, C.K., Barnes, J.D., et al. (1996), Responses to ultraviolet-B radiation (280–315 nm) of pea (Pisum sativum) lines differing in leaf surface wax, Physiologia Plantarum, 98(4):852860.Google Scholar
González-Martín, I., Hernández-Hierro, J.M. and González-Cabrera, J.M. (2007), Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa, Analytical and Bioanalytical Chemistry, 387(6):21992205.Google Scholar
Gorbunova, G.S., Parshina, Z.S. and Bedenko, V.P. (1960),Optical properties and photosynthesis of certain species of cultivated and wild plants in relation to ecological conditions, Trudy sektora astrobotaniki, 8: 3151 (in Russian).Google Scholar
Gorton, H.L., Williams, W.E. and Vogelmann, T.C. (1999), Chloroplast movement in Alocasia macrorrhiza, Physiologia Plantarum, 106(4):421428.Google Scholar
Gorton, H.L., Brodersen, C.R., Williams, W.E. and Vogelmann, T.C. (2010), Measurement of the optical properties of leaves under diffuse light, Photochemistry and Photobiology, 86(5):10761083.Google Scholar
Göttlicher, D., Albert, J., Nauss, T. and Bendix, J. (2011), Optical properties of selected plants from a tropical mountain ecosystem – Traits for plant functional types to parametrize a land surface model, Ecological Modelling, 222(3):493502.Google Scholar
Gottwald, T.R., da Graça, J.V. and Bassanezi, R.B. (2007), Citrus Huanglongbing: the pathogen and its impact, in Plant Health Progress, 36 pages.Google Scholar
Goudarzi, U., Mokhtari, J. and Nouri, M. (2013), Camouflage of cotton fabrics in visible and near infrared region using vat dyes, Journal of Color Science and Technology, 7 (1):35–25 (in Persian).Google Scholar
Goudarzi, U., Mokhtari, J. and Nouri, M. (2014), Camouflage of cotton fabrics in visible and NIR region using three selected vat dyes, Color Research & Application, 39(2):200207.Google Scholar
Goulas, Y., Cerovic, Z.G., Cartelat, A. and Moya, I. (2004), Dualex: a new instrument for field measurements of epidermal ultraviolet absorbance by chlorophyll fluorescence, Applied Optics, 43(23):44884496.Google Scholar
Gould, J.M. (1982), Characterization of lignin in situ by photoacoustic spectroscopy, Plant Physiology, 70(5):15211525.Google Scholar
Gould, K.S. and Lee, D.W. (1996), Physical and ultrastructural basis of blue leaf iridescence in four Malaysian understory plants, American Journal of Botany, 83(1):4550.Google Scholar
Gould, K.S., Vogelmann, T.C., Han, T. and Clearwater, M.J. (2002), Profiles of photosynthesis within red and green leaves of Quintinia serrata, Physiologia Plantarum, 116(1):127133.Google Scholar
Gould, K.S., Davies, K.M. and Winefield, C., Eds (2009), Anthocyanins: Biosynthesis, Functions, and Applications, Springer, 329 pages.Google Scholar
Govaerts, Y., Jacquemoud, S., Verstraete, M.M. and Ustin, S.L. (1996), Three-dimensional radiation transfer modeling in a dicotyledon leaf, Applied Optics, 35(33):65856598.Google Scholar
Govaerts, Y. and Verstraete, M.M. (1998), Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media, IEEE Transactions on Geoscience and Remote Sensing, 36(2):493505.Google Scholar
Govindjee, and Krogmann, D. (2004), Discoveries in oxygenic photosynthesis (1727–2003): a perspective, Photosynthesis Research, 80(1–3):1557.Google Scholar
Graeff, S., Steffens, D. and Schubert, S. (2001), Use of reflectance measurements for the early detection of N, P, Mg, and Fe deficiencies in Zea mays L., Journal of Plant Nutrition and Soil Science, 164(4):445450.Google Scholar
Graeff, S. and Claupein, W. (2003), Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectance measurements, European Journal of Agronomy, 19(4):611618.Google Scholar
Graeff, S., Link, J. and Claupein, W. (2006), Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements, Central European Journal of Biology, 1(2):275288.Google Scholar
Graham, R.M., Lee, D.W. and Norstog, K. (1993), Physical and ultrastructural basis of blue leaf iridescence in two neotropical ferns, American Journal of Botany, 80(2):198203.Google Scholar
Grant, L. (1985), Polarized and non-polarized components of leaf reflectance, PhD Thesis, Purdue University, West Lafayette, IN, 142 pages.Google Scholar
Grant, L. (1987), Diffuse and specular characteristics of leaf reflectance, Remote Sensing of Environment, 22(2):309322.Google Scholar
Grant, L., Daughtry, C.S.T. and Vanderbilt, V.C. (1987a), Polarized and non-polarized leaf reflectance of Coleus blumei, Environmental and Experimental Botany, 27(2):139145.Google Scholar
Grant, L., Daughtry, C.S.T. and Vanderbilt, V.C. (1987b), Variations in the polarized leaf reflectance of Sorghum bicolor, Remote Sensing of Environment, 21(3):333339.Google Scholar
Grant, L., Daughtry, C.S.T. and Vanderbilt, V.C. (1993), Polarized and specular reflectance variation with leaf surface features, Physiologia Plantarum, 88(1):19.Google Scholar
Grant, R.H., Heisler, G.M., Gaoa, W. and Jenks, M. (2003), Ultraviolet leaf reflectance of common urban trees and the prediction of reflectance from leaf surface characteristics, Agricultural and Forest Meteorology, 120(1–4):127139.Google Scholar
Graβmann, J. (2005), Terpenoids as plant antioxidants. In Vitamins and Hormones, Vol. 72, Elsevier, pp. 505535.Google Scholar
Gratani, L., Covone, F. and Larcher, W. (2006), Leaf plasticity in response to light of three evergreen species of the Mediterranean maquis, Trees – Structure and Function, 20(5):549558.Google Scholar
Greiner, M.A., Duncan, B.D. and Dierking, M.P. (2007), Bidirectional scattering distribution functions of maple and cottonwood leaves, Applied Optics, 46(25):64856494.Google Scholar
Greiner, M.A., Duncan, B.D. and Dierking, M.P. (2009), Monte Carlo simulation of multiple photon scattering in sugar maple tree canopies, Applied Optics, 48(32):61596171.Google Scholar
Grew, N. (1682), The Anatomy of Plants with an Idea of a Philosophical History of Plants, W. Rawlins, London, 561 pages.Google Scholar
Griffioen, H., Kornet, J.G. and Schurer, K. (1992), An optical leaf wetness sensor, Acta Horticulturae, 304:127135.Google Scholar
Griffiths, P.R. and Dahm, D.J. (2008), Continuum and discontinuum theories of diffuse reflection, in Handbook of Near-Infrared Analysis (Burns, D.A. and Ciurczak, E.W., Eds), CRC Press, pp. 2164.Google Scholar
Grime, J.P. (1961), Measurement of leaf colour, Nature, 191(4788):614615.Google Scholar
Grisham, M.P., Johnson, R.M. and Zimba, P.V. (2010), Detecting sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes, Journal of Virological Methods, 167(2):140145.Google Scholar
Gronwall, T.H. (1926), Reflection of radiation from a finite number of equally spaced parallel planes, Physical Review, 27(3):277285.Google Scholar
Grossman, Y.L., Ustin, S.L., Jacquemoud, S., Sanderson, E., Schmuck, G. and Verdebout, J. (1996), Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data, Remote Sensing of Environment, 56(3):182193.Google Scholar
Gruninger, J.H., Robertson, D.C. and Pervaiz, M.M. (1992), Data Analysis for Bark and Leaf Reflectance Measurements, Smart Weapons Operability Enhancement, June 1992, SWOE Report 92–6, 52 pages.Google Scholar
Gudi, G., Krähmer, A., Krüger, H. and Schulz, H. (2015), Attenuated total reflectance−Fourier transform infrared spectroscopy on intact dried leaves of sage (Salvia officinalis L.): accelerated chemotaxonomic discrimination and analysis of essential oil composition, Journal of Agricultural and Food Chemistry, 63(39):87438750.Google Scholar
Guo, H., Jin, L.S. and Lin, G.L. (2008), Rapid determination of nitrogen and potassium contents in tobacco leaves by near infrared reflectance spectroscopy, Heilongjiang Agricultural Sciences, 4: 103104 (in Chinese).Google Scholar
Guo, H., Chen, J., Pan, T., Wang, J. and Cao, G. (2014), Vis-NIR wavelength selection for non-destructive discriminant analysis of breed screening of transgenic sugarcane, Analytical Methods, 6:88108816.Google Scholar
Guyot, G., Baret, F. and Jacquemoud, S. (1992), Imaging spectroscopy for vegetation studies, in Imaging Spectroscopy: Fundamentals and Prospective Application (Toselli, F. and Bodechtel, J., Eds), pp. 145165.Google Scholar
Gupta, R.K. and Woolley, J.T. (1971), Spectral properties of soybean leaves, Agronomy Journal, 63(1):123126.Google Scholar
Guyon, E. and Troadec, J.P. (1994), Du sac de billes au tas de sable, Editions Odile Jacob, Paris, 306 pages.Google Scholar
Habel, R., Kusternig, A. and Wimmer, M. (2007), Physically based real-time translucency for leaves, in Proc. Eurographics Symposium on Rendering (Kautz, J. and Pattanaik, S., Eds), pp. 253263.Google Scholar
Habel, R. (2009), Real-time Rendering and Animation of Vegetation, PhD Thesis, Fakultät für Technische Naturwissenschaften und Informatik, Technischen Universität Wien, Wien (Osterreich), 137 pages.Google Scholar
Habel, R. (2010), Real-time rendering and animation of vegetation, Buletinul Institutului Politehnic din Iaşi, 4:115130.Google Scholar
Haberlandt, G. (1914), Optical sense-organs, in Physiological Plant Anatomy (Haberlandt, G., Ed), MacMillan and Co., London, pp. 613631.Google Scholar
Hadjiloucas, S., Karatzas, L.S. and Bowen, J.W. (1999), Measurements of leaf water content using terahertz radiation, IEEE Transactions on Microwave Theory and Techniques, 47(2):142149.Google Scholar
Hadjiloucas, S., Galvão, R.K.H. and Bowen, J.W. (2002), Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms, Journal of the Optical Society of America A, 19(12):24952509.Google Scholar
Hadjiloucas, S., Walker, G.C., Bowen, J.W. and Zafiropoulos, A. (2009), Propagation of errors from a null balance terahertz reflectometer to a sample’s relative water content, Journal of Physics: Conference Series, 178(1):012012.Google Scholar
Hagen, S.B., Folstad, I. and Jakobsen, S.W. (2003), Autumn colouration and herbivore resistance in mountain birch (Betula pubescens), Ecology Letters, 6(9):807811.Google Scholar
Hagenbach, E. (1870), Untersuchung über die optischen Eigenschaften des Blattgrüns, Annalen der Physik, 217(10):245275.Google Scholar
Hale, G.M. and Querry, M.R. (1973), Optical constants of water in the 200-nm to 200-µm wavelength region, Applied Optics, 12(3):555562.Google Scholar
Hamilton, R.J. (1995), Waxes: Chemistry, Molecular Biology and Function, Oily Press Ltd, 360 pages.Google Scholar
Hamilton, W.D. and Brown, S.P. (2001), Autumn tree colours as a handicap signal, Proceedings of the Royal Society of London. Series B, 268(1475):14891493.Google Scholar
Han, S., Yu, B., Sun, M., Huang, W., Liu, L. and Sun, G. (2009), Development of a portable meter for detecting chlorophyll, nitrogen and water contents of plants, Transactions of the Chinese Society of Agricultural Machinery, 40(z1):256259 (in Chinese).Google Scholar
Hana, M., McClure, W.F., Whitaker, T.B., White, M.W. and Bahler, D.R. (1997), Applying neural networks. Part 2. Using near infrared to classify tobacco types and identify native grown tobacco, Journal of Near Infrared Spectroscopy, 5(1):1925.Google Scholar
Hanrahan, P. and Krueger, W. (1993), Reflection from layered surfaces due to subsurface scattering, in Proc. Computer Graphics Proceedings (SIGGRAPH 93) (Kajiya J.T., Ed), Anaheim, CA, Vol. 27, pp. 165174.Google Scholar
Hara, N. (1957), Study of the variegated leaves with special reference to those caused by air spaces, Japanese Journal of Botany, 16(1):86101.Google Scholar
Harbinson, J. and Woodward, I. (1987), The use of microwaves to monitor the freezing and thawing of water in plants, Journal of Experimental Botany, 38(183):13251335.Google Scholar
Hardacre, A.K., Nicholson, H.F. and Boyce, M.L.P. (1984), A portable photometer for the measurement of chlorophyll in intact leaves, New Zealand Journal of Experimental Agriculture, 12(4):357362.Google Scholar
Hardin, J.A., Jones, C.L., Maness, N.O., Weckler, P.R. and Dillwith, J.W. (2011), Rapid in situ quantification of leaf cuticular wax using FTIR-ATR and DSC, in Proc. 2011 ASAE Annual Meeting, Louisville, KY, 7–10 August 2011, ASABE, 18 pages.Google Scholar
Hardin, J.A., Jones, C.L., Weckler, P.R., Maness, N.O., Dillwith, J.W. and Madden, R.D. (2013), Rapid quantification of spinach leaf cuticular wax using Fourier transform infrared attenuated total reflectance spectroscopy, Transactions of the ASABE, 56(1):331339.Google Scholar
Hardwick, K. and Baker, N.R. (1973), In vivo measurement of chlorophyll content of leaves, New phytologist, 72(1):5154.Google Scholar
Harrington, D.F. and Clark, C. (1989), Reduction in light reflectance of leaves of Encelia densifolia (Asteraceae) by trichome wetting, Madroño, 36(3):180186.Google Scholar
Harron, J. (2000), Optical properties of phytoelements in conifers, MSc Thesis, Department of Earth and Space Science, York University, North York, ON, 208 pages.Google Scholar
Hart, W.G. and Myers, V.I. (1968), Infrared aerial color photography for detection of populations of brown soft scale in citrus groves, Journal of Economic Entomology, 61(3):617624.Google Scholar
Hart, W.G., Gausman, H.W. and Rodriguez, R.R. (1976), Citrus blackfly (Hemiptera: Aleyrodidae), feeding injury and its influence on the spectral properties of citrus foliage, Journal of the Rio Grande Valley Horticultural Society, 30:3743.Google Scholar
Harting, P. (1855), Ueber das Absorptionsvermögen des reinen und des unreinen Chlorophylls für die Strahlen der Sonne, Annalen der Physik, 172(12):543550.Google Scholar
Harvey, B. (2007), Russian Planetary Exploration: History, Development, Legacy, Prospects, Springer, New York, 354 pages.Google Scholar
Hashimoto, Y., Ino, T., Kramer, P.J., Naylor, A.W. and Strain, B.R. (1984), Dynamic analysis of water stress of sunflower leaves by means of a thermal image processing system, Plant Physiology, 76(1):266269.Google Scholar
Hatier, J.H.B. and Gould, K.S. (2007), Black coloration in leaves of Ophiopogon planiscapus ‘Nigrescens’. Leaf optics, chromaticity, and internal light gradients, Functional Plant Biology, 34(2):130138.Google Scholar
Hattey, J.A., Sabbe, W.E., Baten, G.D. and Blakeney, A.B. (1994), Nitrogen and starch analysis of cotton leaves using near infrared reflectance spectroscopy (NIRS), Communications in Soil Science and Plant Analysis, 25(9–10):18551863.Google Scholar
Hawkins, S.A., Park, B., Poole, G.H., Gottwald, T., Windham, W.R. and Lawrence, K.C. (2010), Detection of Citrus Huanglongbing by Fourier transform infrared-attenuated total reflection spectroscopy, Applied Spectroscopy, 64(1):100103.Google Scholar
Heath, R. (1994), Possible mechanisms for the inhibition of photosynthesis by ozone, Photosynthesis Research, 39(3):439451.Google Scholar
Heath, S.M., Southworth, D. and D’Allura, J.A. (1997), Localization of nickel in epidermal subsidiary cells of leaves of Thlaspi montanum var. Siskiyouense (Brassicaceae) using energy-dispersive X-ray microanalysis, International Journal of Plant Sciences, 158(2):184188.Google Scholar
Hébant, C. and Lee, D.W. (1984), Ultrastructural basis and developmental control of blue iridescence in Selaginella leaves, American Journal of Botany, 71(2):216219.Google Scholar
Hecht, H.G. (1976), The interpretation of diffuse reflectance spectra, Journal of Research of the National Bureau of Standards-A. Physics and Chemistry, 80A(4):567583.Google Scholar
Hegedüs, R. and Horvath, G. (2004a), How and why are uniformly polarization-sensitive retinae subject to polarization-related artefacts? Correction of some errors in the theory of polarization-induced false colours, Journal of Theoretical Biology, 230(1):7 SPIE, 787.Google Scholar
Hegedüs, R. and Horvath, G. (2004b), Polarizational colours could help polarization-dependent colour vision systems to discriminate between shiny and matt surfaces, but cannot unambiguously code surface orientation, Vision Research, 44(20):23372348.Google Scholar
Heil, M., Baumann, B., Andary, C., Linsenmair, K.E. and McKey, D. (2002), Extraction and quantification of “condensed tannins” as a measure of plant anti-herbivore defence? Revisiting an old problem, Naturwissenschaften, 89(11):519524.Google Scholar
Helhel, S., Colak, B. and Özen, S. (2009), Measurement of dielectric constant of thin leaves by moisture content at 4 mm band, Progress in Electromagnetics Research Letters, 7:183191.Google Scholar
Hellicar, A.D., Li, L., Greene, K., Hislop, G., Hanham, S., Nikolic, N. and Du, J. (2007), A 500–700 GHz system for exploring the THz frequency regime, in Proc. 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, Sydney, Australia, 27–30 August 2007, IEEE, 6 pages.Google Scholar
Hellmann, C., Große-Stoltenberg, A., Lauströer, V., Oldeland, J. and Werner, C. (2015), Retrieving nitrogen isotopic signatures from fresh leaf reflectance spectra: disentangling δ15N from biochemical and structural leaf properties, Frontiers in Plant Science, 6:307.Google Scholar
Hemenger, R.P. (1977), Optical properties of turbid media with specularly reflecting boundaries: applications to biological problems, Applied Optics, 16(7):20072012.Google Scholar
Henckel, J.F. (1760), Flora saturnisans, ou preuve de l’alliance qui existe entre le règne végétal et le règne minéral, Chez Jean-Thomas Hérissant, Paris, 284 pages (Traduit de l’Allemand par M. Charas, Apothicaire à Paris).Google Scholar
Henderson, N.C., Dick, R.J., Grandinetti, P.M., Riley, G.R. and Roop, D.E. (1974), Camouflage by Artificial Foliage, Battelle Colombus Laboratories, Colombus, OH, June 1974, 64 pages.Google Scholar
Hendry, G.A.F., Houghton, J.D. and Brown, S.B. (1987), The degradation of chlorophyll – A biological enigma, New Phytologist, 107(2):255302.Google Scholar
Henry, W.B., Shaw, D.R., Reddy, K.R., Bruce, L.M. and Tamhankar, H.D. (2004), Spectral reflectance curves to distinguish soybean from common Cocklebur (Xanthium strumarium) and Sicklepod (Cassia obtusifolia) grown with varying soil moisture, Weed Science, 52(5):788796.Google Scholar
Heredia, A. (2003), Biophysical and biochemical characteristics of cutin, a plant barrier biopolymer, Biochimica et Biophysica Acta, 1620(1–3):17.Google Scholar
Hernández, E.I., Melendez-Pastor, I., Navarro-Pedreño, J. and Gómez, I. (2014), Spectral indices for the detection of salinity effects in melon plants, Scientia Agricola (Piracicaba, Braz.), 71(4):324330.Google Scholar
Hernández-Clemente, R., Navarro-Cerrillo, R.M., Suárez, L., Morales, F. and Zarco-Tejada, P.J. (2011), Assessing structural effects on PRI for stress detection in conifer forests, Remote Sensing of Environment, 115(9):23602375.Google Scholar
Herrmann, I., Berenstein, M., Sade, A., Karnieli, A., Bonfil, D.J. and Weintraub, P.G. (2012), Spectral monitoring of two-spotted spider mite damage to pepper leaves, Remote Sensing Letters, 3(4):277283.Google Scholar
Hesketh, M. and Sánchez-Azofeifa, G.A. (2012), The effect of seasonal spectral variation on species classification in the Panamanian tropical forest, Remote Sensing of Environment, 118:7382.Google Scholar
Hesketh, M. and Sánchez-Azofeifa, G.A. (2013), A review of remote sensing of tropical dry forests, in Tropical Dry Forests in the Americas (Quesada, M., Ed), CRC Press, pp. 83100.Google Scholar
Hettinger, J.W., de la Peña Mattozzi, M., Myers, W.R., et al. (2000), Optical coherence microscopy. A technology for rapid, in vivo, non-destructive visualization of plants and plant cells, Plant Physiology, 123(1):315.Google Scholar
Heusinkveld, B.G., Berkowicz, S.M., Jacobs, A.F.G., Hillen, W. and Holtslag, A.A.M. (2008), A new remote optical wetness sensor and its applications, Agricultural and Forest Meteorology, 148(4):580591.Google Scholar
Hickey, L.J. (1973), Classification of the architecture of dicotyledonous leaves, American Journal of Botany, 60(1):1733.Google Scholar
Hildebrandt, S. and Tromba, A. (1995), The Parsimonious Universe – Shape and Form in the Natural World, Springer-Verlag, New York, 330 pages.Google Scholar
Hill, J.F. (2012), Early pioneers of photosynthesis research, in Photosynthesis: Plastid Biology, Energy Conversion and Carbon Assimilation, Advances in Photosynthesis and Respiration, Vol 34 (Eaton-Rye, J.J., Tripathy, B.C. and Sharkey, T.D., Eds),Springer, Dordrecht, pp. 771800.Google Scholar
Hill, R.M., Dissado, L.A., Pugh, J., Broadhurst, M.G., Chiang, C.K. and Wahlstrand, K.J. (1986), The dielectric response of Portulacaceae (Jade) leaves over an extended frequency range, Journal of Biological Physics, 14(4):133135.Google Scholar
Hill, R.M., Dissado, L.A. and Pathmanathan, K. (1987), The low-frequency dielectric properties of leaves, Journal of Biological Physics, 15(1):216.Google Scholar
Hillnhütter, C., Mahlein, A.K., Sikora, R.A. and Oerke, E.C. (2012), Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet, Precision Agriculture, 13(1):1732.Google Scholar
Himmelsbach, D.S., Boer, H., Akin, D.E. and Barton, F.E. (1988), Solid-state 13C NMR, FTIR, and NIRS spectroscopic studies of ruminant silage digestion, in Analytical Applications in Spectroscopy (Creaser, C.S. and Davies, A.M.C., Eds), The Royal Society of Chemistry, London, pp. 410413.Google Scholar
Hirano, T., Kiyota, M., Seki, K. and Aiga, I. (1992), Effects of volcanic ashes from Mt Unzen-Fugendake and Mt Sakurajima on leaf temperature and stomatal conductance of cucumber, Journal of Agricultural Meteorology, 48(2):139145.Google Scholar
Hirata, M., Ishii, R., Kumura, A. and Murata, Y. (1983), Photoinhibition of photosynthesis in soybean leaves. III: Leaf transmittance change in response to incident light intensity, Japanese Journal of Crop Science, 52(4):430434.Google Scholar
Hiukka, R. (1998), A multivariate approach to the analysis of pine needle samples using NIR, Chemometrics and Intelligent Laboratory Systems, 44(1–2):395401.Google Scholar
Hlavka, C.A., Peterson, D.L., Johnson, L.F. and Ganapol, B. (1997), Analysis of forest foliage spectra using a multivariate mixture model, Journal of Near Infrared Spectroscopy, 5(3):167173.Google Scholar
Hmimina, G., Dufrêne, E. and Soudani, K. (2014), Relationship between photochemical reflectance index and leaf ecophysiological and biochemical parameters under two different water statuses: towards a rapid and efficient correction method using real-time measurements, Plant, Cell & Environment, 37(2):473487.Google Scholar
Ho, Q.T., Berghuijs, H.N.C., Watté, R., Verboven, P., Herremans, E., Yin, X., et al. (2016), Three-dimensional microscale modelling of CO2 transport and light propagation in tomato leaves enlightens photosynthesis, Plant, Cell & Environment, 39(1):5061.Google Scholar
Hoch, W.A., Zeldin, E.L. and McCown, B.H. (2001), Physiological significance of anthocyanins during autumnal leaf senescence, Tree Physiology, 21(1):18.Google Scholar
Hoel, B.O. and Solhaug, K.A. (1998), Effect of irradiance on chlorophyll estimation with the Minolta SPAD-502 leaf chlorophyll meter, Annals of Botany, 82(3):389392.Google Scholar
Holdridge, L.R. (1967), Life Zone Ecology, Tropical Science Center, San Jose, Costa Rica, 149 pages.Google Scholar
Holloway, PJ. (1982), The chemical constitution of plant cutins, in The Plant Cuticle (Cutler, D.F., Alvin, K.L., and Price, C.E., Eds), Academic Press, London, pp. 4585.Google Scholar
Holmes, M.G. and Keiller, D.R. (2002), Effects of pubescence and waxes on the reflectance of leaves in the ultraviolet and photosynthetic wavebands: a comparison of a range of species, Plant, Cell & Environment, 25(1):8593.Google Scholar
Holopainen, J.K., Semiz, G. and Blande, J.D. (2009), Life-history strategies affect aphid preference for yellowing leaves, Biology Letters, 5(5):603605.Google Scholar
Hongo, C., Kobayashi, T. and Arita, Y. (1998), The response of water status in tree seedlings through spectral reflectance, Journal of the Japan Society of Photogrammetry and Remote Sensing, 37(4):4350 (in Japanese).Google Scholar
Hoque, E. and Hutzler, P.J.S. (1992), Spectral blue-shift of red edge monitors damage class of beech trees, Remote Sensing of Environment, 39(1):8184.Google Scholar
Horler, D.N.H., Barber, J. and Barringer, A.R. (1980), Effects of heavy metals on the absorbance and reflectance spectra of plants, International Journal of Remote Sensing, 1(2):121136.Google Scholar
Horler, D.H.N. and Barber, J. (1981), Principles of remote sensing of plants, in Plants and the Daylight Spectrum (Smith, H., Ed), Academic Press, London, pp. 4363.Google Scholar
Horler, D.N.H., Barber, J., Darch, J.P., Ferns, D.C. and Barringer, A.R. (1983a), Approaches to detection of geochemical stress in vegetation, Advances in Space Research, 3(2):175179.Google Scholar
Horler, D.N.H., Dockray, M. and Barber, J. (1983b), The red edge of plant leaf reflectance, International Journal of Remote Sensing, 4(2):273288.Google Scholar
Horler, D.N.H., Dockray, M., Barber, J. and Barringer, A.R. (1983c), Red edge measurements for remotely sensing plant chlorophyll content, Advances in Space Research, 3(2):273277.Google Scholar
Hormaetxe, K., Becerril, J.M., Fleck, I., Pinto, M. and Garcia-Plazaola, J.I. (2005), Functional role of red (retro)-carotenoids as passive light filters in the leaves of Buxus sempervirens L.: increased protection of photosynthetic tissues? Journal of Experimental Botany, 56(420):26292636.Google Scholar
Hort, A.F. (1916), Theophrastus. Enquiry into plants: Books 1–5, Harvard University Press, Cambridge, 475 pages.Google Scholar
Hörtensteiner, S. and Feller, U. (2002), Nitrogen metabolism and remobilization during senescence, Journal of Experimental Botany, 53(370):927937.Google Scholar
Horváth, G., Gál, J., Labhart, T. and Wehner, R. (2002), Does reflection polarization by plants influence colour perception in insects? Polarimetric measurements applied to a polarization-sensitive model retina of Papilio butterflies, Journal of Experimental Biology, 205:32813298.Google Scholar
Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, G. and Schmuck, G. (1995), Leaf Optical Properties EXperiment 93 (LOPEX93), European Commission, Joint Research Centre, Ispra, Italy, EUR 16095 EN, 20 pages.Google Scholar
Howard, J.A. (1966), Spectral energy relations of isobilateral leaves, Australian Journal of Biological Sciences, 19(5):757766.Google Scholar
Howard, J.A. (1969), Increased luminance in the direction of reflex reflexion – A recently observed natural phenomenon, Nature, 224(5224):11021103.Google Scholar
Howard, J.A. (1971a), Luminance and luminous intensity indicatrices of isolateral leaves, Applied Optics, 10(10):23542360.Google Scholar
Howard, J.A., Watson, R.D. and Hessin, T.D. (1971), Spectral reflectance properties of Pinus ponderosa in relation to copper content of the soil-Malachite mine, Jefferson County, Colorado, in Proc. 7th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, 17–21 May 1971, ERIM, Vol. 1, pp. 285297.Google Scholar
Hsieh, C.Y. (2003), Dependence of backscattering from leaves on dry-matter fraction and permittivity of saline water of leaves, Journal of Microwaves and Optoelectronics, 3(3):14.Google Scholar
Hu, B.B. and Nuss, M.C. (1995), Imaging with terahertz waves, Optics Letters, 20(16):17161718.Google Scholar
Huang, J.F. and Apan, A. (2006), Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression, Journal of Spatial Science, 51(2):129142.Google Scholar
Huang, J.F. and Blackburn, G.A. (2011), Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data, International Journal of Remote Sensing, 32(24):93759396.Google Scholar
Hubert, L., Flura, D., Saint-Jean, S., Chelle, M., Beysens, D. and Jacquemoud, S. (2009), L’eau liquide à la surface des feuilles: développement d’une méthode de mesure non intrusive de la condensation en vue d’applications à des processus biophysiques ou chimiques superficiels, Projet Innovant 2007–2008, INRA, Département Environnement et Agronomie, 18 pages.Google Scholar
Hueni, A., Nieke, J., Schopfer, J., Kneubühler, M. and Itten, K.I. (2009), The spectral database SPECCHIO for improved long-term usability and data sharing, Computers & Geosciences, 35(3):557565.Google Scholar
Hueni, A., Malthus, T., Kneubuehler, M. and Schaepman, M. (2011), Data exchange between distributed spectral databases, Computers & Geosciences, 37:861873.Google Scholar
Hui, Z. and Jianchun, Z. (2007), Near-infrared green camouflage of PET fabrics using disperse dyes, Sen’i Gakkaishi, 63(10):223229.Google Scholar
Huibers, P.D.T. (1997), Models for the wavelength dependence of the index of refraction of water, Applied Optics, 36(16):37853787.Google Scholar
Hulbary, R.L. (1944), The influence of air spaces on the three-dimensional shapes of cells in Elodea stems, and a comparison with pith cells of Ailanthus, American Journal of Botany, 31(9):561580.Google Scholar
Hunt, E.R., Rock, B.N. and Nobel, P.S. (1987), Measurement of leaf relative water content by infrared reflectance, Remote Sensing of Environment, 22(3):429435.Google Scholar
Hunt, E.R. and Rock, B.N. (1989), Detection of changes in leaf water content using near and middle-infrared reflectances, Remote Sensing of Environment, 30(1):4354.Google Scholar
Hunt, E.R. and Daughtry, C.S.T. (2014), Chlorophyll meter calibrations for chlorophyll content using measured and simulated leaf transmittances, Agronomy Journal, 106(3):931939.Google Scholar
Idso, S.B., Baker, D.G. and Gates, D.M. (1966), The energy environment of plants, Advances in Agronomy, 18:171218.Google Scholar
Idso, S.B., Jackson, R.D., Ehrler, W.L. and Mitchell, S.T. (1969), A method for determination of infrared emittance of leaves, Ecology, 50(5):899902.Google Scholar
Inada, K. (1963), Studies on a method for determining the deepness of green color and chlorophyll content of intact crop leaves and its practical applications. 1: Principal for estimating the deepness of green color and chlorophyll content of whole leaves, Japanese Journal of Crop Science, 32(2):157162.Google Scholar
Inada, K. (1965), Studies on a method for determining the deepness of green color and chlorophyll content of intact crop leaves and its practical applications. 2: Photoelectric characters of chlorophyllo-meter and correlation between the reading and chlorophyll content in leaves, Japanese Journal of Crop Science, 33 (4):301308 (in Japanese).Google Scholar
Inada, K. (1976), Action spectra for photosynthesis in higher plants, Plant & Cell Physiology, 17(2):355365.Google Scholar
Inada, K. (1980), Spectral absorption property of pigments in living leaves and its contribution to photosynthesis, Japanese Journal of Crop Science, 49(2):286294.Google Scholar
Inada, K. (1985), Spectral ratio of reflectance for estimating chlorophyll content of leaf, Japanese Journal of Crop Science, 54(3):261265.Google Scholar
Ingen-Housz, J. (1779), Experiments upon Vegetables, Discovering Their Great Power of Purifying the Common Air in the Sunshine and of Injuring It in the Shade and at Night; to Which Is Joined a New Method of Examining the Accurate Degree of Salubrity of the Atmosphere, Elmsley and Payne, London, 402 pages.Google Scholar
Inoue, Y. and Shibata, K. (1973), Light-induced chloroplasts rearrangements and their action spectra as measured by absorption spectrophotometry, Planta, 114(4):341358.Google Scholar
Inoue, Y., Kimball, B.A., Jackson, R.D., Pinter, P.J. and Reginato, R.J. (1990), Remote estimation of leaf transpiration rate and stomatal resistance based on infrared thermometry, Agricultural and Forest Meteorology, 51(1):2133.Google Scholar
Inoue, Y., Morinaga, S. and Shibayama, M. (1993), Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements, Japanese Journal of Crop Science, 62(3):462469.Google Scholar
Inoue, Y. and Peñuelas, J. (2006), Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content, International Journal of Remote Sensing, 27(22):51095114.Google Scholar
Intaravanne, Y. and Sumriddetchkajorn, S. (2015), Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer, Computers and Electronics in Agriculture, 116:228233.Google Scholar
Irvine, W.M. and Pollack, J.B. (1968), Infrared optical properties of water and ice spheres, Icarus, 8(1–3):324360.Google Scholar
Ismail, R., Mutanga, O. and Ahmed, F. (2008), Discriminating Sirex noctilio attack in pine forest plantations in South Africa using high spectral resolution data, in Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests (Kalacska, M. and Sánchez-Azofeifa, G.A., Eds), CRC Press, pp. 161175.Google Scholar
Ivanova, L.A., Petrov, M.S. and Kadushnikov, R.M. (2006), Determination of mesophyll diffusion resistance in Chamaerion angustifolium by the method of three-dimensional reconstruction of the leaf cell packing, Russian Journal of Plant Physiology, 53(3):316324.Google Scholar
Jackson, R.D. (1982), Canopy temperature and crop water stress, Advances in Irrigation, 1:4385.Google Scholar
Jackson, R.D. (1986), Remote sensing of biotic and abiotic plant stress, Annual Review of Phytopathology, 24:265287.Google Scholar
Jacquemoud, S. and Baret, F. (1990), PROSPECT: a model of leaf optical properties spectra, Remote Sensing of Environment, 34(2):7591.Google Scholar
Jacquemoud, S. (1992), Utilisation de la haute résolution spectrale pour l’étude des couverts végétaux : développement d’un modèle de réflectance spectrale, Thèse de Doctorat de Physique, Spécialité Méthodes physiques en télédétection, Université Paris Diderot, 92 pages.Google Scholar
Jacquemoud, S. (1993), Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equivalent spectra: theoretical study, Remote Sensing of Environment, 44(2–3):281292.Google Scholar
Jacquemoud, S. (2004), Leaf optical properties, in Reflection Properties of Vegetation and Soil – with a BRDF Data Base (von Schönermark, M., Geiger, B. and Röser, H.P., Eds), Wissenschaft & Technik Verlag, Berlin, Germany, pp. 5670.Google Scholar
Jacquemoud, S., Baret, F., Andrieu, B., Danson, F.M. and Jaggard, K. (1995a), Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL model on sugar beet canopy reflectance data – Application to TM and AVIRIS sensors, Remote Sensing of Environment, 52(3):163172.Google Scholar
Jacquemoud, S., Verdebout, J., Schmuck, G., Andreoli, G. and Hosgood, B. (1995b), Investigation of leaf biochemistry by statistics, Remote Sensing of Environment, 54(3):180188.Google Scholar
Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G. and Hosgood, B. (1996), Estimating leaf biochemistry using the PROSPECT leaf optical properties model, Remote Sensing of Environment, 56(3):194202.Google Scholar
Jacquemoud, S., Frangi, J.P., Govaerts, Y. and Ustin, S.L. (1997), Three-dimensional representation of leaf anatomy – Application to the study of photon transport, in Proc. 7th International Symposium on Physical Measurements and Signatures in Remote Sensing (Guyot G. and Phulpin T., Eds), Courchevel, France, 7–11 April 1997, Balkema, pp. 295302.Google Scholar
Jacquemoud, S., Bacour, C., Poilvé, H. and Frangi, J.P. (2000), Comparison of four radiative transfer models to simulate plant canopies reflectance – Direct and inverse mode, Remote Sensing of Environment, 74(3):471481.Google Scholar
Jacquemoud, S. (2004), Leaf optical properties, in Reflection Properties of Vegetation and Soil – with a BRDF Data Base (von Schönermark, M., Geiger, B. and Röser, H.P., Eds), Wissenschaft & Technik Verlag, Berlin, Germany, pp. 5670.Google Scholar
Jacquemoud, S., Féret, J.B. and Ustin, S.L. (2009a), Compréhension et modélisation de la couleur des feuilles, in Proc. Ecole thématique interdisciplinaire du CNRS – Couleur, question d’échelle : l’espace, Roussillon en Provence, France, 23–27 mars 2009, pp. 181190.Google Scholar
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., et al. (2009b), PROSPECT + SAIL Models: a review of use for vegetation characterization, Remote Sensing of Environment, 113(1):S56S66.Google Scholar
Jay, S., Bendoula, R., Hadoux, X. and Gorretta, N. (2015), Mapping of foliar content using radiative transfer modeling and VIS-NIR hyperspectral close-range remote sensing, in Proc. ISPRS Geospatial Week 2015, La Grande Motte, France, 28 September–2 October 2015, ISPRS, 6 pages.Google Scholar
Jay, S., Bendoula, R., Hadoux, X., Féret, J.B. and Gorretta, N. (2016), A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy, Remote Sensing of Environment, 177:220236.Google Scholar
Jeje, A. and Zimmermann, M. (1983), The anisotropy of the mesophyll and CO2 capture sites in Vicia faba L. leaves at low light intensities, Journal of Experimental Botany, 34(12):16761694.Google Scholar
Jenkins, M.W., Krofcheck, D.J., Teasdale, R., Houpis, J. and Pushnik, J. (2012), Exploring the edge of a natural disaster, Open Journal of Ecology, 2(4):222232.Google Scholar
Jensen, H.W., Marschner, S.R., Levoy, M. and Hanrahan, P. (2001), A practical model for subsurface light transport, in Proc. 28th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, 12–17 August 2001, ACM, pp. 511518.Google Scholar
Jensen, H.W. and Buhler, J. (2002), A rapid hierarchical rendering technique for translucent materials, ACM Transactions on Graphics, 21(3):576581.Google Scholar
Jernshøj, K.D. and Hassing, S. (2009), Analysis of reflectance and transmittance measurements on absorbing and scattering small samples using a modified integrating sphere setup, Applied Spectroscopy, 63(8):879888.Google Scholar
Ji, L. and Peters, A.J. (2007), Performance evaluation of spectral vegetation indices using a statistical sensitivity function, Remote Sensing of Environment, 106(1):5965.Google Scholar
Jiang, H., Ying, Y. and Bao, Y. (2005a), Study on the water content measurement of tomatoes by near infrared technique, in Proc. Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality (Chen Y.R., Meyer G.E. and Tu S.I., Eds), Boston, MA, 23–24 October 2005, SPIE, Vol. 5996, 599612.Google Scholar
Jiang, H., Bao, Y. and Ying, Y. (2005b), Measure the chlorophyll content in leaves by near infrared analysis, in Proc. Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality (Chen Y.R., Meyer G.E. and Tu S.I., Eds), Boston, MA, 23–24 October 2005, SPIE, Vol. 5996, 599617.Google Scholar
Jiang, H. and Lu, Y.Y.H. (2006), Near-infrared diffuse reflection systems for chlorophyll content of tomato leaves measurement, in Proc. Optics for Natural Resources, Agriculture, and Foods (Chen Y.R., Meyer G.E. and Tu S.I., Eds), Boston, MA, SPIE, Vol. 6381, 638112.Google Scholar
Jin, J., Jiang, H., Zhang, X., Wang, Y., Cheng, M. and Song, X. (2013), Using multivariate analysis to detect the hyperspectral response of Chinese fir to acid stress, International Journal of Remote Sensing, 34(11):37753786.Google Scholar
Joffre, R., Gillon, D., Dardenne, P. and Agneessens, R. (1992), The use of near-infrared reflectance spectroscopy in litter decomposition studies, Annales des Sciences Forestières, 49(5):481488.Google Scholar
Johnson, H.B. (1975), Plant pubescence: an ecological perspective, The Botanical Review, 41(3):233258.Google Scholar
Johnson, L.F. (2001), Nitrogen influence on fresh-leaf NIR spectra, Remote Sensing of Environment, 78(3):314320.Google Scholar
Jolivet, P. (2010), Vert, jaune, blanc ou rouge … Pourquoi les feuilles changent-elles de couleur? Incidence sur les insectes, L’ Entomologiste, 66(3):177188.Google Scholar
Jones, C.D., Jones, J.B., and Lee, W.S. (2010), Diagnosis of bacterial spot of tomato using spectral signatures, Computers and Electronics in Agriculture, 74(2):329335.Google Scholar
Jones, H.G. (1999), Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces, Plant, Cell & Environment, 22(9):10431055.Google Scholar
Jones, H.G. (2010), Can water droplets on leaves cause leaf scorch? New Phytologist, 185(4):865867.Google Scholar
Jones, H.G. and Vaughan, R.A. (2010), Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press, Oxford, 353 pages.Google Scholar
Jordan, G.J., Dillon, R.A. and Weston, P.H. (2005), Solar radiation as a factor in the evolution of scleromorphic leaf anatomy in Proteaceae, American Journal of Botany, 92(5):789796.Google Scholar
Jördens, C., Scheller, M., Breitenstein, B., Selmar, D. and Koch, M. (2009), Evaluation of leaf water status by means of permittivity at terahertz frequencies, Journal of Biological Physics, 35(3):255264.Google Scholar
Jørgensen, R.N., Christensen, L.K. and Bro, R. (2007), Spectral reflectance at sub-leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression, International Journal of Remote Sensing, 28(5):943962.Google Scholar
Jovanic, B.R. and Jovanic, S.B. (2001), The effect of high concentrations of negative ions in the air on the chlorophyll contents in plant leaves, Water, Air, and Soil Pollution, 129(1–4):259265.Google Scholar
Ju, C.H., Tian, Y.C., Yao, X., Cao, W.X., Zhu, Y. and Hannaway, D. (2010), Estimating leaf chlorophyll content using red edge parameters, Pedosphere, 20(5):633644.Google Scholar
Judkins, W.P. and Wander, I.W. (1950), Correlation between leaf color, leaf nitrogen content, and growth of apple, peach, and grape plants, Plant Physiology, 25(1):7885.Google Scholar
Juniper, B.E. and Jeffree, C.E. (1983), Plant Surfaces, Hodder Arnold, London, 93 pages.Google Scholar
Jusoff, K., Yusoff, M.M. and Mohd Ali, N.H. (2010), Spectral signatures of leaf fall diseases in Hevea brasiliensis using a handheld spectroradiometer, Modern Applied Science, 4(2):7884.Google Scholar
Justice, C., Belward, A., Morisette, J., Lewis, P., Privette, J. and Baret, F. (2000), Developments in the “validation” of satellite sensor products for the study of the land surface, International Journal of Remote Sensing, 21(17):33833390.Google Scholar
Kaasalainen, S. and Rautiainen, M. (2007), Backscattering measurements from individual Scots pine needles, Applied Optics, 46(22):49164922.Google Scholar
Kaiser, J., Reale, L., Ritucci, A., Tomassetti, G., Poma, A., Spanò, L., et al. (2005), Mapping of the metal intake in plants by large-field X-ray microradiography and preliminary feasibility studies in microtomography, The European Physical Journal D, 32(1):113118.Google Scholar
Kaiser, J., Samek, O., Reale, L., Liška, M., Malina, R., Ritucci, A., et al. (2007), Monitoring of the heavy-metal hyperaccumulation in vegetal tissues by X-ray radiography and by femto-second laser induced breakdown spectroscopy, Microscopy Research and Technique, 70(2):147153.Google Scholar
Kakani, V.G., Reddy, K.R., Zhao, D. and Gao, W. (2004), Senescence and hyperspectral reflectance of cotton leaves exposed to ultraviolet-B radiation and carbon dioxide, Physiologia Plantarum, 121(2):250257.Google Scholar
Kakani, V.G., Reddy, K.R. and Zhao, D. (2007), Deriving a simple spectral reflectance ratio to determine cotton leaf water potential, Journal of New Seeds, 8(3):1127.Google Scholar
Kallel, A. (2018), Leaf polarized BRDF simulation based on Monte Carlo 3-D vector RT modeling, Journal of Quantitative Spectroscopy and Radiative Transfer, 221: 202224.Google Scholar
Kalacska, M., Bohlman, S., Sánchez-Azofeifa, G.A., Castro-Esau, K. and Caellic, T. (2007), Hyperspectral discrimination of tropical dry forest lianas and trees: comparative data reduction approaches at the leaf and canopy levels, Remote Sensing of Environment, 109(4):406415.Google Scholar
Karabourniotis, G., Papadopoulos, K., Papamarkou, M. and Manetas, Y. (1992), Ultraviolet-B radiation absorbing capacity of leaf hairs, Physiologia Plantarum, 86(3):414418.Google Scholar
Karabourniotis, G., Kyparissis, A. and Manetas, Y. (1993), Leaf hairs of Olea europaea L. protect underlying tissues against ultraviolet-B radiation damage, Environmental and Experimental Botany, 33:341345.Google Scholar
Karabourniotis, G., Papastergiou, N., Kabanopoulou, E. and Fasseas, C. (1994), Foliar sclereids of Olea europaea may function as optical fibres, Canadian Journal of Botany, 72(3):330336.Google Scholar
Karabourniotis, G., Kotsabassidis, D. and Manetas, Y. (1995), Trichome density and its protective potential against ultraviolet-B radiation damage during leaf development, Canadian Journal of Botany, 73(3):376383.Google Scholar
Karabourniotis, G. (1998), Light-guiding function of foliar sclereids in the evergreen sclerophyll Phillyrea latifolia: a quantitative approach, Journal of Experimental Botany, 49(321):739746.Google Scholar
Karabourniotis, G. and Bornman, J.F. (1999), Penetration of UV-A, UV-B and blue light through the leaf trichome layers of two xeromorphic plants, olive and oak, measured by optical fibre microprobes, Physiologia Plantarum, 105(4):655661.Google Scholar
Karabourniotis, G., Bornman, J.F. and Liakoura, V. (1999a), Different leaf surface characteristics of three grape cultivars affect leaf optical properties as measured with fibre optics: possible implication in stress tolerance, Australian Journal of Plant Physiology, 26(1):4753.Google Scholar
Karabourniotis, G., Bornman, J.F. and Nikolopoulos, D. (2000), A possible optical role of the bundle sheath extensions of the heterobaric leaves of Vitis vinifera and Quercus coccifera, Plant, Cell & Environment, 23(4):423430.Google Scholar
Karageorgou, P., Buschmann, C. and Manetas, Y. (2008), Red leaf color as a warning signal against insect herbivory: honest or mimetic? Flora, 203(8):648652.Google Scholar
Karam, M.A., Fung, A.K., Blanchard, A.J. and Shen, G.X. (1988), The leaf-shape effect on electromagnetic scattering from vegetated media, in Proc. IEEE Geoscience and Remote Sensing Symposium (IGARSS’88), Edinburgh, Scotland, 12–16 September 1988 IEEE, Vol. 2, pp. 677680.Google Scholar
Karam, M.A. and Fung, A.K. (1989), Leaf-shape effects in electromagnetic wave scattering from vegetation, IEEE Transactions on Geoscience and Remote Sensing, 27(6):687697.Google Scholar
Karvaly, B. (1970), Investigations on the connections between the bulk absorption and diffuse reflectance spectra of powdered solids, Acta Physica Academiae Scientiarum Hungaricae, 28(4):381399.Google Scholar
Katterfeld, G.N. and Suslov, A.K. (1969), Bibliography on problems of Astrobiology, NASA, 1 April 1969, 56 pages (Translation of: “Literatura po Problemam Astrobiologii” Subcommission on Astrobiology and Planetary Physics, Commission on Planetology, Geographic Society of the USSR, Leningrad, 1967, 76 pp.).Google Scholar
Kattge, J. et al. (2011), TRY – a global database of plant traits, Global Change Biology, 17(9):29052935.Google Scholar
Kattge, J., Díaz, S., Lavorel, S., Prentice, I.C., Leadley, P., Boenisch, G., et al. and The TRY Consortium (2015), TRY 3.0 – a substantial upgrade of the global database of plant traits: more data, more species, largely open access, Geophysical Research Abstracts, 17, EGU2015-15745–2.Google Scholar
Kaufmann, W.F. and Hartmann, K.M. (1988), Internal brightness of disk-shaped samples, Journal of Photochemistry and Photobiology B: Biology, 1(3):337360.Google Scholar
Kautsky, H. and Hirsch, A. (1931), Neue Versuche zur Kohlensäureassimilation, Die Naturwissenschaften, 19(48):964964.Google Scholar
Kawashima, S. and Nakatani, M. (1998), An algorithm for estimating chlorophyll content in leaves using a video camera, Annals of Botany, 81(1):4954.Google Scholar
Kazarinova-Fukshansky, N., Seyfried, M. and Schafer, E. (1985), Distortion of action spectra in photomorphogenesis by light gradients within the plant tissue, Photochemistry and Photobiology, 41(6):689702.Google Scholar
Keegan, H.J., Schleter, J.C., Hall, W.A. and Haas, G.M. (1956), Spectrophotometric and colorimetric study of diseased and rust resisting cereal crops, National Bureau of Standards, July 1956, NBS Report 4591, 128 pages. https://archive.org/details/spectrophotometr4591keeg/page/n145Google Scholar
Keith, L., Roy, A., Morrison, R. and Schade, P. (2011), Leonardo da Vinci’s “Virgin of the Rocks”: treatment, technique and display, National Gallery Technical Bulletin, 32:3256.Google Scholar
Kelber, A. (1999a), Ovipositing butterflies use a red receptor to see green, Journal of Experimental Biology, 202(19):26192630.Google Scholar
Kelber, A. (1999b), Why “false” colours are seen by butterflies, Nature, 402(6759):251251.Google Scholar
Kempeneers, P., De Backer, S., Debruyn, W., Coppin, P. and Scheunders, P. (2005), Generic wavelet-based hyperspectral classification applied to vegetation stress detection, IEEE Transactions on Geoscience and Remote Sensing, 43(3):610614.Google Scholar
Kendal, D., Hauser, C.E., Garrard, G.E., Jellinek, S., Giljohann, K.M. and Moore, J.L. (2013), Quantifying plant colour and colour difference as perceived by humans using digital images, PLOS One, 8(8):e72296.Google Scholar
Kerstiens, G., Ed (1996), Plant Cuticles: An Integrated Functional Approach, BIOS Scientific Publishers Limited, Oxford, UK, 337 pages.Google Scholar
Kestner, J.M., Leidecker, H.W., Irons, J.R., Smith, J.A., Brakke, T.W. and Horning, N.A. (1988), Goniometric observations of light scattered from soils and leaves, Journal of Wave-Material Interaction, 3(2):189198.Google Scholar
Khamis, S., Lamaze, T., Lemoine, Y. and Foyer, C. (1990), Adaptation of the photosynthetic apparatus in maize leaves as a result of nitrogen limitation: relationships between electron transport and carbon assimilation, Plant Physiology, 94(3):14361443.Google Scholar
Khanchi, A.R., Mahani, M.K., Hajihosseini, M., Maragheh, M.G., Chaloosi, M. and Bani, F. (2007), Simultaneous spectrophotometric determination of caffeine and theobromine in Iranian tea by artificial neural networks and its comparison with PLS, Food Chemistry, 103(3):10621068.Google Scholar
Khanna, S., Santos, M.J., Ustin, S.L. and Haverkamp, P.J. (2011), An integrated approach to a biophysiologically based classification of floating aquatic macrophytes, International Journal of Remote Sensing, 32(4):10671094.Google Scholar
Kharuk, V.I. and Yegorov, V.V. (1990), Polarimetric indication of plant stress, Remote Sensing of Environment, 33(1):3540.Google Scholar
Khavanin Zadeh, A.R., Veroustraete, F., Wuyts, K., Kardel, F. and Samson, R. (2012), Dorsi-ventral leaf reflectance properties of Carpinus betulus L.: an indicator of urban habitat quality, Environmental Pollution, 162:332337.Google Scholar
Khavanin Zadeh, A.R., Veroustraete, F., Buytaert, J.A.N., Dirckx, J. and Samson, R. (2013), Assessing urban habitat quality using spectral characteristics of Tilia leaves, Environmental Pollution, 178:714.Google Scholar
Khavanin Zadeh, A.R., Veroustraete, F., Buytaert, J.A.N. and Samson, R. (2014), Leaf injury symptoms of Tilia sp. as an indicator of urban habitat quality, Ecological Indicators, 41:5864.Google Scholar
Kiang, N.Y., Siefert, J., Govindjee, and Blankenship, R.E. (2007a), Spectral signatures of photosynthesis. I: review of Earth organisms, Astrobiology, 7(1):222251.Google Scholar
Kiang, N.Y., Segura, A., Tinetti, G., Govindjee, G., Blankenship, R.E., Cohen, M., et al. (2007b), Spectral signatures of photosynthesis. II: coevolution with other stars and the atmosphere on extrasolar worlds, Astrobiology, 7(1):252274.Google Scholar
Kiang, N.Y. (2008), The color of plants on other worlds, Scientific American, April 2008, pp. 4855.Google Scholar
Kim, D.M., Zhang, H., Zhou, H., Du, T., Wu, Q., Mockler, T.C. et al. (2015), Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis, Scientific Reports, 5:15919.Google Scholar
Kim, E., Lin, Y., Kerney, R., Blumenberg, L. and Bishop, C. (2014), Phylogenetic analysis of algal symbionts associated with four North American amphibian egg masses, PLOS One, 9(11):e108915.Google Scholar
Kim, M.S., McMurtrey, J.E., Mulchi, C.L., Daughtry, C.S.T., Chappelle, E.W. and Chen, Y.R. (2001), Steady-state multispectral fluorescence imaging system for plant leaves, Applied Optics, 40(1):157166.Google Scholar
Kira, O., Linker, R. and Gitelson, A. (2015), Non-destructive estimation of foliar chlorophyll and carotenoid contents: focus on informative spectral bands, International Journal of Applied Earth Observation and Geoinformation, 38:251260.Google Scholar
Klančnik, K., Mlinar, M. and Gaberščik, A. (2012), Heterophylly results in a variety of “spectral signatures” in aquatic plant species, Aquatic Botany, 98(1):2026.Google Scholar
Klančnik, K., Vogel-Mikuš, K. and Gaberščik, A. (2014a), Silicified structures affect leaf optical properties in grasses and sedge, Journal of Photochemistry and Photobiology B: Biology, 130:110.Google Scholar
Klančnik, K., Pančić, M. and Gaberščik, A. (2014b), Leaf optical properties in amphibious plant species are affected by multiple leaf traits, Hydrobiologia, 737(1):121130.Google Scholar
Klančnik, K., Zelnik, I., Gnezda, P. and Gaberščik, A. (2015), Do reflectance spectra of different plant stands in wetland indicate species properties? in The Role of Natural and Constructed Wetlands in Nutrient Cycling and Retention on the Landscape (Vymazal, J., Ed), Springer, pp. 7386.Google Scholar
Kleyer, M., Bekker, R.M., Knevel, I.C., Bakker, J.P., Thompson, K. et al. (2008), The LEDA traitbase: a database of life-history traits of the Northwest European flora, Journal of Ecology, 96(6):12661274.Google Scholar
Knapp, A.K., Vogelmann, T.C., McClean, T.M. and Smith, W.K. (1988), Light and chlorophyll gradients within Cucurbita cotyledons, Plant, Cell & Environment, 11(4):257263.Google Scholar
Knapp, A.K. and Carter, G.A. (1998), Variability in leaf optical properties among 26 species from a broad range of habitats, American Journal of Botany, 85(7):940946.Google Scholar
Knipling, E.B. (1970), Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation, Remote Sensing of Environment, 1(3):155159.Google Scholar
Knoerr, K.R. and Gay, L.W. (1965), Tree leaf energy balance, Ecology, 46(1–2):1724.Google Scholar
Knudson, L.L., Tibbitts, T.W. and Edwards, G.E. (1977), Measurement of ozone injury by determination of leaf chlorophyll concentration, Plant Physiology, 60(4):606608.Google Scholar
Knyazikhin, Y., Schull, M.A., Yang, Y., Stenberg, P., Mõttus, M., Rautiainen, M., et al. (2012), Hyperspectral remote sensing of foliar nitrogen content, Proceedings of the National Academy of Sciences, 110(3):E185E192.Google Scholar
Kobayashi, S., Rokugawa, S., Yamagata, Y. and Oguma, H. (2002), A study on predicting biochemical contents of larch needles – A study on LIBERTY model, Journal of the Remote Sensing Society of Japan, 22 (5):571587 (in Japanese).Google Scholar
Kobayashi, T., Kanda, E., Naito, S., Nakajima, T., Arakawa, I., Nemoto, K., et al. (2003), Ratio of rice reflectance for estimating leaf blast severity with a multispectral radiometer, Journal of General Plant Pathology, 69(1):1722.Google Scholar
Koch, K. and Barthlott, W. (2009), Superhydrophobic and superhydrophilic plant surfaces: an inspiration for biomimetic materials, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1893):14871509.Google Scholar
Kochubey, S.M., Kobets, N.I. and Shadchina, T.M. (1987), The shape of reflectance spectra of leaves as informative basis of the remote sensing of crop states, Physiology and Biochemistry of Cultivated Plants, 19(6):539545 (in Russian).Google Scholar
Kochubey, S.M., Kobets, N.I. and Shadchina, T.M. (1988), Quantitative analysis of the spectral distribution curve shape for reflection of plant leaves as a method for testing their state, Physiology and Biochemistry of Cultivated Plants, 20(6):535540 (in Russian).Google Scholar
Kochubey, S.M. and Kazantsev, T.A. (2007), Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content, Journal of Plant Physiology, 164(12):16481655.Google Scholar
Kochubey, S.M. and Kazantsev, T.A. (2012), Derivative vegetation indices as a new approach in remote sensing of vegetation, Frontiers of Earth Science, 6(2):188195.Google Scholar
Koetz, B., Schaepman, M., Morsdorf, F., Bowyer, P., Itten, K. and Allgöwer, B. (2004), Radiative transfer modeling within heterogeneous canopy for estimation of forest fire fuel properties, Remote Sensing of Environment, 92(3):332344.Google Scholar
Koizumi, M., Takahashi, K., Mineuchi, K., Nakamura, T. and Kano, H. (1998), Light gradients and the transverse distribution of chlorophyll fluorescence in mangrove and Camellia leaves, Annals of Botany, 81(4):527533.Google Scholar
Kokalj, M., Kolar, J., Trafela, T. and Kreft, S. (2011), Differences among Epilobium and Hypericum species revealed by four IR spectroscopy modes: transmission, KBr tablet, diffuse reflectance and ATR, Phytochemical Analysis, 22(6):541546.Google Scholar
Kokaly, R.F. and Clark, R.N. (1999), Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression, Remote Sensing of Environment, 67(3):267287.Google Scholar
Kokaly, R.F. (2001), Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration, Remote Sensing of Environment, 75(2):153161.Google Scholar
Kokaly, R.F. and Skidmore, A.K. (2015), Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm, International Journal of Applied Earth Observation and Geoinformation, 43:5583.Google Scholar
Kolattukudy, P.E. (1976), Chemistry and Biochemistry of Natural Waxes, Elsevier, Amsterdam, 459 pages.Google Scholar
Kolb, C.A. and Pfündel, E.E. (2005), Origins of non-linear and dissimilar relationships between epidermal UV absorbance and UV absorbance of extracted phenolics in leaves of grapevine and barley, Plant, Cell & Environment, 28(5):580590.Google Scholar
Kondo, A., Kaikawa, J., Funaguma, T. and Ueno, O. (2004), Clumping and dispersal of chloroplasts in succulent plants, Planta, 219(3):500506.Google Scholar
Korn, R.W. (1974), The three-dimensional shape of plant cells and its relationship to pattern of tissue growth, New Phytologist, 73(5):927935.Google Scholar
Körner, C. (2003), Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems, Springer Verlag, Berlin, 344 pages.Google Scholar
Kortüm, G. (1969), Reflectance Spectroscopy, Springer Verlag, Berlin, 365 pages.Google Scholar
Kováč, D., Navrátil, M., Malenovský, Z., Štroch, M., Špunda, V. and Urban, O. (2012), Reflectance continuum removal spectral index tracking the xanthophyll cycle photoprotective reactions in Norway spruce needles, Functional Plant Biology, 39(12):987998.Google Scholar
Kováč, D., Malenovský, Z., Urban, O., Špunda, V., Kalina, J., , A., et al. (2013), Response of green reflectance continuum removal index to the xanthophyll de-epoxidation cycle in Norway spruce needles, Journal of Experimental Botany, 64(7):18171827.Google Scholar
Kozlova, K.I. (1957), Experiment of spectrophotometric investigation of the reflection by plants of close ultraviolet rays, Trudy sektora astrobotaniki, 5: 110117 (in Russian).Google Scholar
Kraft, M., Weigel, H.J., Mejer, G.J. and Brandes, F. (1996), Reflectance measurements of leaves for detecting visible and non-visible ozone damage to crops, Journal of Plant Physiology, 148(1–2):148154.Google Scholar
Krauss, P., Markstädter, C. and Riederer, M. (1997), Attenuation of UV radiation by plant cuticles from woody species, Plant, Cell & Environment, 20(8):10791085.Google Scholar
Krekov, G.M., Krekova, M.M., Lisenko, A.A. and Sukhanov, A.Y. (2009), Radiative characteristics of plant leaf, Atmospheric and Oceanic Optics, 22(2):241256 (original text published in Russian in Optika Atmosfery i Okeana, 22(4):397410).Google Scholar
Krezhova, D.D., Yanev, T.K., Alexieva, V.S. and Ivanov, C.V. (2005), Early detection of changes in leaf reflectance of pea plants (Pisum sativum L.) under herbicide action in Proc. 2nd International Conference on Recent Advances in Space Technologies (RAST’05), Istanbul, Turkey, 9–11 June 2005, IEEE, pp. 636641.Google Scholar
Krezhova, D.D. and Yanev, T.K. (2007), Use of a remote sensing method to estimate the influence of anthropogenic factors on the spectral reflectance of plant species, in Proc. 6th International Conference of the Balkan Physical Union (Cetin S.A. and Hikmet I., Eds), Istanbul, Turkey, 22–26 August 2006, American Institute of Physics, Vol. 899, pp. 738738.Google Scholar
Krezhova, D.D., Alexieva, V.S., Yanev, T.K. and Ivanov, C.V. (2007a), Remote sensing study of the influence of herbicides Fluridone and Acifluorfen on the spectral reflectance of pea plant leaves (Pisum sativum L.), in Proc. 3rd International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 14–16 June 2007, IEEE, pp. 326330.Google Scholar
Krezhova, D.D. and Yanev, T.K., Ivanov, C.V. and Alexieva, V.S. (2007b), Remote sensing of the effect of the herbicide glyphosate on the leaf spectral reflectance of pea plants (Pisum sativum L.), in New Developments and Challenges in Remote Sensing (Bochenek, Z., Ed.), Millpress, pp. 4552.Google Scholar
Krezhova, D.D., Iliev, I.T., Yanev, Т.K. and Kirova, E.B. (2009a), Assessment of the effect of salinity on the early growth stage of soybean plants (Glycine max L.), in Proc. 4th International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 11–13 June 2009, IEEE, pp. 397402.Google Scholar
Krezhova, D.D., Kirova, E.B., Yanev, Т.K. and Iliev, I.T. (2009b), Effects of salinity on leaf spectral reflectance and biochemical parameters of nitrogen fixing soybean plants (Glycine max L.), in Proc. 7th International Conference of the Balkan Physical Union (Angelopoulos A. and Fildisis T., Eds), Alexandroupolis, Greece, 9–13 September 2009, American Institute of Physics, Vol. 1203, pp. 694696.Google Scholar
Krezhova, D.D., Hristova, D.P. and Yanev, T.K. (2010), Spectral remote sensing of tomato plants (Lycopersicon esculentum L.) infected with tomato mosaic virus (ToMV), in Proc. Remote Sensing for Science, Education, and Natural and Cultural Heritage (Reuter R., Ed), Paris, France, 31 May 2010–3 June 2010, pp. 715722.Google Scholar
Krezhova, D.D. (2011), Spectral remote sensing of the responses of soybean plants to environmental stresses, in Soybean – Genetics and Novel Techniques for Yield Enhancement (Krezhova, D.D., Ed), InTech, pp. 216256.Google Scholar
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., et al. (2005), A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19: GB1015.Google Scholar
Krishnan, S. and Nordine, P.C. (1994), Mueller-matrix ellipsometry using the division-of-amplitude photopolarimeter: a study of depolarization effects, Applied Optics, 33(19):41844192.Google Scholar
Kruckeberg, A.R. (1951), Intraspecific variability in the response of certain native plant species to serpentine soil, American Journal of Botany, 38(6):408419.Google Scholar
Krulik, G.A. (1980), Light transmission in window-leaved plants, Canadian Journal of Botany, 58(14):15911600.Google Scholar
Kubelka, P. and Munk, F. (1931), Ein beitrag zur optik der farbanstriche, Zeitschrift fur Technische Physik, 12:593601.Google Scholar
Kubinova, L., Janacek, J., Albrechtova, J. and Karen, P. (2004), Stereological and digital methods for estimating geometrical characteristics of biological structures using confocal microscopy, in Proc. NATO Advanced Study Institute on From Cells to Proteins: Imaging Nature across Dimensions (Evangelista V., Barsanti L., Passarelli V. and Gualtieri P., Eds), Pisa, Italy, 12–23 September 2004, Springer, Vol. 3/2006, pp. 271321.Google Scholar
Kudo, M., Watt, R.A. and Moffat, A.C. (2000), Rapid identification of Digitalis purpurea using near-infrared reflectance spectroscopy, Journal of Pharmacy and Pharmacology, 52(10):12711277.Google Scholar
Kumar, L. (2007a), A comparison of reflectance characteristics of some Australian Eucalyptus species based on high spectral resolution data – Discriminating using the visible and NIR regions, Journal of Spatial Science, 52(2):5164.Google Scholar
Kumar, L. (2007b), High-spectral resolution data for determining leaf water content in Eucalyptus species: leaf level experiments, Geocarto International, 22(1):316.Google Scholar
Kumar, L., Skidmore, A.K. and Mutanga, O. (2010), Leaf level experiments to discriminate between eucalyptus species using high spectral resolution reflectance data: use of derivatives, ratios and vegetation indices, Geocarto International, 25(4):327344.Google Scholar
Kumar, R. and Silva, L. (1973), Light ray tracing through a leaf cross-section, Applied Optics, 12(12):29502954.Google Scholar
Kümmerlen, B., Dauwe, S., Schmundt, D. and Schurr, U. (1999), Thermography to measure relations of plant leaves, in Handbook of Computer Vision and Applications (Jahne, B., Haussecker, H. and Geissler, P., Eds), Academic Press, pp. 763781.Google Scholar
Kupková, L., Potůčková, M., Zachová, K., Lhotáková, Z., Kopačková, V. and Albrechtová, J. (2012), Chlorophyll determination in silver birch and scots pine foliage from heavy metal polluted regions using spectral reflectance data, EARSeL eProceedings, 11(1):6473.Google Scholar
Kursar, T.A. and Coley, P.D. (1992), Delayed greening in tropical leaves: an anti-herbivore defense? Biotropica, 24(2):256262.Google Scholar
Kutschera, U. (2008), The growing outer epidermal wall: design and physiological role of a composite structure, Annals of Botany, 101(5):615621.Google Scholar
Kutyreva, A.P., Intykbayeva, B.B. and Kuatova, Z. (1960), Characteristics of the optical properties of alpine plants of eastern Pamir, Trudy sektora astrobotaniki, 8:65121 (English translation).Google Scholar
Kuusk, A. (1994), A multispectral canopy reflectance model, Remote Sensing of Environment, 50(2):7582.Google Scholar
Kuusk, A. (1995a), A fast, invertible canopy reflectance model, Remote Sensing of Environment, 51(3):342350.Google Scholar
Kuusk, A. (1995b), A Markov chain model of canopy reflectance, Agricultural and Forest Meteorology, 76(3–4):221236.Google Scholar
Kwan, A., Dudley, J. and Lantz, E. (2002), Who really discovered Snell’s law? Physics World, April 2002, pp. 6464.Google Scholar
Kwolek, W.F. (1982), Variations in leaf coloration using a reflectance colorimeter, Journal of Arboriculture, 8(6):157159.Google Scholar
Labovitz, M.L., Masuoka, E.J., Bell, R., Siegrist, A.W. and Nelson, R.F. (1983), The application of remote sensing to geobotanical exploration for metal sulfides – Results from the 1980 field season at Mineral, Virginia, Economic Geology, 78(4):750760.Google Scholar
Lacaze, B. and Joffre, R. (1994), Extracting biochemical information from visible and near infrared reflectance spectroscopy of fresh and dried leaves, Journal of Plant Physiology, 144(3):277281.Google Scholar
Lambers, H., Chapin, F.S. and Pons, T.L. (2008), Plant Physiological Ecology, Springer, 605 pages.Google Scholar
Landrin, A. (1864), Notice historique et analytique sur les travaux relatifs a la coloration des végétaux. Rédigée sur la demande du Comité des Sciences appliquées de la Société d’Horticulture de Seine-et-Oise, Imprimerie de E. Aubert, Versailles, France, 15 pages.Google Scholar
Lao, C., Li, B., Guo, Y. and Yan, T. (2005), Design and evaluation of a new device for measuring scattering light distribution of leaf, Transactions of the Chinese Society of Agricultural Engineering, 21 (9):8589 (in Chinese).Google Scholar
Lao, C., Guo, Y. and Li, B. (2006), Parameterization of bidirectional reflection from maize leaves with measurements in the principal plane, in Proc. 2nd International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Beijing, China, 13–17 November 2006, pp. 109115.Google Scholar
Lao, C., Guo, Y. and Li, B. (2009), Simulating the distribution of R/FR in maize canopies with Monte Carlo ray tracing approach, in Proc. 3rd International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Beijing, China, 9–13 November 2009, IEEE, pp. 6571.Google Scholar
Lass, L.W., Prather, T.S., Glenn, N.F., Weber, K.T., Mundt, J.T. and Pettingill, J. (2005), A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor, Weed Science, 53(2):242251.Google Scholar
Lautenschlager-Fleury, D. (1955), Über die Ultraviolettdurchlässigkeit von Blattepidermen, Berichte der Schweizerischen Botanischen Gesellschaft, 65:343386.Google Scholar
Lawrence, R.L., Wood, S.D. and Sheley, R.L. (2006), Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest), Remote Sensing of Environment, 100(3):356362.Google Scholar
Leadbetter, J. (2005), Environmental Microbiology, Academic Press, Amsterdam, 568 pages.Google Scholar
Leaf Architecture Working Group (1979), Manual of leaf architecture: morphological description and categorization of dicotyledonous and net‐veined monocotyledonous angiosperms, Smithsonian Institution, Washington, DC, 65 pages.Google Scholar
Lecointre, G., Le Guyader, H., Visset, D. and McCoy, K. (2007), The Tree of Life: A Phylogenetic Classification, Harvard University Press, Cambridge, MA, 560 pages.Google Scholar
Lee, D.W. and Lowry, J.B. (1975), Physical basis and ecological significance of iridescence in blue plants, Nature, 254(5495):5051.Google Scholar
Lee, D.W. and Lowry, J.B. (1980), Young-leaf anthocyanin and solar ultraviolet, Biotropica, 12(1):7576.Google Scholar
Lee, D.W. (1977), On iridescent plants, The Gardens’ Bulletin Singapore, 30:2129.Google Scholar
Lee, D.W. (1986), Unusual strategies of light absorption in rain-forest herbs, in On the Economy of Plant Form and Function (Givnish, T.J., Ed), Cambridge University Press, New York, pp. 105131.Google Scholar
Givnish, T.J. (1997), Iridescent blue plants, American Scientist, 85(1):5658.Google Scholar
Givnish, T.J. (2007), Nature’s Palette. The science of Plant Color, The University of Chicago Press, 409 pages.Google Scholar
Givnish, T.J. (2011), Biomimicry of the ultimate optical device – The plant, in Biomimetics: Nature-Based Innovation (Bar-Cohen, Y., Ed), CRC Press, Boca Raton, pp. 307330.Google Scholar
Lee, D.W. and Graham, R. (1986), Leaf optical properties of rainforest sun and extreme shade plants, American Journal of Botany, 73(8):11001108.Google Scholar
Lee, D.W., Bone, R.A., Tarsis, S.L. and Storch, D. (1990), Correlates of leaf optical properties in tropical forest sun and extreme-shade plants, American Journal of Botany, 77(3):370380.Google Scholar
Lee, S.Y., Jeong, H.J., Kang, N.S., Jang, T.Y., Jang, S.H. and Lajeunesse, T.C. (2015), Symbiodinium tridacnidorum sp. nov., a dinoflagellate common to Indo-Pacific giant clams, and a revised morphological description of Symbiodinium microadriaticum Freudenthal, emended Trench & Blank, European Journal of Phycology, 50(2):155172.Google Scholar
Lehmann, A.S., Pont, S.C. and Geusebroek, J.M. (2005), Tree textures: modern techniques in art-historical context, in Proc. 4th International Workshop on Texture Analysis and Synthesis, Beijing, China, 31 October 2005, pp. 4348.Google Scholar
Lehmann, J.R.K., Große-Stoltenberg, A., Römer, M. and Oldeland, J. (2015), Field spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content, Remote Sensing, 7(2):12251241.Google Scholar
Lei, T.T., Tabuchi, R., Kitao, M. and Koike, T. (1996), Functional relationship between chlorophyll content and leaf reflectance, and light-capturing efficiency of Japanese forest species, Physiologia Plantarum, 96(3):411418.Google Scholar
Le Maire, G., François, C. and Dufrêne, E. (2004), Towards universal broadleaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements, Remote Sensing of Environment, 89(1):128.Google Scholar
Le Maire, G., François, C., Soudani, K., Berveiller, D., Pontailler, J.Y., Bréda, N., et al. (2008), Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass, Remote Sensing of Environment, 112(10):38463864.Google Scholar
Lemery, (1706), Que les plantes contiennent réellement du fer, & que ce métal entre nécessairement dans leur composition naturelle, Histoire de l’Académie royale des sciences, Tome I, pp. 411418.Google Scholar
Lenk, S., Chaerle, L., Pfündel, E., Langsdorf, G., Hagenbeek, D., Lichtenthaler, H.K., et al. (2007), Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications, Journal of Experimental Botany, 58(4):807814.Google Scholar
Leon, C.T., Shaw, D.R., Bruce, L.M. and Watson, C. (2003), Effect of purple (Cyperus rotundus) and yellow nutsedge (C. esculentus) on growth and reflectance characteristics of cotton and soybean, Weed Science, 51(4):557564.Google Scholar
Léon, L. and Downey, G. (2006), Preliminary studies by visible and near-infrared reflectance spectroscopy of juvenile and adult olive (Olea europaea L.) leaves, Journal of the Science of Food and Agriculture, 86(6):9991004.Google Scholar
Leong, H.C. (2008), Imaging and reflectance spectroscopy for the evaluation of effective camouflage in the SWIR, POINTER, 34(3):7784.Google Scholar
Lestari, W.A., Herdiyeni, Y., Prasetyo, L.B., Hasbi, W., Arai, K. and Okumura, H. (2015), Nitrogen estimation of paddy based on leaf reflectance using artificial neural network, in Proc. 7th International Conference of Soft Computing and Pattern Recognition, Fukouka, Japan, 13–15 November 2015, IEEE, pp. 224229.Google Scholar
Leuning, R. (1989), Leaf energy balances: developments and applications, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 324(1223):191206.Google Scholar
Lev-Yadun, S. (2001), Aposematic (warning) coloration associated with thorns in higher plants, Journal of Theoretical Biology, 210(3):385388.Google Scholar
Lev-Yadun, S. (2003), Why do some thorny plants resemble green zebras? Journal of Theoretical Biology, 224(4):483489.Google Scholar
Lev-Yadun, S. (2006), Defensive functions of white coloration in coastal and dune plants, Israel Journal of Plant Sciences, 54(4):317325.Google Scholar
Lev-Yadun, S. (2009), Aposematic (warning) coloration in plants, in Plant-Environment Interactions: From Sensory Plant Biology to Active Plant Behavior (Baluška, F., Ed), Springer, Heidelberg, Germany, pp. 167202.Google Scholar
Lev-Yadun, S. and Ne’eman, G. (2004), When may green plants be aposematic? Biological Journal of the Linnean Society, 81(3):413416.Google Scholar
Lev-Yadun, S., Dafni, A., Flaishman, M.A., et al. (2004), Plant coloration undermines herbivorous insect camouflage, BioEssays, 26(10):11261130.Google Scholar
Lev-Yadun, S. and Gould, K.S. (2007), What do red and yellow autumn leaves signal? The Botanical Review, 73(4):279289.Google Scholar
Lev-Yadun, S. and Gould, K.S. (2009), Role of anthocyanins in plant defence, in Anthocyanins: Biosynthesis, Functions, and Applications (Gould, K.S., Davies, K. and Winefield, C., Eds), Springer-Verlag, New York, pp. 2128.Google Scholar
Lev-Yadun, S. and Keasar, T. (2012), Prerequisites for evolution: variation and selection in yellow autumn birch leaves, New Phytologist, 195(2):282284.Google Scholar
Lewis, F.T. (1923), The typical shape of polyhedra cells in vegetable parenchyma and the restoration of that shape following cell division, Proceedings of the American Academy of Arts and Sciences, 58(15):537552.Google Scholar
Lewis, M. (2002), Spectral characterization of Australian arid zone plants, Canadian Journal of Remote Sensing, 28(2):219230.Google Scholar
Lewis, P. and Disney, M. (2007), Spectral invariants and scattering across multiple scales from within-leaf to canopy, Remote Sensing of Environment, 109(2):196206.Google Scholar
Lhotáková, Z., Albrechtová, J., Janáček, J. and Kubinova, L. (2008), Advantages and pitfalls of using free-hand sections of frozen needles for three-dimensional analysis of mesophyll by stereology and confocal microscopy, Journal of Microscopy, 232(1):5663.Google Scholar
Li, B., Liew, O.W. and Asundi, A.K. (2005a), Early detection of calcium deficiency in plants using red edge position, in Proc. Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality (Chen Y.R., Meyer G.E. and Tu S.I., Eds), Boston, MA, 8 November 2005, SPIE, Vol. 5996, 599609.Google Scholar
Li, B., Liew, O.W. and Asundi, A.K. (2006), Pre-visual detection of iron and phosphorus deficiency by transformed reflectance spectra, Journal of Photochemistry and Photobiology B: Biology, 85(2):131139.Google Scholar
Li, B., Wah, L.O. and Asundi, A.K. (2005b), Use of reflectance spectroscopy for early detection of calcium deficiency in plants, in Proc. 3rd International Conference on Experimental Mechanics (Quan C., Chau F.S., Asundi A., Wong B.S. and Lim C.T., Eds), Seville, Spain, SPIE, Vol. 5852, pp. 693697.Google Scholar
Li, B., Liu, Z., Huang, J., Zhang, L., Zhou, W. and Shi, J. (2009), Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network, Transactions of the CSAE, 25(9):143147 (in Chinese).Google Scholar
Li, D., Xu, B., Shi, X., Zhang, C. and Wu, R. (1996), Influence of simulated acid rain on characteristics of reflectance spectra of rice leaves, Remote Sensing of Environment China, 11(4):241247 (in Chinese).Google Scholar
Li, D., Cheng, T., Yao, X., Zhang, Z., Tian, Y., Zhu, Y. et al. (2016), Wavelet-based PROSPECT inversion for retrieving leaf mass per area (LMA) and equivalent water thickness (EWT) from leaf reflectance, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’16), Beijing, 10–15 July 2016, IEEE, pp. 69106913.Google Scholar
Li, G., Alchanatis, V. and Shmilovitch, Z. (1999), Nitrogen status detection of corn leaves by reflectance technique, in Proc. International Conference on Agricultural Engineering, Beijing, China,14–17 December 1999, Vol. 5, pp. 1926.Google Scholar
Li, L., Ustin, S.L. and Lay, M. (2005c), Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico, Remote Sensing of Environment, 94(1):116.Google Scholar
Li, L., Ustin, S.L. and Riano, D. (2007), Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling, IEEE Geoscience and Remote Sensing Letters, 4(2):216220.Google Scholar
Li, L., Cheng, Y.B., Ustin, S.L., Hua, X.T. and Riaño, D. (2008), Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression, Advances in Space Research, 41(11):17551763.Google Scholar
Li, P. and Wang, Q. (2011), Retrieval of leaf biochemical parameters using PROSPECT inversion: a new approach for alleviating ill-posed problems, IEEE Transactions on Geoscience and Remote Sensing, 49(7):24992506.Google Scholar
Li, P. and Wang, Q. (2013), Retrieval of chlorophyll for assimilating branches of a typical desert plant through inversed radiative transfer models, International Journal of Remote Sensing, 34(7):24022416.Google Scholar
Li, X. and He, Y. (2008), Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks, Biosystems Engineering, 99(3):313321.Google Scholar
Li, Y., Ni, S. and Huang, J. (2003), Simulation rice leaf reflectance and its inversion, in Proc. Multispectral and Hyperspectral Remote Sensing Instruments and Applications (Larar A.M., Tong Q. and Suzuki M., Eds), Hangzhou, China, Vol. 4897, pp. 171178.Google Scholar
Liakoura, V., Bornman, J.F. and Karabourniotis, G. (2003), The ability of abaxial and adaxial epidermis of sun and shade leaves to attenuate UV-A and UV-B radiation in relation to the UV absorbing capacity of the whole leaf methanolic extracts, Physiologia Plantarum, 117(1):3343.Google Scholar
Liao, Q., Wang, J., Yang, G., Zhang, D., Li, H., Fu, Y. et al. (2013), Comparison of spectral indices and wavelet transform for estimating chlorophyll content of maize from hyperspectral reflectance, Journal of Applied Remote Sensing, 7(1):073575.Google Scholar
Lichtenthaler, H.K. (1987), Chlorophylls and carotenoids: pigments of photosynthetic biomembranes, Methods in Enzymology, 148:350382.Google Scholar
Lichtenthaler, H. K. and Rinderle, U. (1988), The role of chlorophyll fluorescence in the detection of stress conditions in plants, CRC Critical Reviews in Analytical Chemistry, 19(1):S29S85.Google Scholar
Lichtenthaler, H.K., Gitelson, A. and Lang, M. (1996), Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements, Journal of Plant Physiology, 148(3–4):483493.Google Scholar
Lichtenthaler, H.K., Wenzel, O., Buschmann, C. and Gitelson, A. (1998), Plant stress detection by reflectance and fluorescence, Annals of the New York Academy of Sciences, 851(1):271285.Google Scholar
Liew, O.W., Boey, W.S.L., Asundi, A.K., Chen, J.W. and He, D.M. (1999), Fibre optic spectrophotometry monitoring of plant nutrient deficiency under hydroponic culture conditions, in Proc. Optical Engineering for Sensing and Nanotechnology (Yamaguchi I., Ed), Yokohama, Japan, 7 May 1999, SPIE, Vol. 3740, pp. 186190.Google Scholar
Lillesaeter, O. (1982), Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling, Remote Sensing of Environment, 12(3):247254.Google Scholar
Lin, B., Sun, W., Min, Q. and Hu, Y. (2008), Numerical studies of scattering properties of leaves and leaf moisture influences on the scattering at microwave wavelengths, IEEE Transactions on Geoscience and Remote Sensing, 46(2):353360.Google Scholar
Lin, Z.F. and Ehleringer, J.R. (1983), Epidermis effects on spectral properties of leaves of four herbaceous species, Physiologia Plantarum, 59(1):9194.Google Scholar
Linacre, E.T. (1964), Determinations of the heat transfer coefficient of a leaf, Plant Physiology, 39(4):687690.Google Scholar
Lindenmayer, A. (1984), Models for plant tissue development with cell division orientation regulated by preprophase bands of microtubules, Differentiation, 26(1–3):110.Google Scholar
Lindenthal, M., Steiner, U., Dehne, H.W. and Oerke, E.C. (2005), Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography, Phytopathology, 95(3):233240.Google Scholar
Linke, R., Richter, K., Haumann, J., Schneider, W. and Weihs, P. (2008), Occurrence of repeated drought events: can repetitive stress situations and recovery from drought be traced with leaf reflectance? Periodicum Biologorum, 110(3):219229.Google Scholar
Lintern, M., Anand, R., Ryan, C. and Paterson, D. (2013), Natural gold particles in Eucalyptus leaves and their relevance to exploration for buried gold deposits, Nature Communications, 4:2614.Google Scholar
Lister, S.J., Dhanoa, M.S., Stewart, J.L. and Gill, M. (2000), Classification and comparison of Gliricidia provenances using near infrared reflectance spectroscopy, Animal Feed Science and Technology, 86(3–4):221238.Google Scholar
Litjens, R.A.J., Quickenden, T.I. and Freeman, C.G. (1999), Visible and near-ultraviolet absorption spectrum of liquid water, Applied Optics, 38(7):12161223.Google Scholar
Liu, C., Guo, J., Cui, Y., , T., Zhang, X. and Shi, G. (2011a), Effects of cadmium and salicylic acid on growth, spectral reflectance and photosynthesis of castor bean seedlings, Plant Soil, 344(1–2):131141.Google Scholar
Liu, M., Liu, X., Li, T. and Xiu, L. (2010a), Analysis of hyperspectral singularity of rice under Zn pollution stress, Transactions of the Chinese Society of Agricultural Engineering, 26(3):191197 (in Chinese).Google Scholar
Liu, N., Peng, C.L., Lin, Z.F., Lin, G.Z., Zhang, L.L. and Pan, X.P. (2006), Changes in photosystem II activity and leaf reflectance features of several subtropical woody plants under simulated SO2 treatment, Journal of Integrative Plant Biology, 48(11):12741286.Google Scholar
Liu, N., Lin, Z.F., Van Devender, A., Lin, G.Z., Peng, C.L., Pan, X.P., et al. (2009a), Spectral reflectance indices and pigment functions during leaf ontogenesis in six subtropical landscape plants, Plant Growth Regulation, 58(1):7384.Google Scholar
Liu, P., Shi, R., Wang, H., Bai, K. and Gao, W. (2014), Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model, in Proc. Remote Sensing and Modeling of Ecosystems for Sustainability XI (Gao W., Chang N.B. and Wang J., Eds), San Diego, CA, 17 August 2014, SPIE, Vol. 9221, 92211A.Google Scholar
Liu, P., Zhou, J., Shi, R., Zhang, C., Liu, C., Sun, Z. et al. (2016), Identification of Phragmites australis and Spartina alterniflora in the Yangtze Estuary between Bayes and BP neural network using hyper-spectral data, in Proc. Remote Sensing and Modeling of Ecosystems for Sustainability XIII (Gao B.C. and Chang N.B., Eds), San Diego, CA, 28 August–1 September 2016, SPIE, Vol. 9975, 99750H.Google Scholar
Liu, S.H., Liu, X.H., Hou, J., Chi, G.Y. and Cui, B.S. (2008a), Study on the spectral response of Brassica campestris L. leaf to the copper pollution, Science in China Series E: Technological Sciences, 51(2):202208.Google Scholar
Liu, W. and Lao, C. (2007), The application of three B-spline interpolation in measuring scattering flux from leaves, Control & Management, 6: 181182 (in Chinese).Google Scholar
Liu, W., Chang, Q.R., Guo, M., Xing, D.X. and Yuan, Y.S. (2011b), Diagnosis of phosphorus nutrition in winter wheat based on first derivative spectra and radial basis function neural network, Spectroscopy and Spectral Analysis, 31(4):10921096 (in Chinese).Google Scholar
Liu, Y., Chen, H., Wu, G. and Wu, X. (2010b), Feasibility of estimating heavy metal concentrations in Phragmites australis using laboratory-based hyperspectral data-A case study along Le’an River, China, International Journal of Applied Earth Observation and Geoinformation, 12(2):S166S170.Google Scholar
Liu, Z.M., Wu, W.J. and Hu, B.R. (2008b), Design of biomimetic camouflage materials based on angiosperm leaf organs, Science in China Series E: Technological Sciences, 51(11):19021910.Google Scholar
Liu, Z.M., Hu, B.R., Wu, W.J. and Zhang, Y. (2009b), Spectral imaging of green coating camouflage under hyperspectral detection, Acta Photonica Sinica, 38(4):885890 (in Chinese).Google Scholar
Liu, Z.Y., Huang, J.F., Shi, J.J., Tao, R.X., Zhou, W. and Zhang, L.L. (2007), Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression, Journal of Zhejiang University SCIENCE B, 8(10):738744.Google Scholar
Liu, Z.Y., Huang, J.F. and Tao, R.X. (2008c), Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data, Rice Science, 15(3):232242.Google Scholar
Liu, Z., Cheng, J., Huang, W., Li, C., Xu, X., Ding, X., et al. (2012), Hyperspectral discrimination and response characteristics of stressed rice leaves caused by rice leaf folder, in Computer and Computing Technologies in Agriculture V (Li, D. and Chen, Y., Eds), Springer, pp. 528537.Google Scholar
Lolli, L., Pisani, M., Rajteri, M., Widlowski, J.L., Bialek, A., Greenwell, C. et al. (2014), Phytos: a portable goniometer for in situ spectro-directional measurements of leaves, Metrologia, 51(6):S309S313.Google Scholar
Lommel, E. (1874), Ueber den Lichtschein um den Schatten des Kopfe, in Annalen der Physik und Chemie., Jubelband, Leipzig, pp. 1021.Google Scholar
Lorenzen, B. and Jensen, A. (1989), Changes in leaf spectral properties induced in barley by cereal powdery mildew, Remote Sensing of Environment, 27(2):201209.Google Scholar
Lorenzen, B. and Jensen, A. (1991), Spectral properties of barley canopy in relation to the spectral properties of single leaves and the soil, Remote Sensing of Environment, 37(1):2334.Google Scholar
Lorenzen, B., Skovhus, K. and Jensen, A. (1990), Spectral properties and net photosynthesis of Aster tripolium L. and Halimione portulacoides (L.) Aellen leaves under saline and hypoxic conditions, New Phytologist, 116(2):255262.Google Scholar
Lovchikova, L.P., Nikonenko, S.V., Plyuta, V.E. and Sergeichik, S.A. (1997), Spectral reflection characteristics of spruce needles and leaves of trees in the zones with industrially polluted air, Journal of Applied Spectroscopy, 64(6):804808 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 64(6):789792).Google Scholar
Lovelock, C.E., Clough, B.F. and Woodrow, I.E. (1992), Distribution and accumulation of ultraviolet-radiation absorbing compounds in leaves of tropical mangroves, Planta, 188(2):143154.Google Scholar
Lu, C., Hen, S.B. and Liu, W.S. (2013), Research of PROSPECT leaf optical property model, Global Geology, 32(1):177188 (in Chinese).Google Scholar
Lu, S., Zhao, C. and Guo, X. (2009), Venation skeleton-based modeling plant leaf wilting, International Journal of Computer Games Technology, ID 890917, 8 pages.http://dx.doi.org/10.1155/2009/890917Google Scholar
Lucarini, V., Saarinen, J.J., Peiponen, K.E. and Vartiainen, E.M. (2005), Kramers–Kronig Relations in Optical Materials Research, Springer-Verlag, Berlin 162 pages.Google Scholar
Lück, J., Lindenmayer, A. and Lück, H.B. (1988), Models of cell tetrads and clones in meristematic cell layers, Botanical Gazette, 149(2):127141.Google Scholar
Lüdeker, W. and Günther, K.P. (1990), Leaf reflectance: a stochastic model for analysing the blue shift, in Proc. Symposium on Global and Environmental Monitoring Techniques and Impacts, Victoria, BC, 17–21 September 1990, ISPRS, Vol. 28, pp. 475480.Google Scholar
Lukeš, P., Stenberg, P., Rautiainen, M., Mõttus, M. and Vanhatalo, K.M. (2013), Optical properties of leaves and needles for boreal tree species in Europe, Remote Sensing Letters, 4(7):667676.Google Scholar
Lunadei, L., Diezma, B., Lleó, L., Ruiz-Garcia, L., Cantalapiedra, S. and Ruiz-Altisent, M. (2012), Monitoring of fresh-cut spinach leaves through a multispectral vision system, Postharvest Biology and Technology, 63(1):7484Google Scholar
Lunagaria, M.M., Patel, H.R. and Pandey, V.Y.A.S. (2015), Evaluation and calibration of noninvasive leaf chlorophyll meters for wheat, Journal of Agrometeorology, 17(1):5154.Google Scholar
Luo, N.N., Zhao, W.J. and Yan, X. (2013), Impact of dust-fall on spectral features of plant leaves, Spectroscopy and Spectral Analysis, 33(10):2715–20 (in Chinese).Google Scholar
Lvovsky, A.I. (2013), Fresnel equations, in Encyclopedia of Optical Engineering, CRC Press, 7 pp.Google Scholar
Ma, K., Baret, F., Barroy, P. and Bousquet, L. (2007), A leaf optical properties model accounting for differences between two faces, in Proc. 10th International Symposium on Physical Measurements and Signatures in Remote Sensing (Schaepman M., Liang S., Groot N. and Kneubühler M., Eds), Davos, Switzerland, 12–14 March 2007, ISPRS, pp. 89–95.Google Scholar
Ma, Q., Ishimaru, A., Phu, P. and Kuga, Y. (1990), Transmission, reflection, and depolarization of an optical wave for a single leaf, IEEE Transactions on Geoscience and Remote Sensing, 28(5):865872.Google Scholar
Ma, Z.G., Chen, X., Wang, Q., Li, P.H. and Jiapaer, G. (2012), Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an application to Populus euphratica polymorphic leaves, Journal of Arid Land, 4(1):5262.Google Scholar
Maas, S.J. and Dunlap, J.R. (1989), Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves, Agronomy Journal, 81(1):105110.Google Scholar
Macaire-Princep, I. (1828), Mémoire sur la coloration automnale des feuilles, Mémoires de la Société de physique et d’histoire naturelle de Genève, 4:4353.Google Scholar
MacArthur, A. and Malthus, T. (2012), Calluna vulgaris foliar pigments and spectral reflectance modelling, International Journal of Remote Sensing, 33(16):52145239.Google Scholar
Maccioni, A., Agati, G. and Mazzinghi, P. (2001), New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra, Journal of Photochemistry and Photobiology. B, Biology, 61(1–2):5261.Google Scholar
Machado, M.L., Pinto, F.A.C., Paula, T.J., Queiroz, D.M. and Cerqueira, O.A.T. (2015), White mold detection in common beans through leaf reflectance spectroscopy, Engenharia Agrícola, 35(6):11171126.Google Scholar
Macior, W.A. and Matzke, E.B. (1951), An experimental analysis of cell-wall curvatures, and approximations to minimal tetrakaidecahedra in the leaf parenchyma of Rhoeo discolor, American Journal of Botany, 38(10):783793.Google Scholar
MacNeil, J.D., Downing, R.S. and Hikichi, M. (1974), Diffuse reflectance spectroscopy – Applications in measuring mite-feeding injury to orchard leaves, Canadian Journal of Plant Science, 54(3):505509.Google Scholar
MacNeil, J.D., Hikichi, M. and Downing, R.S. (1987), An investigation of the effects of seasonal changes, leaf maturity, nitrogen deficiency and leafhopper injury on the chlorophyll content and diffuse reflectance spectroscopic properties of orchard leaves, International Journal of Environmental and Analytical Chemistry, 31(1):5562.Google Scholar
Macnicol, P.K., Dudzinski, M.L. and Condon, B.N. (1976), Estimation of chlorophyll in tobacco leaves by direct photometry, Annals of Botany, 40(1):143152.Google Scholar
Madeira, A.C., Mendonca, A., Ferreira, M.E. and Taborda, M.D. (2000), Relationship between spectroradiometric and chlorophyll measurements in green beans, Communications in Soil Science and Plant Analysis, 31(5–6):631643.Google Scholar
Madeira, A.C., Ferreira, A., de Varennes, A. and Vieira, M.I. (2003), SPAD meter versus tristimulus colorimeter to estimate chlorophyll content and leaf color in sweet pepper, Communications in Soil Science and Plant Analysis, 34(17–18):26412470.Google Scholar
Magarey, R.D., Russo, J.M., Seem, R.C. and Gadoury, D.M. (2005), Surface wetness duration under controlled environmental conditions, Agricultural and Forest Meteorology, 128(1–2):111122.Google Scholar
Magnussen, S., Coops, N., Luther, J.E. and Carroll, A.L. (2004), An approach for the analysis of vegetation spectra using non-linear mixed modeling of truncated power spectra, Annals of Forest Science, 61(6):515523.Google Scholar
Mahlein, A.K., Steiner, U., Dehne, H.W. and Oerke, E.C. (2010), Spectral signatures of sugar beet leaves for the detection and differentiation of diseases, Precision Agriculture, 11(4):413431.Google Scholar
Mahlein, A.K., Steiner, U., Hillnhütter, C., Dehne, H.W. and Oerke, E.C. (2012), Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases, Plant Methods, 8:3.Google Scholar
Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plümer, L., Steiner, U. et al. (2013), Development of spectral indices for detecting and identifying plant diseases, Remote Sensing of Environment, 128:2130.Google Scholar
Maier, S.W., Lüdeker, W. and Günther, K.P. (1999), SLOP: a revised version of the stochastic model for leaf optical properties, Remote Sensing of Environment, 68(3):273280.Google Scholar
Maier, S.W. (2000), Modeling the radiative transfer in leaves in the 300 nm to 2.5 µm wavelength region taking into consideration chlorophyll fluorescence – The leaf model SLOPE, PhD Thesis, Deutsches Fernerkundungstagsdatenzentrum, Technische Universität München, Oberpfaffenhofen, Germany, 110 pages.Google Scholar
Majeke, B., van Aardt, J.A.N. and Cho, M.A. (2008), Imaging spectroscopy of foliar biochemistry in forestry environments, Southern Forests, 70(3):275285.Google Scholar
Major, D.J., McGinn, S.M., Gillespie, T.J. and Baret, F. (1993), A technique for determination of single leaf reflectance and transmittance in field studies, Remote Sensing of Environment, 43(2):209215.Google Scholar
Maki, M., Ishiahra, M. and Tamura, M. (2004), Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data, Remote Sensing of Environment, 90(4):441450.Google Scholar
Makrides, S.C. and Goldthwaite, J. (1981), Biochemical changes during bean leaf growth, maturity, and senescence, Journal of Experimental Botany, 32(129):725735.Google Scholar
Main, R., Cho, M.A., Mathieu, R., O’Kennedy, M.M., Ramoelo, A. and Koch, S. (2011), An investigation into robust spectral indices for leaf chlorophyll estimation, ISPRS Journal of Photogrammetry and Remote Sensing, 66(6):751761.Google Scholar
Male, E.J., Pickles, W.L., Silver, E.A., Hoffmann, G.D., Lewicki, J., Apple, M., et al. (2010), Using hyperspectral plant signatures for CO2 leak detection during the 2008 ZERT CO2 sequestration field experiment in Bozeman, Montana, Environmental Earth Sciences, 60(2):251261.Google Scholar
Malenovsky, Z., Albrechtova, J., Lhotakova, Z., Zurita-Milla, R., Clevers, J.G.P.W., Schaepman, M.E. et al. (2006), Applicability of the PROSPECT model for Norway spruce needles, International Journal of Remote Sensing, 27(24):53155340.Google Scholar
Malhotra, S.S. (1977), Effects of aqueous sulphur dioxide on chlorophyll destruction in Pinus contorta, New physiologist, 78(1):101109.Google Scholar
Malinin, D.R. and Yoe, J.H. (1961), Development of the laws of colorimetry: a historical sketch, Journal of Chemical Education, 38(3):129131.Google Scholar
Malthus, T.J. and Madeira, A.C. (1993), High resolution spectroradiometry: spectral reflectance of field bean leaves infected by Botrytis fabae, Remote Sensing of Environment, 45(1):107116.Google Scholar
Manabe, S.I., Fujioka, R., Iigima, H. and Yaginuma, Y. (2001), Change in refractive index of cellulose particle with particle size, Bulletin of the Faculty of Human Environmental Science, 32:6569.Google Scholar
Mandelis, A., Boroumand, F. and vanden Bergh, H. (1990), Quantitative diffuse reflectance spectroscopy of large powders: the Melamed model revisited, Applied Optics, 29(19):28532860.Google Scholar
Mandelis, A. and Grossmann, J.P. (1992), Perturbation theoretical approach to the generalized Kubelka-Munk problem in nonhomogeneous optical media, Applied Spectroscopy, 46(5):737745.Google Scholar
Mandoli, D.F. and Briggs, W.R (1982), Optical properties of etiolated plant tissues, Proceedings of the National Academy of Sciences of the United States of America, 79(9):29022906.Google Scholar
Mandoli, D.F. and Briggs, W.R (1984a), Fiber optics plant tissues: spectral dependence in dark-grown and green tissues, Photochemistry and Photobiology, 39(3):419424.Google Scholar
Mandoli, D.F. and Briggs, W.R (1984b), Fiber optics in plants, Scientific American, 251(2):9098.Google Scholar
Mandre, M. and Tuulmets, L. (1997), Pigment changes in Norway spruce induced by dust pollution, Water, Air, & Soil Pollution, 94(3–4):247258.Google Scholar
Manetas, Y., Grammatikopoulos, G. and Kyparissis, A. (1998), The use of the portable, non-destructive, SPAD-502 (Minolta) chlorophyll meter with leaves of varying trichome density and anthocyanin content, Journal of Plant Physiology, 153(3–4):513516.Google Scholar
Manetas, Y. (2003), The importance of being hairy: the adverse effects of hair removal on stem photosynthesis of Verbascum speciosum are due to solar UV-B radiation, New Phytologist, 158(3):503508.Google Scholar
Manetas, Y. (2006), Why some leaves are anthocyanic and why most anthocyanic leaves are red? Flora, 201(3):163177.Google Scholar
Manna, J.S., Basu, S., Mitra, M.K., Mukherjee, S. and Chandra Das, G. (2009), Study on the biostability of chlorophyll a entrapped in silica gel nanomatrix, Journal of Materials Science: Materials in Electronics, 20(11):10681072.Google Scholar
Maquenne, E. (1880), Recherches sur la détermination des pouvoirs absorbants et diffusifs des feuilles, Annales Agronomiques, 6:321390.Google Scholar
Maréchal, Y., and Chanzy, H. (2000), The hydrogen bond network in Iβ cellulose as observed by infrared spectrometry, Journal of Molecular Structure, 523(1–3):183196.Google Scholar
Marenco, R.A., Antezana-Vera, S.A. and Nascimento, H.C.S. (2009), Relationship between specific leaf area, leaf thickness, leaf water content and SPAD-502 readings in six Amazonian tree species, Photosynthetica, 47(2):184190.Google Scholar
Mariotti, M., Ercoli, L. and Masoni, A. (1996), Spectral properties of iron-deficient corn and sunflower leaves, Remote Sensing of Environment, 58(3):282288.Google Scholar
Markel, V.A. (2016), Introduction to the Maxwell Garnett approximation: tutorial, Journal of the Optical Society of America A, 33(7):12441256.Google Scholar
Markwell, J., Osterman, J.C. and Mitchell, J.L. (1995), Calibration of the Minolta SPAD-502 leaf chlorophyll meter, Photosynthesis Research, 46(3):467472.Google Scholar
Markwell, J. and Blevins, D. (1999), The Minolta SPAD-502 leaf chlorophyll meter: an exciting new tool for education in the plant sciences, American Biology Teacher, 61(9):672676.Google Scholar
Markwell, J. (2002), Nondestructive chlorophyll assessment, New Phytologist, 153(1):78.Google Scholar
Marquart, L.C. (1835), Die Farben der Blüthen: Eine Chemisch-Physiologische Abhandlung, T. Habicht, Bonn, 75 pages.Google Scholar
Martin, C.E., Brandmeyer, E.A. and Ross, R.D. (2013), Ecophysiological function of leaf ‘windows’ in Lithops species – “Living Stones” that grow underground, Plant Biology, 15(1):243247.Google Scholar
Martin, G., Josserand, S.A., Bornman, J.F. and Volgemann, T.C. (1989), Epidermal focussing and the light microenvironment within leaves of Medicago sativa, Physiologia Plantarum, 76(4):485492.Google Scholar
Martin, G., Myers, D.A. and Vogelmann, T.C. (1991), Characterization of plant epidermal lens effects by a surface replica technique, Journal of Experimental Botany, 42(238):581587.Google Scholar
Martin, J.T. and Juniper, B.E. (1970), The Cuticles of Plants, E. Arnold, London, 347 pages.Google Scholar
Martin, M.E. and Aber, J.D. (1994), Analyses of forest foliage III: determining nitrogen, lignin and cellulose in fresh leaves using near infrared reflectance data, Journal of Near Infrared Spectroscopy, 2(1):2532.Google Scholar
Martin, M.E. and Aber, J.D. (1997), High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes, Ecological Applications, 7(2):431443.Google Scholar
Martin, R.E., Asner, G.P. and Sack, L. (2007), Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden, Oecologia, 151(3):387400.Google Scholar
Martin, R.E. and Asner, G.P. (2009), Leaf chemical and optical properties of Metrosideros polymorpha across environmental gradients in Hawaii, Biotropica, 41(3):292301.Google Scholar
Martin, W.E., Hesse, E., Hough, J.H., Sparks, W.B., Cockell, C.S., Ulanowski, Z., et al. (2010), Polarized optical scattering signatures from biological materials, Journal of Quantitative Spectroscopy & Radiative Transfer, 111(16):24442459.Google Scholar
Martin, W.K. and Thomas, S.B. (1887), The autumnal changes in maple leaves, Scientific American, 24:98139814.Google Scholar
Martinez, D.E. and Guiamet, J.J. (2004), Distortion of the SPAD 502 chlorophyll meter readings by changes in irradiance and leaf water status, Agronomie, 24(1):4146.Google Scholar
Martinez von Remisowsky, A., McClendon, J.H. and Fukshansky, L. (1992), Estimation of the optical parameters and light gradients in leaves: multi-flux versus two-flux treatment, Photochemistry and Photobiology, 55(6):857865.Google Scholar
Masaitis, G., Mozgeris, G. and Augustaitis, A. (2013), Spectral reflectance properties of healthy and stressed coniferous trees, iForest, 6:3036.Google Scholar
Masoni, A., Ercoli, L., Mariotti, M. and Barberi, P. (1994), Changes in spectral properties of ageing and senescing maize and sunflower leaves, Physiologia Plantarum, 91(2):334338.Google Scholar
Masoni, A., Ercoli, L. and Mariotti, M. (1996), Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese, Agronomy Journal, 88(6):937943.Google Scholar
Massantini, F., Masoni, A., Mariotti, M. and Volterrani, M. (1992), Influenza della concimazione azotata sulla riflettanza, assorbanza e trasmittanza della foglia a bandiera del frumento tenero (Triticum aestivum L.), Rivista di Agronomia, 26 (4):489497 (in Italian).Google Scholar
Matile, P., Flach, B.M.P. and Eller, B.M. (1992), Autumn leaves of Ginkgo biloba L. optical properties, pigments and optical brighteners, Botanica Acta, 105(1):1317.Google Scholar
Matile, P. (2000), Biochemistry of Indian summer: physiology of autumnal leaf coloration, Experimental Gerontology, 35(2):145158.Google Scholar
Mattsson, J.O. and Cavallin, C. (1972), Retroreflection of light from drop-covered surfaces and an image-producing device for registration of this light, Oikos, 23(3):285294.Google Scholar
Mätzler, C. (1994), Microwave (1–100 GHz) dielectric model of leaves, IEEE Transactions on Geoscience and Remote Sensing, 32(5):947949.Google Scholar
Mätzler, C. and Sume, A. (1989), Microwave radiometry of leaves, in Microwave Radiometry and Remote Sensing Applications (Pampaloni, P., Ed), VSP Books, Utrech, The Netherlands, pp. 133148.Google Scholar
Maxwell, K. and Johnson, G.N. (2000), Chlorophyll fluorescence-a practical guide, Journal of Experimental Botany, 51(345): 659668.Google Scholar
Maxwell Garnett, J.C. (1904), Colours in metal glasses and in metallic films, Philosophical Transactions of the Royal Society of London, Series A, 203:385420.Google Scholar
Mayer, J.R. (1845), Die organische Bewegung in ihrem Zusammenhange mit dem Stoffwechsel : Ein Beitrag zur Naturkunde, Heilbronn, 112 pages (in German).Google Scholar
McClendon, J.H. (1984), The micro-optic of leaves. I: Patterns of reflection from the epidermis, American Journal of Botany, 71(10):13911397.Google Scholar
McClendon, J.H. and Fukshansky, L. (1990a), On the interpretation of absorption spectra of leaves. I. Introduction and the correction of leaf spectra for surface reflection, Photochemistry and Photobiology, 51(2):203210.Google Scholar
McClendon, J.H. and Fukshansky, L. (1990b), On the interpretation of absorption spectra of leaves. II: The non-absorbed ray of the sieve effect and the mean optical pathlength in the remainder of the leaf, Photochemistry and Photobiology, 51(2):211216.Google Scholar
McCree, K.J. (1972), The action spectrum, absorptance and quantum yield of photosynthesis in crop plants, Agricultural Meteorology, 9:191216.Google Scholar
McLellan, T.M., Aber, J.D., Martin, M.E., Melillo, J.M. and Nadelhoffer, K.J. (1991a), Determination of nitrogen, lignin, and cellulose content of decomposing leaf material by near infrared reflectance spectroscopy, Canadian Journal of Forest Research, 21(11):16841688.Google Scholar
McLellan, T.M., Martin, M.E., Aber, J.D., Melillo, J.M., Nadelhoffer, K.J. and Dewey, B. (1991b), Comparison of wet chemistry and near infrared reflectance measurements of carbon-fraction chemistry and nitrogen concentration of forest foliage, Canadian Journal of Forest Research, 21(11):16891693.Google Scholar
McMurtrey III, J.E., Chappelle, E.W., Kim, M.S., Meisinger, J.J. and Corp, L.A. (1994), Distinguishing nitrogen fertilization levels in field corn (Zea Mays L.) with actively induced fluorescence and passive reflectance measurements, Remote Sensing of Environment, 47(1):3644.Google Scholar
McPherson, S. (2010), Iridescent plants of the world, The Plantsman, 9(2):120125.Google Scholar
Meggio, F., Zarco-Tejada, P.J., Núñez, L.C., Sepulcre-Cantó, G., González, M.R. and Martín, P. (2010), Grape quality assessment in vineyards affected by iron deficiency chlorosis using narrow-band physiological remote sensing indices, Remote Sensing of Environment, 114(9):19681986.Google Scholar
Meinander, O., Somersalo, S., Holopainen, T. and Strasser, R.J. (1996), Scots pines after exposure to elevated ozone and carbon dioxide probed by reflectance spectra and chlorophyll a fluorescence transients, Journal of Plant Physiology, 148(1–2):229236.Google Scholar
Meinzer, F. and Goldstein, G. (1985), Some consequences of leaf pubescence in the Andean giant rosette plant Espeletia timotensis, Ecology, 66(2):512520.Google Scholar
Meir, S., Philosoph-Hadasa, S., Glotera, P. and Aharonia, N. (1992), Nondestructive assessment of chlorophyll content in watercress leaves by a tristimulus reflectance colorimeter, Postharvest Biology and Technology, 2(2):117124.Google Scholar
Meissner, T. and Wentz, F.J. (2004), The complex dielectric constant of pure and sea water from microwave satellite observations, IEEE Transactions on Geoscience and Remote Sensing, 42(9):18361849.Google Scholar
Melamed, N.T. (1963), Optical properties of powders. Part I. Optical absorption coefficients and the absolute value of the diffuse reflectance. Part II: Properties of luminescent powders, Journal of Applied Physics, 34(3):560570.Google Scholar
Melvill, T. (1914), Observations on light and colours, Journal of the Royal Astronomical Society of Canada, 8:231272.Google Scholar
Mendelssohn, I.A., McKee, K.L. and Kong, T. (2001), A comparison of physiological indicators of sublethal cadmium stress in wetland plants, Environmental and Experimental Botany, 46(3):263275.Google Scholar
Menesatti, P., Antonucci, F., Pallottino, F., Roccuzzo, G., Allegra, M., Stagno, F. et al. (2010), Estimation of plant nutritional status by Vis–NIR spectrophotometric analysis on orange leaves (Citrus sinensis (L) Osbeck cv Tarocco], Biosystems Engineering, 105(4):448454.Google Scholar
Merk, S., Blume, A. and Riederer, M. (1998), Phase behaviour and crystallinity of plant cuticular waxes studied by Fourier transform infrared spectroscopy, Planta, 204(1):4453.Google Scholar
Meroni, M., Picchi, V., Rossini, M., et al. (2008), Leaf level early assessment of ozone injuries by passive fluorescence and photochemical reflectance index, International Journal of Remote Sensing, 29(17): 54095422.Google Scholar
Meroni, M., Panigada, C., Rossini, M., Picchi, V., Cogliati, S. and Colombo, R. (2009), Using optical remote sensing techniques to track the development of ozone-induced stress, Environmental Pollution, 157(5):14131420.Google Scholar
Merzlyak, M.N. and Gitelson, A. (1995), Why and what for the leaves are yellow in autumn? On the interpretation of optical spectra of senescing leaves (Acer platanoides L.), Journal of Plant Physiology, 145(3):315320.Google Scholar
Merzlyak, M.N., Gitelson, A.A., Pogosyan, S.I., Chivkunova, O.B., Lehimena, L., Garson, M., et al. (1997), Reflectance spectra of leaves and fruits during their development and senescence and under stress, Russian Journal of Plant Physiology, 44(5):614622.Google Scholar
Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B. and Rakitin, V.Y. (1999), Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening, Physiologia Plantarum, 106(1):135141.Google Scholar
Merzlyak, M.N. and Chivkunova, O.B. (2000), Light-stress-induced pigment changes and evidence for anthocyanin photoprotection in apples, Journal of Photochemistry and Photobiology B: Biology, 55(2–3):155163.Google Scholar
Merzlyak, M.N., Chivkunova, O.B. and Razi Naqvi, K. (2002), Does a leaf absorb radiation in the near infrared (780–900 nm) region? A new approach to quantifying optical reflection, absorption and transmission of leaves, Photosynthesis Research, 72(3):263270.Google Scholar
Pogosyan, S.I. (2003), Application of reflectance spectroscopy for analysis of higher plant pigments, Russian Journal of Plant Physiology, 50(5):704710 (cover-to-cover translation from Fiziologiya Rastenii, 50(5):785792).Google Scholar
Merzlyak, M.N., Melo, T.B. and Razi Naqvi, K. (2004), Estimation of leaf transmittance in the near infrared region through reflectance measurements, Journal of Photochemistry and Photobiology. B, Biology, 74(2–3):145150.Google Scholar
Merzlyak, M.N., Chivkunova, O.B., Zhigalova, T.V. and Naqvi, K.R. (2009), Light absorption by isolated chloroplasts and leaves: effects of scattering and “packing”, Photosynthesis Research, 102(1):3141.Google Scholar
Mesarch, M.A., Walter-Shea, E.A., Asner, G.P., Middleton, E.M. and Chan, S.S. (1999), A revised measurement methodology for conifer needles spectral optical properties: evaluating the influence of gaps between elements, Remote Sensing of Environment, 68(2):177192.Google Scholar
Messier, C. and Bellefleur, P. (1987), Variations des propriétés spectrales des feuilles au cours de la croissance du bouleau jaune, Canadian Journal of Botany, 65(8):16821686.Google Scholar
Metzner, P. (1957), Zur Optik der Blattoberflächen, Die Kulturpflanze, 5(1):221239.Google Scholar
Meyer, S., Cerovic, Z.G., Goulas, Y., Montpied, P., Demotes-Mainard, S., Bidel, L.P.R., et al. (2006), Relationships between optically assessed polyphenols and chlorophyll contents, and leaf mass per area ratio in woody plants: a signature of the carbon-nitrogen balance within leaves? Plant, Cell & Environment, 29(7):13381348.Google Scholar
Meyer-Arendt, J.R. (1968), Radiometry and photometry: units and conversion factors, Applied Optics, 7(10):20812084.Google Scholar
Meziane, D. and Shipley, B. (1999), Interacting determinants of specific leaf area in 22 herbaceous species: effects of irradiance and nutrient availability, Plant, Cell & Environment, 22(5):447459.Google Scholar
Meziane, D. and Shipley, B. (2001), Direct and indirect relationships between specific leaf area, leaf nitrogen and leaf gas exchange. Effects of irradiance and nutrient supply, Annals of Botany, 88(5):915927.Google Scholar
Miao, Q., Zhao, W., Guo, X., Liu, K., Han, J. and Wang, Z. (2011), Inversion of reed leaf chlorophyll content based on PROSPECT model, in Proc. 19th International Conference on Geoinformatics, Shanghai, China, 24–26 June 2011, IEEE, pp. 14.Google Scholar
Miao, T., Zhao, C.J., Guo, X.Y. and Lu, S.L. (2013), A framework for plant leaf modeling and shading, Mathematical and Computer Modelling, 58(3–4):710718.Google Scholar
Miao, T., Zhao, C., Guo, X., Lu, S. and Wen, W. (2014), Visual simulating appearance of plant leaves infected by disease and insect pests, Transactions of the Chinese Society of Agricultural Engineering, 30 (2):169175 (in Chinese).Google Scholar
Middleton, E.M. and Walter-Shea, E.A. (1995), Optical properties of canopy elements in the boreal forest, in Proc. 15th International Geoscience and Remote Sensing Symposium (IGARSS’95), Florence, Italy, 10–14 July 1995, IEEE, Vol. 1, pp. 789793.Google Scholar
Middleton, E.M., Chan, S.S., Mesarch, M.A. and Walter-Shea, E.A. (1996), A revised measurement methodology for spectral optical properties of conifer needles, in Proc. 16th International Geoscience and Remote Sensing Symposium (IGARSS’96), Lincoln, NB, 27–31 May 1996, IEEE, Vol. 2, pp. 10051009.Google Scholar
Middleton, E.M., Sullivan, J.H., Bovard, B.D., DeLucas, A.J., Chan, S.S. and Cannon, T.A. (1997), Seasonal variability in foliar characteristics and physiology for boreal forest species at the five Saskatchewan tower sites during the 1994 Boreal Ecosystem-Atmosphere Study (BOREAS), Journal of Geophysical Research – Atmospheres, 102(D24):2883128844.Google Scholar
Middleton, E.M. and Sullivan, J. (2000), BOREAS TE-10 Leaf Chemistry Data, NASA Goddard Space Flight Center, Greenbelt, MD, 1 October 2000, NASA/TM-2000–209891/VOL161, 30 pages.Google Scholar
Middleton, E.M., Campbell, P.K.E., McMurtrey, J.E., Corp, L.A., Butcher, L.M. and Chappelle, E.W. (2002), “Red edge” optical properties of corn leaves from different nitrogen regimes, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02), Toronto, ON, 24–28 June 2002, IEEE, Vol. 4, pp. 22082210.Google Scholar
Middleton, E.M., McMurtrey, J.E., Campbell, P.K.E., Corp, L.A., Butcher, L.M. and Chappelle, E.W. (2003), Optical and fluorescence properties of corn leaves from different nitrogen regimes, in Proc. Remote Sensing for Agriculture, Ecosystems, and Hydrology IV (Owe M., D’Urso G. and Toulios L., Eds), Agia Pelagia, Greece, 23 September 2002 SPIE, Vol. 4879, pp. 7283.Google Scholar
Milanowska, J. and Gruszecki, W.I. (2005), Heat-induced and light-induced isomerization of the xanthophyll pigment zeaxanthin, Journal of Photochemistry and Photobiology B: Biology, 80(3):178186.Google Scholar
Miller, C.E. (2001), Chemical principles of near infrared technology, in Near-Infrared Technology in the Agricultural and Food Industries (Williams, P. and Norris, K., Eds), American Association of Cereal Chemist, pp. 1937.Google Scholar
Miller, J.R., Hare, E.W. and Wu, J. (1990), Quantitative characterization of the vegetation red edge reflectance. 1: An inverted-Gaussian model, International Journal of Remote Sensing, 11(10):17551773.Google Scholar
Miller, J.R., Wu, J., Boyer, M.G., Belanger, M. and Hare, E.W. (1991), Seasonal patterns in leaf reflectance red-edge characteristics, International Journal of Remote Sensing, 12(7):15091523.Google Scholar
Miller, J.R., Steven, M.D. and Demetriades-shah, T.H. (1992), Reflection of layered bean leaves over different soil backgrounds: measured and simulated spectra, International Journal of Remote Sensing, 13(17):32733286.Google Scholar
Miller, J., Berger, M., Goulas, Y., et al. (2005), Development of A Vegetation Fluorescence Canopy Model, ESTEC Contract No. 16365/02/NL/FF, 138 pages.Google Scholar
Milton, N.M., Ager, C.M. and Power, M.S. (1988), Spectral Reflectance Changes in Greenhouse-Grown Metal-Doped Plants, USGS, Reston, VA, 13 pages.Google Scholar
Milton, N.M., Ager, C.M., Eiswerth, B.A. and Power, M.S. (1989a), Arsenic- and selenium-induced changes in spectral reflectance and morphology of soybean plants, Remote Sensing of Environment, 30(3):263269.Google Scholar
Milton, N.M. and Mouat, D.A. (1989b), Remote sensing of vegetation responses to natural and cultural environmental conditions, Photogrammetric Engineering & Remote Sensing, 55(8):11671173.Google Scholar
Milton, N.M., Eiswerth, B.A. and Ager, C.M. (1991), Effect of phosphorus deficiency on spectral reflectance and morphology of soybean plants, Remote Sensing of Environment, 36(2):121127.Google Scholar
Min, M. and Lee, W.S. (2003), Spectral-based nitrogen sensing for Citrus, in Proc. 2003 ASAE Annual Meeting, Las Vegas, NV, 27–30 July 2003, ASAE, 031137.Google Scholar
Min, M. and Lee, W.S. (2005), Determination of significant wavelengths and prediction of nitrogen content for Citrus, Transactions of the ASAE, 48(2):455461.Google Scholar
Min, M., Lee, W.S. and Bogrekci, I. (2004), The effect of water and variety on nitrogen sensing of Citrus leaf, in Proc. 2004 ASAE Annual Meeting, Ottawa, ON, 1–4 August 2004, ASAE, 041080.Google Scholar
Mirzaie, M., Darvishzadeh, R., Shakiba, A., Matkan, A.A., Atzberger, C. and Skidmore, A.K. (2014), Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements, International Journal of Applied Earth Observation and Geoinformation, 26:111.Google Scholar
Młodzińska, E. (2009), Survey of plant pigments: molecular and environmental determinants of plant colors, Acta Biologica Cracovienca Series Botanica, 51(1):716.Google Scholar
Mobasheri, M.R. and Fatemi, S.B. (2013), Leaf equivalent water thickness assessment using reflectance at optimum wavelengths, Theoretical and Experimental Plant Physiology, 25(3):196202.Google Scholar
Mochizuki, S., Cai, D., Komori, T., Kimura, H. and Hori, R. (2001), Virtual autumn coloring system based on biological and fractal model, in Proc. 9th Pacific Conference on Computer Graphics and Applications, Tokyo, Japan, 16–18 October 2001, IEEE, pp. 348354.Google Scholar
Mochizuki, S., Horie, D. and Cai, D. (2007), Fractal shading for autumn coloring, The Journal of the Society for Art and Science, 6(2):7687.Google Scholar
Mohammed, G.H., Noland, T.L., Irving, D., Sampson, P.H., Zarco-Tejada, P.J. and Miller, J.R. (2000), Natural and Stress-Induced Effects on Leaf Spectral Reflectance in Ontario Species, Forest Research Report No.156, 42 pages.Google Scholar
Mohl, H. (1837), Untersuchungen über die winterliche Färbung der Blätter, Flora oder allgemeine botanische Zeitung, 20(2):673720.Google Scholar
Mohl, H. (1838), Recherches sur la coloration hibernale des feuilles, Annales des sciences Naturelles, 9:212235.Google Scholar
Mohr, H. and Schopfer, P. (1995), Plant Physiology, Springer-Verlag, Berlin, 629 pages.Google Scholar
Moldau, H. (1965), On the Use of Polarized Radiation to Analyse the Reflection Indicatrixes of Leaves, 7: Questions on Radiation Regime of Plant Stand. Acad Sci ESSR Inst Phys Astron, Tartu, pp. 96101 (in Russian).Google Scholar
Moldau, H. (1967), Optical model of plant leaf, in Photoactinometric Investigations of Plant Canopy, Valgus Publishers, Tallinn, pp. 89109 (in Russian).Google Scholar
Monje, O.A. and Bugbee, B. (1992), Inherent limitations of nondestructive chlorophyll meters: a comparison of two types of meters, HortScience, 27(1):6971.Google Scholar
Monod, B., Collin, A., Parent, G. and Boulet, P. (2009), Infrared radiative properties of vegetation involved in forest fires, Fire Safety Journal, 44(1):8895.Google Scholar
Monson, R. and Baldocchi, D. (2014), Terrestrial Biosphere-Atmosphere Fluxes, Cambridge University Press, Cambridge, 507 pages.Google Scholar
Mooney, H.A. and Cleland, E.E. (2001), The evolutionary impact of invasive species, Proceedings of the National Academy of Sciences, 98(10):54465451.Google Scholar
Moorthy, I., Miller, J.R., Noland, T.L., Nielsen, U. and Zarco-Tejada, P.J. (2003a), Chlorophyll content estimation of Boreal conifers using hyperspectral remote sensing, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03), Toulouse, France, 21–25 July 2003, IEEE, Vol. 4, pp. 25682570.Google Scholar
Moorthy, I., Miller, J.R., Zarco-Tejada, P.J. and Noland, T.L. (2003b), Needle chlorophyll content estimation: a comparative study of PROSPECT and LIBERTY, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03), Toulouse, France, 21–25 July 2003, IEEE, Vol. 3, pp. 16761678.Google Scholar
Moorthy, I., Miller, J.R. and Noland, T.L. (2008), Estimating chlorophyll concentration in conifer needles with hyperspectral data: an assessment at the needle and canopy level, Remote Sensing of Environment, 112(6):28242838.Google Scholar
Mora, C., Tittensor, D.P., Adl, S., Simpson, A.G.B. and Worm, B. (2011), How many species are there on Earth and in the ocean? PLoS Biology, 9(8):e1001127.Google Scholar
Moran, J.A., Mitchell, A.K., Goodmanson, G. and Stockburger, K.A. (2000), Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods, Tree Physiology, 20(16):11131120.Google Scholar
Moran, M.S., Maas, S.J., Vanderbilt, V.C., Barnes, E.M., Miller, S.N. and Clark, T.R. (2004), Application of image-based remote sensing to irrigated agriculture, in Remote Sensing for Natural Resource Management and Environmental Monitoring (Ustin, S.L., Ed.), Manual of Remote Sensing Third Edition, Vol. 4, Wiley and Sons, pp. 617676.Google Scholar
Moreno, N., Bougourd, S., Haseloff, J. and Feijó, J.A. (2006), Imaging plant cells, in Handbook of Biological Confocal Microscopy (Pawley, J.B., Ed), Springer, US, pp. 769787.Google Scholar
Morozzo, (1782), Lettre de M. le Comte Morozzo à M. L’Abbé Mongez, Auteur du Journal de Physique, Sur les expériences de M. Achard, sur la couleur des végétaux, Journal de physique, de chimie, d’histoire naturelle et des arts, 20: 385389 .Google Scholar
Morren, E. (1858a), Dissertation sur les feuilles vertes et colorées, Imprimerie et Lithographie de C. Annoot-Braeckman, Gand, Belgium, 249 pages.Google Scholar
Morren, E. (1858b), Notice sur les changements de couleur des feuilles pendant l’automne, l’hiver et le printemps, La Belgique Horticole, 8:5358.Google Scholar
Morren, E. (1858c), Notice sur les changements de couleur des feuilles pendant l’automne, l’hiver et le printemps (suite et fin), La Belgique Horticole, 8:8185.Google Scholar
Mortenson, M.E. (1985), Geometric Modeling, Industrial Press, South Norwalk, 452 pages.Google Scholar
Moss, D.M. and Rock, B.N. (1991), Analysis of red edge spectral characteristics and total chlorophyll values for red spruce (Picea rubens) branch segments from Mt Moosilauke, NH, in Proc. 11th International Geoscience and Remote Sensing Symposium (IGARSS’91), Helsinki, Finland, 3–6 June 1991, IEEE, Vol. 3, pp. 15291532.Google Scholar
Moss, D.N. (1964), Optimum lighting of leaves, Crop Science, 4:131136.Google Scholar
Moss, R.A. and Loomis, W.E. (1952), Absorption spectra of leaves. I: The visible spectrum, Plant Physiology, 27(2):370391.Google Scholar
Mõttus, M., Sulev, M. and Hallik, L. (2014), Seasonal course of the spectral properties of alder and birch leaves, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):24962505.Google Scholar
Mouroux, (1770), Examen physico-chimique sur les couleurs des fleurs & de quelques autres substances végétales, Miscellanea philosophico-mathematica Societatis privatae Taurinensis, 6:1151.Google Scholar
Muir, C.D. (2013), How did the Swiss cheese plant get its holes? The American Naturalist, 181(2):273281.Google Scholar
Muller, C. and Riederer, M. (2005), Plant surface properties in chemical ecology, Journal of Chemical Ecology, 31(11):26212651.Google Scholar
Mulroy, T.W. (1979), Spectral properties of heavily glaucous and non-glaucous leaves of a succulent rosette-plant, Oecologia, 38(3):349357.Google Scholar
Murchie, E.H and Horton, P. (1997), Acclimation of photosynthesis to irradiance and spectral quality in British plant species: chlorophyll content, photosynthetic capacity and habitat preference, Plant, Cell & Environment, 20:438448.Google Scholar
Murray, I. and Williams, P.C. (1987), Chemical Principles of Near-Infrared Technology, American Association of Cereal Chemists, St Paul, MN, 312 pages.Google Scholar
Mutanga, O., Ismail, R., Ahmed, F. and Kumar, L. (2007), Using in situ hyperspectral remote sensing to discriminate pest attacked pine forests in South Africa, in Proc. 28th Asian Conference on Remote Sensing, Kuala Lumpur, Malaysia, 12–16 November 2007, AARS, pp. PS3.G5.4.Google Scholar
Myers, D.A., Vogelmann, T.C. and Bornman, J.F. (1994), Epidermal focussing and effects on light utilization in Oxalis acetosella, Physiologia Plantarum, 91(4):651656.Google Scholar
Myers, V.I. and Allen, W.A. (1968), Electrooptical remote sensing methods as nondestructive testing and measuring techniques in agriculture, Applied Optics, 7(9):18191838Google Scholar
Myneni, R.B., Asrar, G., Burnett, R.B. and Kanemasu, E.T. (1987), Radiative transfer in an anisotropically scattering vegetative medium, Agricultural and Forest Meteorology, 41(1–2):97121.Google Scholar
Myneni, R.B. and Ross, J., Eds (1991), Photon-Vegetation Interactions: Applications in Optical Remote Sensing and Plant Ecology, Springer-Verlag, Berlin, 560 pages.Google Scholar
Naidu, R.A., Perry, E.M., Pierce, F.J. and Mekuria, T. (2009), The potential of spectral reflectance technique for the detection of grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars, Computers and Electronics in Agriculture, 66(1):3845.Google Scholar
Nakatani, M. and Kawashima, S. (1994), An attempt to assess leaf color of winter cereals using a simple video system, Japanese Journal of Crop Science, 63(1):4247 (in Japanese).Google Scholar
Naqvi, K.R., Merzlyak, M.N. and Melo, T.B. (2004), Absorption and scattering of light by suspensions of cells and subcellular particles: an analysis in terms of Kramers-Kronig relations, Photochemical & Photobiological Sciences, 3(1):132137.Google Scholar
Narayanan, R.M., Green, S.E. and Alexander, D.R. (1991), Mid-infrared backscatter spectra of selected agricultural crops, in Proc. Optics in Agriculture (DeShazer, J.A. and Meyer, G.E., Eds), Boston, MA, SPIE, Vol. 1379, pp. 116122.Google Scholar
Narayanan, R.M., Mielke, L.N. and Schirmer, T.J. (1992), Mid-infrared Laser reflectance of crop leaves subjected to water stress, in Proc. 12th International Geoscience and Remote Sensing Symposium (IGARSS’92), Houston, TX, 26–29 May 1992, IEEE, pp. 339341.Google Scholar
National Academy of Sciences (1982), Causes and Effects of Stratospheric Ozone Reduction: An Update, The National Academies Press, Washington, DC, 352 pages.Google Scholar
Natsuyama, H., Ueno, S., and Wang, A.P. (1998), Terrestrial Radiative Transfer: Modeling, Computation, and Data Analysis,Springer Verlag, Tokyo, 279 pages.Google Scholar
Naumann, J.C., Anderson, J.E. and Young, D.R. (2010), Remote detection of plant physiological responses to TNT soil contamination, Plant and Soil, 329(1–2):239248.Google Scholar
Nauš, J., Klinkovský, T. and Korčáková, B. (1993), A model of one chloroplast in a cell for evaluation of reabsorption in the fluorescence spectrum, Acta Universitatis Palackianae Olomucensis, 111:4760.Google Scholar
Nauš, J., Klinkovský, T., Ilik, P. and Cikanek, D. (1994), Model studies of chlorophyll fluorescence reabsorption at chloroplast level under different exciting conditions, Photosynthesis Research, 40(1):6774.Google Scholar
Neill, S. and Gould, K.S. (1999), Optical properties of leaves in relation to anthocyanin concentration and distribution, Canadian Journal of Botany, 77(12):17771782.Google Scholar
Neinhuis, C. and Barthlott, W. (1997), Characterization and distribution of water-repellent, self-cleaning plant surfaces, Annals of Botany, 79(6):667677.Google Scholar
Neitzke, M. and Therburg, A. (2003), Seasonal changes in UV-B absorption in beech leaves (Fagus sylvatica L.) along an elevation gradient, Forstwissenschaftliches Centralblatt, 122(1):121.Google Scholar
Nelson, V.N., Gjerstad, D.H. and Glover, G.R. (1986), Determining nitrogen status of young loblolly pine by leaf reflectance, Tree Physiology, 1(3):333339.Google Scholar
Neto, A.J.S., Lopes, D.C. and Borges, J.C.F. (2017), Assessment of photosynthetic pigment and water contents in intact sunflower plants from spectral indices, Agriculture, 7:8.Google Scholar
Neuwirthová, E., Lhotáková, Z. and Albrechtová, J. (2017), The effect of leaf stacking on leaf reflectance and vegetation indices measured by contact probe during the season, Sensors, 17(6):1202.Google Scholar
Newnham, G.J. and Burt, T. (2001), Validation of a leaf reflectance and transmittance model for three agricultural crop species, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’01), Sydney, Australia, 9–13 July 2001, IEEE, Vol. 7, pp. 29762978.Google Scholar
Newton, I. (1704), Optics : or a Treatise of the Reflexions, Refractions Inflexions and Colours of Light, W. Innys, London, 382 pages.Google Scholar
Ngo, V.D., Ryu, D.K., Chung, S.O., Park, S.U., Kim, S.J. and Park, J.T. (2013), Comparison of optical reflectance spectrum at blade and vein parts of cabbage and kale leaves, CNU Journal of Agricultural Science, 40(2):163167.Google Scholar
Nicodemus, F.E. (1968), Emissivity of isothermal spherical cavity with gray Lambertian walls, Applied Optics, 7(7):13591362.Google Scholar
Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W. and Limperis, T. (1977), Geometrical Consideration and Nomenclature for Reflectance, NBS Monograph, October 1977, NBS MN-160, 52 pages.Google Scholar
Nicoletti, S. and Adams, A.J. (2000), Optical transmittance of a rice leaf via ray tracing, Journal of the Arkansas Academy of Science, 54:8187.Google Scholar
Niinemets, Ü. (1999), Components of leaf dry mass per area – thickness and density – alter leaf photosynthetic capacity in reverse directions in woody plants, New Phytologist, 144(1):3547.Google Scholar
Niklas, K.J. (1992), Plant Biomechanics: An Engineering Approach to Plant Form and Function, University of Chicago Press, Chicago, IL, 622 pages.Google Scholar
Nikolayev, V.S., Sibille, P., and Beysens, D.A. (1998), Coherent light transmission by a dew pattern, Optics Communication, 150(1–6):263269.Google Scholar
Nilson, T. and Kuusk, A. (1989), A reflectance model for the homogeneous plant canopy and its inversion, Remote Sensing of Environment, 27(2):157167.Google Scholar
Nishida, K., Kosugi, Y. and Ohte, N. (2000), Spectral reflectance, photosynthesis, and water deficit stress of tree leaves, Journal of the Remote Sensing Society of Japan, 20 (3):230240 (in Japanese).Google Scholar
Nishio, J.N., Sun, J. and Vogelmann, T.C. (1993), Carbon fixation gradients across spinach leaves do not follow internal light gradients, The Plant Cell, 5(8):953961.Google Scholar
Nissen, M., Shcherbakov, D., Heyer, A., Brümmer, F. and Schill, R.O. (2015), Behaviour of the platehelminth Symsagittifera roscoffensis under different light conditions and the consequences for the symbiotic algae Tetraselmis convolutae, Journal of Experimental Biology, 218(11):16931698.Google Scholar
Niu, Y., Chen, G., Peng, D.L., Song, B., Yang, Y., Li, Z.M. et al. (2014), Grey leaves in an alpine plant: a cryptic colouration to avoid attack? New Phytologist, 203(3):953963.Google Scholar
Niu, Y. and Su, H. (2014), Alpine scree plants benefit from cryptic coloration with limited cost, Plant Signaling & Behavior, 9(9):e29698.Google Scholar
Niu, Y., Chen, Z., Stevens, M. and Sun, H. (2017), Divergence in cryptic leaf colour provides local camouflage in an alpine plant, Proceedings of the Royal Society of London. Series B, 284(1864):20171654.Google Scholar
Niyogi, K.K., Björkman, O. and Grossman, A.R. (1997), The roles of specific xanthophylls in photoprotection, Proceedings of the National Academy of Sciences, 94(25):1416214167.Google Scholar
Nobel, P.S. (2009), Physicochemical and Environmental Plant Physiology, Academic Press, 600 pages.Google Scholar
Noble, S.D. and Crowe, T.G. (2001a), Background effects on apparent leaf reflectance, in Proc. ASAE Meeting, ASAE, Guelph, ON, 8–11 July 2001, 011177.Google Scholar
Noble, S.D. and Crowe, T.G. (2001b), Plant discrimination based on leaf reflectance, in Proc. 2001 ASAE Annual Meeting, ASAE, Sacramento, CA, 30 July–1 August 2001, 011150.Google Scholar
Noble, S.D. and Crowe, T.G. (2005), Analysis of crop and weed leaf diffuse reflectance spectra, Transactions of the ASAE, 48(6):23792387.Google Scholar
Noble, S.D. and Crowe, T.G. (2007), Sample holder and methodology for measuring the reflectance and transmittance of narrow-leaf samples, Applied Optics, 46 (22):49684976.Google Scholar
Nocera, D.G. (2012), The artificial leaf, Accounts of Chemical Research, 45(5):767776.Google Scholar
Noda, H.M., Motohka, T., Murakami, K., Muraoka, H. and Nasahara, K.N. (2013), Accurate measurement of optical properties of narrow leaves and conifer needles with a typical integrating sphere and spectroradiometer, Plant, Cell & Environment, 36(10):19031909.Google Scholar
Noomen, M.F., Skidmore, A.K., van der Meer, F.D. and Prins, H.H.T. (2006), Continuum removed band depth analysis for detecting the effects of natural gas, methane and ethane on maize reflectance, Remote Sensing of Environment, 105(3):262270.Google Scholar
Noomen, M.F. and Skidmore, A.K. (2009), The effects of high soil CO2 concentrations on leaf reflectance of maize plants, International Journal of Remote Sensing, 30(2):481497.Google Scholar
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J.P., Munck, L., and Engelsen, S.B. (2000), Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy, Applied Spectroscopy, 54(3):413419.Google Scholar
Norris, K.H., Barnes, R.F., Moore, J.E. and Shenk, J.S. (1976), Predicting forage quality by infrared reflectance spectroscopy, Journal of Animal Science, 43(4):889897.Google Scholar
Nostell, P., Roos, A. and Rönnow, D. (1999), Single-beam integrating sphere spectrophotometer for reflectance and transmittance measurements versus angle of incidence in the solar wavelength range on diffuse and specular samples, Review of Scientific Instruments, 70:24812494.Google Scholar
Ntefidou, M. and Manetas, Y. (1996), Optical properties of hairs during the early stages of leaf development in Platanus orientalis, Australian Journal of Plant Physiology, 23(4):535538.Google Scholar
Nybakken, L., Aubert, S. and Bilger, W. (2004a), Epidermal UV-screening of arctic and alpine plants along a latitudinal gradient in Europe, Polar Biology, 27(7):391398.Google Scholar
Nybakken, L., Bilger, W., Johanson, U., Björn, L.O., Zielke, M. and Solheim, B. (2004b), Epidermal UV-screening in vascular plants from Svalbard (Norwegian Artic), Polar Biology, 27(7):383390.Google Scholar
Nybakken, L. and Bilger, W. (2007), Effects of enhanced UV-B radiation and epidermal UV screening in arctic and alpine plants, in Arctic Alpine Ecosystems and People in a Changing Environment (Ørbæk, J.B., Kallenborn, R., Tombre, I., Hegseth, E.N., Falk-Petersen, S. and Hoel, A.H., Eds), Springer-Verlag, Berlin, pp. 195209.Google Scholar
Oerke, E.C., Steiner, U., Dehne, H.W. and Lindenthal, M. (2006), Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions, Journal of Experimental Botany, 57(9):21212132.Google Scholar
Oerke, E.C. and Steiner, U. (2010), Potential of digital thermography for disease control, in Precision Crop Protection – The Challenge and Use of Heterogeneity (Oerke, E.C., Gerhards, R., Menz, G. and Sikora, R.A., Eds), Springer-Verlag, Berlin, pp. 167182.Google Scholar
Oerke, E.C., Fröhling, P. and Steiner, U. (2011), Thermographic assessment of scab disease on apple leaves, Precision Agriculture, 12(5):699715.Google Scholar
Ögren, E. and Evans, J.R. (1993), Photosynthetic light-response curves. I: The influence of CO2 partial pressure and leaf inversion, Planta, 189(2):182190.Google Scholar
Oh, Y. and Hong, J.Y. (2007), Re-examination of analytical models for microwave scattering from deciduous leaves, IET Microwaves, Antennas & Propagation, 1(3):617623.Google Scholar
Okayama, H. (1996), How different are the indicatrixes of the leaves of various woody plant species? Applied Optics, 35(18):32503254.Google Scholar
Okayama, H. and Li, C. (2007), Evaluation of the surface of objects by use of Minnaert constants, in Proc. Remote Sensing for Agriculture, Ecosystems, and Hydrology IX (Neale C.M.U., Owe M. and D’Urso G., Eds), Florence, Italy, 19 September 2007, SPIE, Vol. 6742, 674204.Google Scholar
Olf, H.G. (1988), Stokes’s pile of plates revisited, Journal of the Optical Society of America, 5(10):16201625.Google Scholar
Olioso, A., Sòria, G., Sobrino, J. and Duchemin, B. (2007), Evidence of low land surface thermal infrared emissivity in the presence of dry vegetation, IEEE Geoscience and Remote Sensing Letters, 4(1):112116.Google Scholar
Ollinger, S.V., Smith, M.L., Martin, M.E., Hallett, R.A., Goodale, C.L. and Aber, J.D. (2002), Regional variation in foliar chemistry and N cycling among forests of diverse history and composition, Ecology, 83(2):339355.Google Scholar
Ollinger, S.V., Richardson, A.D., Martin, M.E., Hollinger, D.Y., Frolking, S.E., Reich, P.B., et al. (2008), Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks, Proceedings of the National Academy of Sciences of the United States of America, 105(49):1933619341.Google Scholar
Olson, C.E. and Good, R.E. (1962), Seasonal changes in light reflectance from forest vegetation, Photogrammetric Engineering, 28(3):107114.Google Scholar
Omarov, T.B. and Tashenov, B.T. (2005), Tikhov’s astrobotany as a prelude to modern astrobiology. in Perspectives in Astrobiology (Hoover, R.B., Rozanov, A.Y. and Paepeet, R.R., Eds), IOS Press, pp. 8687.Google Scholar
Omasa, K., Abo, F., Hashimoto, Y. and Aiga, I. (1980a), Evaluation of air pollution injury to plants by image processing, Research Report from the National Institute for Environmental Studies, 11: 249254.Google Scholar
Omasa, K., Abo, F., Hashimoto, Y. and Aiga, I. (1980b), Measurement of the thermal pattern of plant leaves under fumigation with air pollutant, Research Report from the National Institute for Environmental Studies,11: 239247.Google Scholar
Ono, K., Hiraide, M. and Amari, M. (2003), Determination of lignin, holocellulose, and organic solvent extractives in fresh leaf, litterfall, and organic material on forest floor using near-infrared reflectance spectroscopy, Journal of Forest Research, 8(3):191198.Google Scholar
Ordoñez, C., Martínez, J., Matías, J.M., Reyes, A.N. and Rodríguez-Pérez, J.R. (2010), Functional statistical techniques applied to vine leaf water content determination, Mathematical and Computer Modelling, 52(7–8):11161122.Google Scholar
Ordóñez, C., Rodríguez-Pérez, J.R., Moreira, J.J. and Sanz, E. (2013), Using hyperspectral spectrometry and functional models to characterize vine-leaf composition, IEEE Transactions on Geoscience and Remote Sensing, 51(5):26102618.Google Scholar
Oren, M. and Nayar, S.K. (1995), Generalization of the Lambertian model and implications for machine vision, International Journal of Computer Vision, 14(3):227251.Google Scholar
Osborne, B.A. and Raven, J.A. (1986), Light absorption by plants and its implications for photosynthesis, Biological Reviews, 61(1):161.Google Scholar
Osnas, J.L., Lichstein, J.W., Reich, P.B. and Pacala, S.W. (2013), Global leaf trait relationships: mass, area, and the leaf economics spectrum, Science, 340(6133):741744.Google Scholar
Osorio, S. and Bossomaier, T.R.J. (1992), Human cone-pigment spectral sensitivities and the reflectances of natural surfaces, Biological Cybernetics, 67:217222.Google Scholar
Osorio, D. and Vorobyev, M. (1996), Colour vision as an adaptation to frugivory in primates, Proceedings of the Royal Society of London. Series B, Biological Sciences, 263(1370):593599.Google Scholar
Otterman, J., Brakke, T. and Smith, J. (1995), Effects of leaf-transmittance versus leaf-reflectance on bidirectional scattering from canopy/soil surface: an analytical study, Remote Sensing of Environment, 54(1):4960.Google Scholar
Ounis, A., Cerovic, Z.G., Briantais, J.M. and Moya, I. (2001), Dual excitation FLIDAR for the estimation of epidermal UV absorption in leaves and canopies, Remote Sensing of Environment, 76(1):3348.Google Scholar
Ourcival, J.M., Joffre, R. and Rambal, S. (1999), Exploring the relationships between reflectance and anatomical and biochemical properties of Quercus ilex leaves, New Phytologist, 143(2):351364.Google Scholar
Overton, T.K. (1969), Camouflage colours, Journal of the Society of Dyers and Colourists, 85(4):152154.Google Scholar
Pachepsky, L.B. and Acock, B. (1996), A model 2DLEAF of leaf gas exchange: development, validation, and ecological application, Ecological Modelling, 93(1–3):118.Google Scholar
Pachepsky, L.B. and Acock, B. (1998), Effects of leaf anatomy on hypostomatous leaf gas exchange: a theoretical study with the 2DLEAF model, Biotronics, 27:114.Google Scholar
Pacumbaba, R.O. and Beyl, C.A. (2011), Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies, Advances in Space Research, 48(1):3242.Google Scholar
Paillotin, G., Dobek, A., Breton, J., Leibl, W. and Trissl, H.W. (1993), Why does the light-gradient photovoltage from photosynthetic organelles show a wavelength-dependent polarity? Biophysical Journal, 65(1):379385.Google Scholar
Paillotin, G., Leibl, W., Gapinski, J., Breton, J. and Dobek, A. (1998), Light gradients in spherical photosynthetic vesicles, Biophysical Journal, 75(1):124133.Google Scholar
Palamaryuk, V.E. and Guminetskii, S.G. (1968), Dynamics of the change in spectral coefficients of plant leaves under illumination, Journal of Applied Spectroscopy, 9 (1):743745 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 9(1):152155, 1968).Google Scholar
Palmer, J.M. (1997), The measurement of transmission, absorption, emission, and reflection, in Handbook of Optics, American Institute of Physics, New York, Vol. II, pp. 25.125.25.Google Scholar
Palmer, T. (1877a), The various changes caused on the spectrum by different vegetable colouring matters, The Monthly Microscopical Journal, 17(5):225235.Google Scholar
Palmer, T. (1877b), Vegetable coloring matters, Scientific American, 3:12141214.Google Scholar
Pan, G. and Narayanan, R.M. (2002), Comparison of microwave scattering models for leaf, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’02), Toronto, ON, 24–28 June 2002, IEEE, Vol. 1, pp. 653655.Google Scholar
Panigrahy, S., Kumar, T. and Manjunath, K.R. (2012), Hyperspectral leaf signature as an added dimension for species discrimination: case study of four tropical mangroves, Wetlands Ecology and Management, 20(2):101110.Google Scholar
Pannell, J.R. (2014), Leaf mimicry: chameleon-like leaves in a Patagonian vine, Current Biology, 24(9):R357R359.Google Scholar
Paradiso, R., Meinen, E., Snel, J.F.H., De Visser, P., Van Ieperen, W., Hogewoning, S.W. et al. (2011), Spectral dependence of photosynthesis and light absorptance in single leaves and canopy in rose, Scientia Horticulturae, 127(4):548554.Google Scholar
Park, Y.I., Chow, W.S. and Anderson, J.M. (1996), Chloroplast movement in the shade plant Tradescantia albiflora helps protect photosystem II against light stress, Plant Physiology, 111(3):867875.Google Scholar
Parkhurst, D.F. (1976), Effects of Verbascum thapsus leaf hairs on heat and mass transfer: a reassessment, New Phytologist, 76(3):453457.Google Scholar
Parkhurst, D.F. (1982), Stereological methods for measuring internal leaf structure variables, American Journal of Botany, 69(1):3139.Google Scholar
Parkhurst, D.F. (1986), Internal leaf structure: a three-dimensional perspective, in On the Economy of Plant Form and Function (Givnish, T.J., Ed.), Cambridge University Press, Cambridge, pp. 215249.Google Scholar
Parmesan, C. (2006), Ecological and evolutionary responses to recent climate change, Annual Review of Ecology, Evolution, and Systematics, 37:637669.Google Scholar
Patil, S.B. and Bodhe, S.K. (2011), Leaf disease severity measurement using image processing, International Journal of Engineering and Technology, 3(5):297301.Google Scholar
Pavan, G., Jacquemoud, S., Bidel, L., François, C., de Rosny, G., Rambaut, J.P. et al. (2004), RAMIS: a new portable field radiometer to estimate leaf biochemical content, in Proc. 7th International Conference on Precision Agriculture and Other Precision Resources Management, Minneapolis, MN, 25–28 July 2004, 15 pages.Google Scholar
Pearman, G.I. (1966), The reflection of visible radiation from leaves of some Western Australian species, Australian Journal of Biological Sciences, 19(1):97103.Google Scholar
Pedrini, A. (1991), Methodology for Fluorescence Yield Determination of Vegetation, Unit for Advanced Techniques, Joint Research Centre, Ispra, Italy, 45 pages.Google Scholar
Pedrós, R., Moya, I., Goulas, Y. and Jacquemoud, S. (2008), Chlorophyll fluorescence emission spectrum inside a leaf, Photochemical & Photobiological Sciences, 7(4):498502.Google Scholar
Pedrós, R., Goulas, Y., Jacquemoud, S., Louis, J. and Moya, I. (2010), FluorMODleaf: a new leaf fluorescence emission model based on the PROSPECT model, Remote Sensing of Environment, 114(1):155167.Google Scholar
Pelletier, J. and Caventou, J. (1817), Notice sur la matière verte des feuilles, Journal de Pharmacie et des Sciences Accessoires, 3:486491.Google Scholar
Peltoniemi, J.I., Gritsevich, M. and Puttonen, E. (2015), Reflectance and polarization characteristics of various vegetation types, in Light Scattering Reviews 9 (Kokhanovsky, A., Ed), Springer-Verlag, Berlin, pp. 257293.Google Scholar
Pengra, B.W., Johnston, C.A. and Loveland, T.R. (2007), Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor, Remote Sensing of Environment, 108(1):7481.Google Scholar
Penny, S.S. and Willan, R.C. (2014), Description of a new species of giant clam (Bivalvia: Tridacnidae) from Ningaloo Reef, Western Australia, Molluscan Research, 34(4):201211.Google Scholar
Peñuelas, J., Filella, I., Biel, C., Serrano, L. and Savé, R. (1993), The reflectance at the 950–970 nm region as an indicator of plant water status, International Journal of Remote Sensing, 14(10):18871905.Google Scholar
Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J. and Field, C.B. (1994), Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves, Remote Sensing of Environment, 48(2):135146.Google Scholar
Peñuelas, J., Filella, I. and Gamon, J.A. (1995), Assessment of photosynthetic radiation-use efficiency with spectral reflectance, New Phytologist, 131(3):291296.Google Scholar
Peñuelas, J., Llusia, J., Piñol, J. and Filella, I. (1997), Photochemical reflectance index and leaf photosynthetic radiation-use-efficiency assessment in Mediterranean trees, International Journal of Remote Sensing, 18(13):28632868.Google Scholar
Peñuelas, J. and Filella, I. (1998), Visible and near-infrared reflectance techniques for diagnosing plant physiological status, Trends in Plant Science, 3(4):151156.Google Scholar
Peñuelas, J. and Inoue, Y. (1999), Reflectance indices indicative of changes in water and pigment contents of peanut and weat leaves, Photosynthetica, 36(3):355360.Google Scholar
Peñuelas, J., Munné-Bosch, S., Llusià, J. and Filella, I. (2004), Leaf reflectance and photo- and antioxidant protection in field-grown summer-stressed Phillyrea angustifolia. Optical signals of oxidative stress? New Phytologist, 162(1):115124.Google Scholar
Perevertun, M.P. (1960), Optical properties of certain plant species in the infrared region of the spectrum in transmitted light, Trudy sektora astrobotaniki, 8: 5973 (English translation).Google Scholar
Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., et al. (2013), New handbook for standardised measurement of plant functional traits worldwide, Australian Journal of Botany, 61(3):167234.Google Scholar
Peterson, D.L., Aber, J.D., Matson, P.A., et al. (1988), Remote sensing of forest canopy and leaf biochemical contents, Remote Sensing of Environment, 24(1):85108.Google Scholar
Petisco, C., Garcia-Criado, B., Vazquez de Aldana, B.R., Zabalgogeazcoa, I., Mediavilla, S. and Garcia-Ciudad, A. (2005), Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species, Analytical and Bioanalytical Chemistry, 382(2):458465.Google Scholar
Peynado, A., Gausman, H.W., Escobar, D.E., Rodriguez, R.R. and Garza, M.V. (1979), Evidence of cell membrane injury detected by reflectance measurements, Cryobiology, 16(1):6368.Google Scholar
Peyrat, A., Terraz, O., Merillou, S. and Galin, E. (2008), Generating vast varieties of realistic leaves with parametric 2Gmap L-systems, The Visual Computer, 24(7–9):807816.Google Scholar
Pfeiffer, H.G. and Liebhafsky, H.A. (1951), The origin of Beer’s law, Journal of Chemical Education, 28(3):123125.Google Scholar
Pfündel, E.E., Agati, G. and Cerovic, Z.G. (2006), Optical properties of plant surfaces, in Biology of the Plant Cuticle (Riederer, M. and Müller, C., Eds), Blackwell Publishing, pp. 216249.Google Scholar
Phan, C.T., Brach, E.J. and Jasmin, J.J. (1979), Studies of the detection of lettuce maturity: anatomical observations and reflectance measurements in the visible range (350–650 nm), Canadian Journal of Plant Science, 59(4):10671075.Google Scholar
Philips-Invernizzi, B., Dupont, D. and Cazé, C. (2001), Biographical review for reflectance of diffusing media, Optical Engineering, 40(6):10821092.Google Scholar
Phipson, M.T.L. (1858), Sur la couleur des feuilles, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 47:912913.Google Scholar
Pickering, J.W., Moes, C.J.M., Sterenborg, H.J.C.M., Prahl, S.A. and van Gemert, M.J.C. (1992), Two integrating spheres with an intervening scattering sample, Journal of the Optical Society of America, 9(4):621631.Google Scholar
Pickering, J.W., Prahl, S.A., van Wieringen, N., Beek, J.F., Sterenborg, H.J.C.M. and van Gemert, M.J.C. (1993), Double-integrating sphere system for measuring the optical properties of tissue, Applied Optics, 32(4):399410.Google Scholar
Pieruschka, R., Huber, G. and Berry, J.A. (2010), Control of transpiration by radiation, Proceedings of the National Academy of Sciences of the United States of America, 107(30):1337213377.Google Scholar
Pietrzykowski, E., Stone, C., Pinkard, E. and Mohammed, C. (2006), Effects of Mycosphaerella leaf disease on the spectral reflectance properties of juvenile Eucalyptus globulus foliage, Forest Pathology, 36(5):334348.Google Scholar
Pilarski, J. (1999), Gradient of photosynthetic pigments in the bark and leaves of lilac (Syringa vulgaris L.), Acta Physiologiae Plantarum, 21(4):365373.Google Scholar
Pilarski, J. and Rajba, S. (2004), Measurement of light gradient in plant organs with a fiber optic microprobe, Acta Physiologiae Plantarum, 26(4):405410.Google Scholar
Pincebourde, S. and Casas, J. (2006), Leaf miner-induced changes in leaf transmittance cause variations in insect respiration rates, Journal of Insect Physiology, 52(2):194201.Google Scholar
Pinzón, J.E., Ustin, S.L., Castañeda, C.M. and Smith, M.O. (1998), Investigation of leaf biochemistry by hierarchical foreground/background analysis, IEEE Transactions on Geoscience and Remote Sensing, 36(6):19131927.Google Scholar
Platt, J.R. (1956), Radiation Biology (Hollaender, A., Ed), McGraw-Hill, Vol. 3, pp. 71123.Google Scholar
Pliny the Elder (1855), Natural History (Bostock, J. and Riley, H.T., translators), H.G.Bohn, London, 536 pages.Google Scholar
Polischuk, V.P., Shadchina, T.M., Kompanetz, T.I., Budzanivskaya, I.G., Boyko, A.L. and Sozinov, A.A. (1997), Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection, Archives of Phytopathology and Plant Protection, 31(1):115119.Google Scholar
Polívka, T., Herek, J.L., Zigmantas, D., Åkerlund, H.E. and Sundström, V. (1999), Direct observation of the (forbidden) S1 state in carotenoids, Proceedings of the National Academy of Sciences of the United States of America, 96(9):49144917.Google Scholar
Ponzoni, F.J. and Gonçalves, J.L. (1999), Spectral features associated with nitrogen, phosphorous, and potassium deficiencies in Eucalyptus saligna seedling leaves, International Journal of Remote Sensing, 20(11):22492264.Google Scholar
Poorter, H., Niinemets, Ü., Poorter, L., Wright, I.J. and Villar, R. (2009), Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis, New Phytologist, 182(3):565588.Google Scholar
Poorter, L., Oberbauer, S.E. and Clark, D.B. (1995), Leaf optical properties along a vertical gradient in a tropical rain forest canopy in Costa Rica, American Journal of Botany, 82(10):12571263.Google Scholar
Poorter, L., Kwant, R., Hernandez, R., Medina, E. and Werger, M.J.A. (2000), Leaf optical properties in Venezuelan cloud forest trees, Tree Physiology, 20(8):519526.Google Scholar
Poschlod, P., Kleyer, M., Jackel, A.K., Dannemann, A. and Tackenberg, O. (2003), BIOPOP – A database of plant traits and internet application for nature conservation, Folia Geobotanica, 38(3):263271.Google Scholar
Pospergelis, M.M. (1969), Spectroscopic measurements of the four Stokes parameters for light scattered by natural objects, Soviet Astronomy, 12(6):973977.Google Scholar
Poulson, M.E. and Vogelmann, T.C. (1990), Epidermal focussing and effects upon photosynthetic light-harvesting in leaves of Oxalis, Plant, Cell & Environment, 13(8):803811.Google Scholar
Poulson, M.E. and DeLucia, E.H. (1993), Photosynthetic and structural acclimation to light direction in vertical leaves of Silphium terebinthinaceum, Oecologia, 95(3):393400.Google Scholar
Pourcel, L., Routaboul, J.M., Cheynier, V., Lepiniec, L. and Debeaujon, I. (2007), Flavonoid oxidation in plants: from biochemical properties to physiological functions, Trends in Plant Science, 12(1):2936.Google Scholar
Powell, K., Lamb, D., Barret, D., Blanchfield, A., Held, A., Renzullo, L., et al. (2006), Early detection of phylloxera in grapevines (Vitis vinifera) – A test-bed for spectro-optical and chemical fingerprinting of pest-induced grapevine stress, Cooperative Research Centre for Viticulture, Australia, April 2006, 153 pages.Google Scholar
Powles, S.B. (1984), Photoinhibition of photosynthesis induced by visible light, Annual Review of Plant Physiology, 35:1544Google Scholar
Prahl, S. (2001), Optical Absorption of Water. Oregon Medical Laser Center. http://omlc.ogi.edu/spectra/water/Google Scholar
Prance, G.T. and Sandved, K.B. (1985), Leaves. The formation, Characteristics, and Uses of Hundreds of Leaves Found in all Parts of the World, Thames & Hudson, London. 244 pages.Google Scholar
Prioul, J.L. and Chartier, P. (1977), Partitioning of transfer and carboxylation components of intracellular resistance to photosynthetic CO2 fixation: a critical analysis of the method used, Annals of Botany, 41(174):789800.Google Scholar
Priestley, J. (1774), Experiments and Observations on Different Kinds of Air, Vol, I. Printed for J Johnson, London, 324 pages.Google Scholar
Prokopy, R.P. and Owens, E.D. (1983), Visual detection of plants by herbivorous insects, Annual Review Entomology, 28:337364.Google Scholar
Prokopy, R.P., Collier, R.H. and Finch, S. (1983), Visual detection of host plants by cabbage root flies, Entomologia Experimentalis et Applicata, 34(1):8589.Google Scholar
Prusinkiewicz, P. and Lindenmayer, A. (1990), The Algorithmic Beauty of Plants, Springer-Verlag, New York, 228 pages.Google Scholar
Pu, R., Ge, S., Kelly, N.M. and Gong, P. (2002), Correlation analysis of hyperspectral absorption features with the water status of coast live oak leaves, in Proc. Imaging Spectrometry VII (Descour M.R. and Shen S.S., Eds), San Diego, CA, 1–3 August 2001, SPIE, Vol. 4480, pp. 147153.Google Scholar
Pu, R., Ge, S., Kelly, N.M. and Gong, P. (2003), Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves, International Journal of Remote Sensing, 24(9):17991810.Google Scholar
Pu, R., Foschi, L. and Gong, P. (2004), Spectral feature analysis for assessment of water status and health level in coast live oak (Quercus agrifolia) leaves, International Journal of Remote Sensing, 25(20):42674286.Google Scholar
Pu, R., Kelly, M., Chen, Q. and Gong, P. (2008), Spectroscopic determination of health levels of coast live oak (Quercus agrifolia) leaves, Geocarto International, 23(1):320.Google Scholar
Purcell, D.E., O’Shea, M.G., Johnson, R.A. and Kokot, S. (2009), Near-infrared spectroscopy for the prediction of disease ratings for Fiji leaf gall in sugarcane clones, Applied Spectroscopy, 63(4):450457.Google Scholar
Pydipati, R., Burks, T.F. and Lee, W.S. (2005), Statistical and neural network classifiers for Citrus disease detection using machine vision, Transactions of the ASAE, 48(5):20072014.Google Scholar
Pydipati, R., Burks, T.F. and Lee, W.S. (2006), Identification of Citrus disease using color texture features and discriminant analysis, Computers and Electronics in Agriculture, 52(1–2):4959.Google Scholar
Qi, Y., Bai, S., Vogelmann, T.C., Heisler, G.M. and Qin, J. (2002), Methodology for comprehensive evaluation of UV-B tolerance in trees, in Proc. Ultraviolet Ground- and Space-based Measurements, Models, and Effects (Slusser J.R., Herman J.R. & Gao W., Eds), San Diego, CA, 29 July 2001, SPIE, Vol. 4482, pp. 367380.Google Scholar
Qi, Y., Bai, S., Gao, W. and Heisler, G.M. (2003a), Intra- and inter-specific comparisons of leaf UV-B absorbing-compound concentration of southern broadleaf trees in the United States, in Proc. Ultraviolet Ground- and Space-based Measurements, Models and Effects II (Gao W., Herman J.R., Shi G., Shibasaki K. and Slusser J.R., Eds), Hangzhou, China, SPIE, Vol. 4896, pp. 120129.Google Scholar
Qi, Y., Bai, S. and Heisler, G.M. (2003b), Changes in ultraviolet-B and visible optical properties and absorbing pigment concentrations in pecan leaves during a growing season, Agricultural and Forest Meteorology, 120(1–4):229240.Google Scholar
Qi, Y., Bai, S., Vogelmann, T.C. and Heisler, G.M. (2003c), Penetration of UV-A, UV-B, blue and red light into leaf tissues of pecan measured by a fiber optic microprobe system, in Proc. Ultraviolet Ground- and Space-based Measurements, Models, and Effects III (Slusser J.R., Herman J.R. and Gao W., Eds), San Diego, CA, SPIE, Vol. 5156, pp. 281290.Google Scholar
Qi, Y., Heisler, G.M., Gao, W., Vogelmann, T.C. and Bai, S. (2010), Characteristics of UV-B radiation tolerance in broadleaf trees in southern USA, in UV Radiation in Global Climate Change (Gao, W., Slusser, J.R. and Schmoldt, D.L., Eds), Springer–Verlag, Berlin, pp. 509530.Google Scholar
Qin, J.L., Rundquist, D., Gitelson, A., Steele, M., Harkins, C. and Briles, R. (2010), A non-linear model for measuring grapevine leaf thickness by means of red-edge/near-infrared spectral reflectance, Acta Ecologica Sinica, 30(6):297303.Google Scholar
Qin, R., Xu, G., Guo, L., Jiang, Y. and Ding, R. (2012), Preparation and characterization of a novel poly(urea-formaldehyde) microcapsules with similar reflectance spectrum to leaves in the UV-Vis-NIR region of 300–2500 nm, Materials Chemistry and Physics, 136(2–3):737743.Google Scholar
Qu, Y., Zhu, Y. and Ge, X. (2014), A reflectance spectra model of heavy metal stressed leaves: advances in the PROSPECT model adding specific absorption coefficients of heavy metal ion, in Proc. 35th International Symposium on Remote Sensing of Environment, Beijing, China, 22–26 April 2013, 012041.Google Scholar
Quickenden, T.I., Freeman, C.G. and Litjens, R.A. (2000), Some comments on the paper by Edward S. Fry on the visible and near-ultraviolet absorption spectrum of liquid water, Applied Optics, 39(16):27402742.Google Scholar
Rabideau, G.S., French, C.S. and Holt, A.S. (1946), The absorption and reflection spectra of leaves, chloroplasts suspensions, and chloroplast fragments as measured in the Ulbricht sphere, American Journal of Botany, 33(10):769777.Google Scholar
Rabinowitch, E.I. (1951), Light absorption by pigments in the living cell, in Photosynthesis and Related Processes (Rabinowitch, E.I., Ed.), Interscience Publishers, New York, pp. 672739.Google Scholar
Rahman, H., Pinty, B. and Verstraete, M.M. (1993), Coupled surface-atmosphere reflectance (CSAR) model. 2. Semiempirical surface model usable with NOAA Advanced Very High Resolution Radiometer Data, Journal of Geophysical Research, 98 (D11):20,791–20,801.Google Scholar
Raines, G.L. and Canney, F.C. (1980), Vegetation and geology, in Remote Sensing in Geology (Siegal, B.S. and Gillespie, A.R., Eds), John Wiley & Sons, pp. 365380.Google Scholar
Ramalingam, N., Ling, P.P. and Derksen, R.C. (2001), Leaf surface moisture detection by multi-spectral imaging, in Proc. 2001 ASAE Annual Meeting, Sacramento, CA, 30 July–1 August 2001, ASAE, 013004.Google Scholar
Ramalingam, N., Ling, P.P. and Derksen, R.C. (2003), Leaf surface wetness detection using ground based multispectral imaging, in Proc. 2003 ASAE Annual Meeting, Las Vegas, NV, 27–30 July 2003, ASAE, 033130.Google Scholar
Ramos, M.E. and Lagorio, M.G. (2004), True fluorescence spectra of leaves, Photochemical & Photobiological Sciences, 3(11–12):10631066.Google Scholar
Ramsey, E.W. and Rangoonwala, A. (2004), Remote sensing and the optical properties of the narrow cylindrical leaves of Juncus roemerianus, IEEE Transactions on Geoscience and Remote Sensing, 42(5):10641075.Google Scholar
Rapaport, T., Hochberg, U., Shoshany, M., Karnieli, A. and Rachmilevitch, S. (2015), Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment, ISPRS Journal of Photogrammetry and Remote Sensing, 109:8897.Google Scholar
Rathod, P.H., Rossiter, D.G., Noomen, M.F. and van der Meer, F.D. (2013), Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils, International Journal of Phytoremediation, 15(5):405426.Google Scholar
Raunkiaer, C. (1934), The Life Forms of Plants and Statistical Plant Geography, Oxford University Press, New York, 632 pages.Google Scholar
Rausher, M.D. (1992), Natural selection and the evolution of plant-insect interactions, in Insect Chemical Ecology – An Evolutionary Approach (Roitberg, B.D. and Isman, M.B., Eds), Springer, US, 376 pages.Google Scholar
Rautiainen, M., Heiskanen, J., Eklundh, L., Mõttus, M., Lukeš, P. and Stenberg, P. (2010), Ecological applications of physically based remote sensing methods, Scandinavian Journal of Forest Research, 25(4):325339.Google Scholar
Rautiainen, M., Lukeš, P., Homolová, L., Hovi, A., Pisek, J. and Mõttus, M. (2018), Spectral properties of coniferous forests: a review of in situ and laboratory measurements, Remote Sensing, 10(2):207.Google Scholar
Raven, P.N., Jordan, D.L. and Smith, C.E. (2002), Polarized directional reflectance from laurel and mullein leaves, Optical Engineering, 41(5):10021012.Google Scholar
Ravi, J., Lee, S.T., Paulraj, M. and Hernandez, R. (2008), Feasibility of neural network approach in spectral mixture analysis of reflectance spectra, International Journal of Remote Sensing, 29(10):29812992.Google Scholar
Ray, J. (1686), Historia Plantarum, Vol. 1, Clark, London, 862 pages.Google Scholar
Raymond, C.A. and Schimleck, L.R. (2002), Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in Eucalyptus globulus, Canadian Journal of Forest Research, 32(1):170176.Google Scholar
Read, J.J., Tarpley, L., McKiniona, J.M. and Reddyc, K.R. (2002), Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton, Journal of Environmental Quality, 31(5):14421452.Google Scholar
Reale, L., Lai, A., Tucci, A., Poma, A., Faenov, A., Pikuz, T., et al. (2004), Differences in X-ray absorption due to cadmium treatment in Saponaria officinalis leaves, Microscopy Research and Technique, 64(1):2129.Google Scholar
Reale, L., Lai, A., Bellucci, I., Faenov, A., Pikuz, T., Flora, F., et al. (2006), Microradiography as a tool to detect heavy metal uptake in plants for phytoremediation applications, Microscopy Research and Technique, 69(8):666674.Google Scholar
Reale, L., Kaiser, J., Reale, A., Lai, A., Flora, F., Balerna, A., et al. (2008a), Mapping the intake of different elements in vegetal tissues by dual-energy X-ray imaging at DaΦne synchrotron light source, Microscopy Research and Technique, 71(3):179185.Google Scholar
Reale, L., Lai, A., Sighicelli, M., Faenov, A., Pikuz, T., Flora, F., et al. (2008b), Qualitative detection of Mg content in a leaf of Hedera helix by using X-ray radiation from a laser plasma source, Microscopy Research and Technique, 71(6):459468.Google Scholar
Regalado, A. (2010), Reinventing the leaf, Scientific American, 303(4):8689.Google Scholar
Regan, B.C., Julliot, C., Simmen, B., Viénot, F., Charles-Dominique, P. and Mollon, J.D. (2001), Fruits, foliage and the evolution of primate colour vision, Philosophical Transactions of the Royal Society of London. Series B, 356(1407):229283.Google Scholar
Reich, P.B., Walters, M.B. and Ellsworth, D.S. (1997), From tropics to tundra: global convergence in plant functioning, Proceedings of the National Academy of Sciences, 94(25):1373013734.Google Scholar
Reicosky, D.A. and Hanover, J.W. (1978), Physiological effects of surface waxes. I: Light reflectance for glaucous and nonglaucous Picea pungens, Plant Physiology, 62:101104.Google Scholar
Reisig, D. and Godfrey, L. (2007), Spectral response of cotton aphid – (Homoptera: Aphididae) and spider mite – (Acari: Tetranychidae) infested cotton: controlled studies, Environmental Entomology, 36(6):14661474.Google Scholar
Renzullo, L.J., Blanchfield, A.L. and Powell, K.S. (2005), Insights into the early detection of grapevine phylloxera from in situ hyperspectral data, in Proc. ISHS Acta Horticulturae: III International Grapevine Phylloxera Symposium, Fremantle, Australia, Vol. 733, pp. 5974.Google Scholar
Renzullo, L.J., Blanchfield, A.L., Guillermin, R., Powell, K.S. and Held, A.A. (2006a), Comparison of PROSPECT and HPLC estimates of leaf chlorophyll contents in a grapevine stress study, International Journal of Remote Sensing, 27(4):817823.Google Scholar
Renzullo, L.J., Blanchfield, A.L. and Powell, K.S. (2006b), A method of wavelength selection and spectral discrimination of hyperspectral reflectance spectrometry, IEEE Transactions on Geoscience and Remote Sensing, 44(7):19861994.Google Scholar
Reum, D. and Zhang, Q. (2007), Wavelet based multi-spectral image analysis of maize leaf chlorophyll content, Computers and Electronics in Agriculture, 56(1):6071.Google Scholar
Reyna, E. and Badhwar, G.D. (1985), Inclusion of specular reflectance in vegetative canopy models, IEEE Transactions on Geoscience and Remote Sensing, 23(5):731736.Google Scholar
Riaño, D., Ustin, S.L., Usero, L. and Patricio, M.A. (2005a), Estimation of fuel moisture content using neural networks, in Proc. International Work-conference on the Interplay between Natural and Artificial Computation (Mira J. and Alvarez J.R., Eds), Las Palmas de Gran Canaria, Canary Islands, 15–18 June 2005, pp. 489498.Google Scholar
Riaño, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P.J. and Ustin, S.L. (2005b), Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level, IEEE Transactions on Geoscience and Remote Sensing, 43(4):819826.Google Scholar
Ribeiro da Luz, B. (2006), Attenuated total reflectance spectroscopy of plant leaves: a tool for ecological and botanical studies, New Phytologist, 172(2):305318.Google Scholar
Ribeiro da Luz, B. and Crowley, J.K. (2007), Spectral reflectance and emissivity features of broadleaf plants: prospects for remote sensing in the thermal infrared (8.0–14.0 μm), Remote Sensing of Environment, 109(4):393405.Google Scholar
Ribeiro da Luz, B. and Crowley, J.K (2010), Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 μm) imagery, Remote Sensing of Environment, 114(2):404413.Google Scholar
Richards-Kortum, R., Rava, R.P., Fitzmaurice, M., Tong, L.L., Ratliff, N.B., Kramer, J.R. et al. (1989), A one-layer model of laser-induced fluorescence for diagnosis of disease in human tissue: applications to atherosclerosis, IEEE Transactions on Biomedical Engineering, 36(12):12221232.Google Scholar
Richardson, A.D. and Berlyn, G.P. (2002), Spectral reflectance and photosynthetic properties of Betula papyrifera (Betulaceae) leaves along an elevational gradient on Mt Mansfield, Vermont, USA, American Journal of Botany, 89(1):8894.Google Scholar
Richardson, A.D., Duigan, S.P. and Berlyn, G.P. (2002), An evaluation of noninvasive methods to estimate foliar chlorophyll content, New Phytologist, 153(1):185194.Google Scholar
Richardson, A.D., Berlyn, G.P. and Duigan, S.P. (2003a), Reflectance of Alaskan black spruce and white spruce foliage in relation to elevation and latitude, Tree Physiology, 23:537544.Google Scholar
Richardson, A.D., Reeves, J.B. and Gregoire, T.G. (2003b), Multivariate analyses of visible/near infrared (VIS/NIR) absorbance spectra reveal underlying spectral differences among dried, ground conifer needle samples from different growth environments, New Phytologist, 161(1):291301.Google Scholar
Richardson, A.J. and Gausman, H.W. (1982), Reflectance differences between untreated and Mepiquat Chloride-treated, field-grown cotton through a growing season, Remote Sensing of Environment, 12(6):501507.Google Scholar
Richter, J.P. (1970), The Notebooks of Leonardo da Vinci Compiled and Edited from the Original Manuscripts, Vol. I, Dover, 367 pages.Google Scholar
Richter, T. and Fukshansky, L. (1994), Authentic in vivo absorption spectra for chlorophyll in leaves as derived from in situ and in vitro measurements, Photochemistry and photobiology, 59(2):237247.Google Scholar
Richter, T. and Fukshansky, L. (1996a), Optics of a bifacial leaf. 1: A novel combined procedure for deriving the optical parameters, Photochemistry and photobiology, 63(4):507516.Google Scholar
Richter, T. and Fukshansky, L. (1996b), Optics of a bifacial leaf. 2. Light regime as affected by the leaf structure and the light source, Photochemistry and Photobiology, 63(4):517527.Google Scholar
Ridley, M. (2003), Evolution, Wiley-Blackwell, 784 pages.Google Scholar
Riedell, W.E. and Blackmer, T.M. (1999), Leaf reflectance spectra of cereal aphid-damaged wheat, Crop Science, 39(6):18351840.Google Scholar
Riederer, M. and Muller, C. (2006), Biology of the Plant Cuticle, Blackwell Publishing, 456 pp.Google Scholar
Ripple, W.J. (1986), Spectral reflectance relationships to leaf water stress, Photogrammetric Engineering & Remote Sensing, 52(10):16691675.Google Scholar
Ripullone, F., Rivelli, A.R., Baraldi, R., et al. (2011), Effectiveness of the photochemical reflectance index to track photosynthetic activity over a range of forest tree species and plant water statuses, Functional Plant Biology, 38(3):177186.Google Scholar
Rivard, B., Feng, J., Gallie, A. and Sanchez-Azofeifa, A. (2008a), Continuous wavelets for the improved use of spectral libraries and hyperspectral data, Remote Sensing of Environment, 112(6):28502862.Google Scholar
Rivard, B., Sánchez-Azofeifa, G.A., Foley, S. and Calvo-Alvarado, J.C. (2008b), Species classification of tropical tree leaf reflectance and dependence on selection of spectral bands, in Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests (Kalacska, M. and Sánchez-Azofeifa, G.A., Eds), CRC Press, pp. 141159.Google Scholar
Robberecht, R. and Caldwell, M.M. (1978), Leaf epidermal transmittance of ultraviolet radiation and its implications for plant sensitivity to ultraviolet-radiation induced injury, Oecologia, 32(3):277287.Google Scholar
Robberecht, R., Caldwell, M.M. and Billings, W.D. (1980), Leaf ultraviolet optical properties along a latitudinal gradient in the arctic-alpine life zone, Ecology, 61(3):612619.Google Scholar
Robberecht, R. and Caldwell, M.M. (1983), Protective mechanisms and acclimation to solar ultraviolet-B radiation in Oenothera stricta, Plant, Cell & Environment, 6(6):477485.Google Scholar
Robberecht, R. and Caldwell, M.M.(1986), Leaf UV optical properties of Rumex patientia L. and Rumex obtusifolius L. in regard to a protective mechanism against solar UV-B radiation injury, in Stratosphere Ozone Reduction, Solar Ultraviolet Radiation and Plant Life (Worrest, R.C. and Caldwell, M.M., Eds), Springer-Verlag, Berlin, pp. 251259.Google Scholar
Roberts, D.A., Adams, J.B. and Smith, M.O. (1990a), Predicted distribution of visible and near-infrared radiant flux above and below a transmittant leaf, Remote Sensing of Environment, 34(1):117.Google Scholar
Roberts, D.A., Adams, J.B. and Smith, M.O. (1990b), Transmission and scattering of light by leaves: effects on spectral mixtures in Proc. 10th International Geoscience and Remote Sensing Symposium (IGARSS’90), College Park, MD, 20–24 May 1990, IEEE, Vol. 3, pp. 13811384.Google Scholar
Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G. and Green, R.O. (1998a), Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sensing of Environment, 65(3):267279.Google Scholar
Roberts, D.A., Nelson, B.W., Adams, J.B. and Palmer, F. (1998b), Spectral changes with leaf aging in Amazon caatinga, Trees – Structure and Function, 12(6):315325.Google Scholar
Robinson, G.W., Cho, C.H. and Gellene, G.I. (2000), Refractive index mysteries of water, Journal of Physical Chemistry B, 104(30):71797182.Google Scholar
Rock, B.N., Hoshizaki, T. and Miller, J.R. (1988), Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline, Remote Sensing of Environment, 24(1):109127.Google Scholar
Rock, B.N., Williams, D.L., Moss, D.M., Lauten, G.N. and Kim, M. (1994), High-spectral resolution field and laboratory optical reflectance measurements of red spruce and eastern hemlock needles and branches, Remote Sensing of Environment, 47(2):176189.Google Scholar
Rodkaew, Y., Chongstitvatana, P., Siripant, S. and Lursinsap, C. (2004), Modeling plant leaves in marble-patterned colours with particle transportation system, in Proc. 4th International Workshop on Functional-Structural Plant Models (Godin C., Ed), Montpellier, France, 7–11 June 2004, pp. 391397.Google Scholar
Rodriguez, R.R. and Gausman, H.W. (1977), Leaf ultraviolet radiation reflectance, transmittance, and absorptance of ten crop species, Journal of the Rio Grande Valley Horticultural Society, 31:175180.Google Scholar
Rodríguez-Pérez, J.R., Riaño, D., Carlisle, E., Ustin, S. and Smart, D.R. (2007), Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards, American Journal of Enology and Viticulture, 58(3):302317.Google Scholar
Roelofsen, H.D., van Bodegom, P.M., Kooistra, L. and Witte, J.P.M. (2014), Predicting leaf traits of herbaceous species from their spectral characteristics, Ecology and Evolution, 4(6):706719.Google Scholar
Rogge, D., Rivard, B., Deyholos, M.K., Lévesque, J., Ardouin, J.P. and Faust, A.A. (2012), Potential discrimination of toxic industrial chemical effects on poplar, canola and wheat, detectable in optical wavelengths 400–2450 nm, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2):563573.Google Scholar
Romberger, J.A., Hejnowicz, Z. and Hill, J.F. (2004), Plant Structure: Function and Development, Springer-Verlag, Berlin, 524 pages.Google Scholar
Romero, A., Aguado, I., Chuvieco, E. and Yebra, M. (2007), Evaluation of dry foliage matter through normalised indexes and inversion of reflectivity models, in Proc. 6th International Workshop of the European Association of Remote Sensing Laboratories (Gitas I.Z. and Carmona-Moreno C., Eds), Thessaloniki, Greece, 27–29 September 2007, EARSeL, pp. 8790.Google Scholar
Romero, A., Aguado, I. and Yebra, M. (2012), Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion, International Journal of Remote Sensing, 33(2):396414.Google Scholar
Rose, A.H. and Lindquist, O.H. (1994), Insects of Eastern Spruces, Fir and, Hemlock, Canadian Forest Service, Ottawa, 162 pages.Google Scholar
Rose, A.W., Hawkes, H.E. and Webb, J.S. (1980), Geochemistry in Mineral Exploration, Academic Press, 657 pages.Google Scholar
Rosema, A., Verhoef, W., Schroote, J. and Snel, J.F.H. (1991), Simulating fluorescence light-canopy interaction in support of laser-induced fluorescence measurements, Remote Sensing of Environment, 37(2):117130.Google Scholar
Rosevear, M.J., Young, A.J. and Johnson, G.N. (2001), Growth conditions are more important than species origin in determining leaf pigment content of British plant species, Functional Ecology, 15(4):474480.Google Scholar
Ross, J. (1981), The Radiation Regime and Architecture of Plant Stands, Springer, The Netherlands, 420 pages.Google Scholar
Ross, J. and Marshak, A. (1989), The influence of leaf orientation and the specular component of leaf reflectance on the canopy bidirectional reflectance, Remote Sensing of Environment, 27(3):251260.Google Scholar
Ross, J., Meinander, O. and Sulev, M. (1994), Spectral scattering properties of Scots pine shoots, in Proc. 14th International Geoscience and Remote Sensing Symposium (IGARSS’94), Pasadena, CA, 8–12 August 1994, IEEE, Vol. 3, pp. 14511454.Google Scholar
Rosso, P.H., Pushnik, J.C., Lay, M. and Ustin, S.L. (2005), Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination, Environmental Pollution, 137(2):241252.Google Scholar
Roth-Nebelsick, A., Uhl, D., Mosbrugger, V. and Kerp, H. (2001), Evolution and function of leaf venation architecture: a review, Annals of Botany, 87(5):553566.Google Scholar
Rozema, J., Chardonnens, A., Tosserams, M., Hafkenscheid, R. and Bruijnzeel, S. (1997), Leaf thickness and UV-B absorbing pigments of plants in relation to an elevational gradient along the Blue Mountains, Jamaica, Plant Ecology, 128(1–2):151159.Google Scholar
Rudorff, B.F., Mulchi, C.L., Lee, E.H., Rowland, R. and Daughtry, C. (1995), Effects of O3 and SO2 on leaf characteristics in soybeans grown under ambient- and enriched-carbon dioxide atmosphere, in Proc. Air Toxics and Water Monitoring, (Russwurm G.M., Ed), SPIE, Munich, Germany, Vol. 2503, pp. 89100.Google Scholar
Ruhland, C.T. and Day, T.A. (1996), Changes in UV-B radiation screening effectiveness with leaf age in Rhododendron maximum, Plant, Cell & Environment, 19(6):740746.Google Scholar
Rühle, W. and Wild, A. (1979), The intensification of absorbances in leaves by light-dispersion, Planta, 146(5):551557.Google Scholar
Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W. and Plümer, L. (2010), Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance, Computers and Electronics in Agriculture, 74 (1):9199.Google Scholar
Rumpho, M.E., Summer, E.J. and Manhart, J.R. (2000), Solar-powered sea slugs. Mollusc/algal chloroplast symbiosis, Plant Physiology, 123(1):2938.Google Scholar
Rumpho, M.E., Worful, J.M., Lee, J., Kannan, K., Tyler, M.S., Bhattacharya, D., et al. (2008), Horizontal gene transfer of the algal nuclear gene psbO to the photosynthetic sea slug Elysia chlorotica, Proceedings of the National Academy of Sciences of Sciences of the United States of America, 105(46):1786717871.Google Scholar
Runeckles, V.C. and Resh, H.M. (1975), The assessment of chronic ozone injury to leaves by reflectance spectrophotometry, Atmospheric Environment, 9(4):447452.Google Scholar
Rupert, C.S. and Latarjet, R. (1978), Toward a nomenclature and dosimetric scheme applicable to all radiations, Photochemistry and Photobiology, 28(1):35.Google Scholar
Ruth, B., Hoque, E., Weisel, B. and Hutzler, P.J.S. (1991), Reflectance and fluorescence parameters of needles of Norway spruce affected by forest decline, Remote Sensing of Environment, 38(1):3544.Google Scholar
Rvachev, V.P. and Guminetskii, S.G. (1966), The structure of light beams reflected by plant leaves, Journal of Applied Spectroscopy, 4(5):303307 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 4(5):415421).Google Scholar
Sack, L. and Scoffoni, C. (2013), Leaf venation: structure, function, development, evolution, ecology and applications in the past, present and future, New Phytologist, 198(4):9831000.Google Scholar
Sack, L., Caringella, M., Scoffoni, C., Mason, C., Rawls, M., Markesteijn, L. et al. (2014), Leaf vein length per unit area is not intrinsically dependent on image magnification: avoiding measurement artifacts for accuracy and precision, Plant Physiology, 166(2):829838.Google Scholar
Safir, G.R., Svits, G.H. and Ellingboe, A.H. (1972), Spectral reflectance and transmittance of corn leaves infected with Helminthosporium maydis, Phytopathology, 62(10):12101213.Google Scholar
Sagan, C., Thompson, W.R., Carlson, R. and Gurnett, D. (1993), A search for life on Earth from the Galileo spacecraft, Nature, 365:715721.Google Scholar
Sage, R. (2005), Atmospheric CO2, environmental stress, and evolution of C4 photosynthesis, in A history of Atmospheric CO2 and Its Effects on Plants, Animals, and Ecosystems (Ehleringer, J.R., Cerling, T.E. and Dearing, M.D., Eds), Springer, New York, pp. 185214.Google Scholar
Saito, H. (2001), Blue biliprotein as an effective factor for cryptic colouration in Rhodinia fugax larvae, Journal of Insect Physiology, 47(2):205212.Google Scholar
Salisbury, J.W. (1986), Preliminary measurements of leaf spectral reflectance in the 8–14 µm region, International Journal of Remote Sensing, 7(12):18791886.Google Scholar
Salisbury, J.W., Milton, N.M. and Walsh, P.A. (1987), Significance of non-isotropic scattering from vegetation for geobotanical remote sensing, International Journal of Remote Sensing, 8(7):9971009.Google Scholar
Salisbury, J.W. and Milton, N.M. (1988), Thermal infrared (2.5- to 13.5-µm) directional-hemispherical reflectance of leaves, Photogrammetric Engineering & Remote Sensing, 54(9):13011304.Google Scholar
Salisbury, J.W. and D’Aria, D.M. (1992), Emissivity of terrestrial materials in the 8–14 μm atmospheric window, Remote Sensing of Environment, 42(2):83106.Google Scholar
Salisbury, J.W., Wald, A. and D’Aria, D.M. (1994), Thermal infrared remote sensing and Kirchhoff’s law. 1: Laboratory measurements, Journal of Geophysical Research-Solid Earth, 99 (B6):11,89711,911.Google Scholar
Salm-Horstmar, F. (1854), Substanz der grünen Infusorien, Annalen der Physik, 169(9):159159.Google Scholar
Salm-Horstmar, F. (1855a), Ueber das dispergirte rothe Licht in der Auflösung des Chlorophylls, Annalen der Physik, 170(3):467468.Google Scholar
Salm-Horstmar, F. (1855b), Untersuchung des grünen Stoffes, den die kleinsten grünen Infusorien enthalten, Annalen der Physik, 170(3):466467.Google Scholar
Salm-Horstmar, F. (1856), Untersuchung des grünen Stoffes wahrer Infusorien, Annalen der Physik, 173(2):331333.Google Scholar
Saltas, V., Triantis, D., Manios, T. and Vallianatos, F. (2007), Biomonitoring of environmental pollution using dielectric properties of tree leaves, Environmental Monitoring and Assessment, 133(1–3):6978.Google Scholar
Saltelli, A. and Bolado, R. (1998), An alternative way to compute Fourier amplitude sensitivity test (FAST), Computational Statistics & Data Analysis, 26(4):445460.Google Scholar
Saltelli, A., Tarantola, S. and Chan, K.P.S. (1999), A quantitative, model independent method for global sensitivity analysis of model output, Technometrics, 41(1):3956.Google Scholar
Saltelli, A., Chan, K. and Scott, E.M. (2000), Sensitivity Analysis, John Wiley & Sons, 475 pages.Google Scholar
Sanches, I.D., Souza Filho, C.R. and Kokaly, R.F. (2014), Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal, ISPRS Journal of Photogrammetry and Remote Sensing, 97:111122.Google Scholar
Sanches, M.C. and Válio, I.F.M. (2006), Leaf optical properties of two liana species Canavalia parviflora Benth. and Gouania virgata Reissk in different light conditions, Revista Brasileira de Botânica, 29(2):319330.Google Scholar
Sánchez-Azofeifa, G.A., Castro, K., Wright, S.J., Gamon, J., Kalacska, M., Rivard, B., et al. (2009), Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: implications for remote sensing in tropical environments, Remote Sensing of Environment, 113(10):20762088.Google Scholar
Sánchez-Azofeifa, G.A., Oki, Y., Fernandes, G.W., Ball, R.A. and Gamon, J. (2012), Relationships between endophyte diversity and leaf optical properties, Trees – Structure and Function, 26(2):291299.Google Scholar
Sancho-Knapik, D., Alvarez-Arenas, T.E.G., Peguero-Pina, J.J. and Gil-Pelegrin, E. (2010), Air-coupled broadband ultrasonic spectroscopy as a new non-invasive and non-contact method for the determination of leaf water status, Journal of Experimental Botany, 61(5):13851391.Google Scholar
Sancho-Knapik, D., Gismero, J. Asensio, A., Peguero-Pina, J.J., Fernández, V., Álvarez-Arenas, T.E.G., et al. (2011a), Microwave L-band (1730 MHz) accurately estimates the relative water content in poplar leaves. A comparison with a near infrared water index (R(1300)/R(1450)), Agricultural and Forest Meteorology, 151(7):827832.Google Scholar
Sancho-Knapik, D., Alvarez-Arenas, T.E.G., Peguero-Pina, J.J., Fernandez, V. and Gil-Pelegrin, E. (2011b), Relationship between ultrasonic properties and structural changes in the mesophyll during leaf dehydration, Journal of Experimental Botany, 62(10):36373645.Google Scholar
Sancho-Knapik, D., Peguero-Pina, J.J., Medrano, H., Fariñas, M.D., Álvarez-Arenas, T.E.G. and Gil-Pelegrín, E. (2013), The reflectivity in the S-band and the broadband ultrasonic spectroscopy as new tools for the study of water relations in Vitis vinifera L., Physiologia Plantarum, 148(4):512521.Google Scholar
Sandquist, D.R. and Ehleringer, J.R. (1997), Intraspecific variation of leaf pubescence and drought response in Encelia farinosa associated with contrasting desert environments, New Phytologist, 135:635644.Google Scholar
Sandwald, E.F. (1981), Laboratory-determined spectral signatures of leaves of healthy and rizomania-diseased sugar beets and disease interpretability from aerial IRC photographs, in Proc. Signatures spectrales d’objets en télédétection, Avignon, France, 8–11 September 1981, INRA, pp. 201208.Google Scholar
Sankaran, S., Ehsani, R. and Etxeberria, E. (2010a), Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves, Talanta, 83(2):574581.Google Scholar
Sankaran, S., Mishra, A., Ehsani, R., and Davis, C. (2010b), A review of advanced techniques for detecting plant diseases, Computers and Electronics in Agriculture, 72(1):113.Google Scholar
Sankaran, S., Mishra, A., Maja, J.M. and Ehsani, R. (2011), Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards, Computers and Electronics in Agriculture, 77(2):127134.Google Scholar
Sankaran, S., Ehsani, R., Inch, S.A. and Ploetz, R.C. (2012), Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of Laurel Wilt, Plant Disease, 96(11):16831689.Google Scholar
Santos, A.O. and Kaye, O. (2009), Grapevine leaf water potential based upon near infrared spectroscopy, Scientia Agricola (Piracicaba, Braz.), 66(3):287292.Google Scholar
Santos, M.J., Hestir, E.L., Khanna, S. and Ustin, S.L. (2012), Image spectroscopy and stable isotopes elucidate functional dissimilarity between native and nonnative plant species in the aquatic environment, New Phytologist, 193:683695.Google Scholar
Sanz, C. (1994), Mesure et modélisation de la variation directionnelle des propriétés optiques des feuilles, DEA Dynamique de la Biosphère Continentale, Université Paul Sabatier, Toulouse, France, 32 pp.Google Scholar
Sarabandi, K. and Ulaby, F.T. (1988), Technique for measuring the dielectric constant of thin materials, IEEE Transactions on Instrumentation and Measurement, 37(4):631636.Google Scholar
Sarabandi, K., Ulaby, F.T. and Senior, T.B.A. (1990), Millimeter wave scattering model for a leaf, Radio Science, 25(1):918.Google Scholar
Sardans, J., Peñuelas, J. and Rodà, F. (2006), Plasticity of leaf morphological traits, leaf nutrient content, and water capture in the Mediterranean evergreen oak Quercus ilex subsp. ballota in response to fertilization and changes in competitive conditions, Ecoscience, 13(2):258270.Google Scholar
Sarto, A.W., Woldemar, C.M. and Vanderbilt, V.C. (1989), Polarized Light Angle Reflectance instrument I polarized incidence (POLAR:I), in Proc. Polarization Considerations for Optical Systems II (Chipman R.A., Ed), San Diego, CA, 9–11 August 1989, SPIE, Vol. 1166, pp. 220230.Google Scholar
Savenkov, S.N., Muttiah, R.S. and Oberemok, Y.A. (2003), Transmitted and reflected scattering matrices from an English oak leaf, Applied Optics, 42(24):49554962.Google Scholar
Savenkov, S.N., Mishchenko, L.T., Muttiah, R.S., Oberemok, Y.A. and Mishchenko, I.A. (2004), Mueller polarimetry of virus-infected and healthy wheat under field and microgravity conditions, Journal of Quantitative Spectroscopy & Radiative Transfer, 88(1–3):327343.Google Scholar
Savenkov, S.N. and Muttiah, R.S. (2004a), Inverse polarimetry, and light scattering from leaves, in Photopolarimetry in Remote Sensing (Videen, G., Ed), Kluwer Academic Publishers, pp. 243264.Google Scholar
Savenkov, S.N., Muttiah, R.S., Yakubchak, V.V. and Klimov, A.S. (2007), Anisotropy parameters for Chlorophytum leaf epidermis, in Proc. 10th International Conference on Electromagnetic & Light Scattering (Videen G., Mishchenko M., Mengüç M.P. and Zakharova N., Eds), Bodrum, Turkey, 17–22 June 2007, pp. 185188.Google Scholar
Savenkov, S.N. (2015), Principles of the Mueller matrix measurements, in Light Scattering Reviews 9 (Kokhanovsky, A., Ed), Springer Praxis Books, pp. 213255.Google Scholar
Saviranta, N.M.M., Julkunen-Tiitto, R., Oksanen, E. and Karjalainen, R.O. (2010), Leaf phenolic compounds in red clover (Trifolium pratense L.) induced by exposure to moderately elevated ozone, Environmental Pollution, 158(2):440446.Google Scholar
Schade, U., Holldack, K., Kuske, P., Wüstefeld, G. and Hübers, H.W. (2004), THz near-field imaging employing synchrotron radiation, Applied Physics Letters, 84(8):14221424.Google Scholar
Schade, U., Holldack, K., Martin, M.C. and Fried, D. (2005), THz near-field imaging of biological tissues employing synchrotron radiation, in Proc. Ultrafast Phenomena in Semiconductors and Nanostructure Materials IX (Tsen K.T., Song J.J. and Jiang H., Eds), San Jose, CA, 24 January 2005, SPIE, Vol. 5725, pp. 46–52.Google Scholar
Schaefer, H.M. and Wilkinson, D.M. (2004), Red leaves, insects and coevolution: a red herring? Trends in Ecology & Evolution, 19(12):616618.Google Scholar
Schaepman, M.E., Ustin, S.L., Plaza, A.J., Painter, T.H., Verrelst, J. and Liang, S. (2009), Earth system science related imaging spectroscopy – An assessment, Remote Sensing of Environment, 113(1):S123S137.Google Scholar
Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S. and Martonchik, J.V. (2006), Reflectance quantities in optical remote sensing – Definitions and case studies, Remote Sensing of Environment, 103(1):2742.Google Scholar
Schanda, R. (1986), Physical Fundamentals of Remote Sensing, Springer Verlag, Berlin, 187 pages.Google Scholar
Schaper, H. and Chacko, E.K. (1991), Relation between extractable chlorophyll and portable chlorophyll meter readings in leaves of eight tropical and subtropical fruit-tree species, Journal of Plant Physiology, 138(6):674677.Google Scholar
Schellekens, J.H., Gilbes, F., Rivera, G.D., Ysa, Y.C., Chardon, S. and Fong, Y. (2005), Reflectance spectra of tropical vegetation as a response to metal enrichment in the substrate of west-central Puerto Rico, Caribbean Journal of Earth Science, 39:912.Google Scholar
Scheller, M., Jansen, C., and Koch, M. (2010), Applications of effective medium theories in the terahertz regime, in Recent Optical and Photonic Technologies (Kim, K.Y., Ed.), INTECH, Croatia, pp. 231250.Google Scholar
Schepers, J.S., Blackmer, T.M., Wilhelm, W.W. and Resende, M. (1996), Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply, Journal of Plant Physiology, 148(5):523529.Google Scholar
Schepers, J.S., Blackmer, T.M. and Francis, D.D. (1997), Chlorophyll meter method for estimating nitrogen content in plant tissue, in Handbook of Reference Methods for Plant Analysis (Kalra, Y.P., Ed), CRC Press, pp. 129135.Google Scholar
Schimleck, L.R., Doran, J.C. and Rimbawanto, A. (2003), Near infrared spectroscopy for cost effective screening of foliar oil characteristics in a Melaleuca cajuputi breeding population, Journal of Agricultural and Food Chemistry, 51(9):24332437.Google Scholar
Schimper, A.F.W., Fisher, W.R., Groom, P. and Balfour, I.B. (1903), Plant-Geography Upon a Physiological Basis, Clarendon Press, 839 pages.Google Scholar
Schlemmer, M.R., Francis, D.D., Shanahan, J.F. and Schepers, J.S. (2005), Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content, Agronomy Journal, 97(1):106112.Google Scholar
Schlerf, M., Atzberger, C., Udelhoven, T., Jarmer, T., Mader, S., Werner, W. et al. (2003), Spectrometric estimation of leaf pigments in Norway spruce needles using band-depth analysis, partial least-square regression and inversion of a conifer leaf model, in Proc. 3rd EARSeL Workshop on Imaging Spectroscopy (Habermeyer M., Müller A. and Holzwarth S., Eds), Herrsching, Germany, 13–16 May 2003, EARSeL, pp. 559568.Google Scholar
Schmidt, J.A. (2010), Electronic spectroscopy of lignins, in Lignin and Lignans: Advances in Chemistry (Heitner, C., Dimmel, D.R., and Schmidt, J.A., Eds), CRC Press, pp. 49102.Google Scholar
Schmidt, K.S. and Skidmore, A.K. (2001), Exploring spectral discrimination of grass species in African rangelands, International Journal of Remote Sensing, 22(17):34213434.Google Scholar
Schönn, L. (1872), Ueber die Absorptionsstreifen des Blattgrüns, Annalen der Physik, 221(1):166167.Google Scholar
Schreiber, U., Vidaver, W., Runeckles, V.C. and Rosen, P. (1978), Chlorophyll fluorescence assay for ozone injury in intact plants, Plant Physiology, 61(1):8084.Google Scholar
Schubert, E.F. (2006), Light Emitting Diodes, Cambridge University Press, Cambridge, 434 pages.Google Scholar
Schuepp, P.H. (1993), Tansley Review N°59 – Leaf boundary layers, New Phytologist, 125(3):477507.Google Scholar
Schultz, H.R. (1996), Leaf absorptance of visible radiation in Vitis vinifera L.: estimates of age and shade effects with a simple field method, Scientia Horticulturae, 66(1–2):93102.Google Scholar
Schulz, H., Engelhardt, U.H., Wegent, A., Drews, H.H. and Lapczynski, S. (1999), Application of near-infrared reflectance spectroscopy to the simultaneous prediction of alkaloids and phenolic substances in green tea leaves, Journal of Agricultural and Food Chemistry, 47(12):50645067.Google Scholar
Schulze, E.D., Eller, B.M., Thomas, D.A., Willert, D.J. and Brinckmann, E. (1980), Leaf temperatures and energy balance of Welwitschia mirabilis in its natural habitat, Oecologia, 44(2):258262.Google Scholar
Schuster, A. (1905), Radiation through a foggy atmosphere, The Astrophysical Journal, 21(1):122.Google Scholar
Schutt, J.B., Rowland, R.R. and Heartly, W.H. (1984a), A laboratory investigation of a physical mechanism for the extended infrared absorption (“red shift”) in wheat, International Journal of Remote Sensing, 5(1):95102.Google Scholar
Schutt, J.B., Rowland, R.A. and Heggestad, H.E. (1984b), Identification of injury resulting from atmospheric pollutants using reflectance measurements, Journal of Environmental Quality, 13(4):605608.Google Scholar
Schwaller, M.R., Schnetzler, C.C. and Marshall, P.E. (1981), The Changes in Leaf Reflectance of Sugar Maple (Acer saccharum Marsh) Seedlings in Response to Heavy Metal Stress, NASA, Greenbelt, MD, June 1981, Technical Memorandum 82150, 17 pages.Google Scholar
Schwaller, M.R., Schnetzler, C.C. and Marshall, P.E. (1983), The changes in leaf reflectance of sugar maple (Acer saccharum Marsh) seedlings in response to heavy metal stress, International Journal of Remote Sensing, 4(1):93100.Google Scholar
Schwalm, P.A., Starrett, P.H. and McDiarmid, R.W. (1977), Infrared reflectance in leaf-sitting neotropical frogs, Science, 196(4295):12251227.Google Scholar
Schweiger, A.K., Schütz, M., Risch, A.C., Kneubühler, M., Haller, R. and Schaepman, M.E. (2017), How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response, Methods in Ecology and Evolution, 8(1):8695.Google Scholar
Seelig, H.D., Adams, W.W., Hoehn, A., Stodieck, L.S., Klaus, D.M. and Emery, W.J. (2008a), Extraneous variables and their influence on reflectance-based measurements of leaf water content, Irrigation Science, 26(5):407414.Google Scholar
Seelig, H.D., Hoehn, A., Stodieck, L.S., Klaus, D.M., Adams III, W.W. and Emery, W.J. (2008b), Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants, Remote Sensing of Environment, 112(2):445455.Google Scholar
Seelig, H.D., Hoehn, A., Stodieck, L.S., Klaus, D.M., Adams III, W.W. and Emery, W.J. (2008c), The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared, International Journal of Remote Sensing, 29(13):37013713.Google Scholar
Seely, G.R. and Jensen, R.G. (1965), Effect of solvent on the spectrum of chlorophyll, Spectrochimica Acta, 21(10):18351845.Google Scholar
Segelstein, D.J. (1981), The Complex Refractive Index of Water, MSc Thesis, Department of Physics. University of Missouri, Kansas City, MO, 167 pages.Google Scholar
Semenenko, A.D. (1960a), Optical properties of etiolated oats, Trudy sektora astrobotaniki, 8: 5466 (in Russian).Google Scholar
Semenenko, A.D. (1960b), Optical properties of various species of wheat, Trudy sektora astrobotaniki, 8: 4655 (English translation).Google Scholar
Senebier, J. (1783), Recherches sur l’influence de la lumière solaire pour métamorphoser l’air fixe en air pur par la végétation, B. Chirol, Genève, 385 pages.Google Scholar
Senior, T.B.A., Sarabandi, K. and Ulaby, F.T. (1987), Measuring and modeling the backscattering cross-section of a leaf, Radio Science, 22(6):11091116.Google Scholar
Serbin, S.P., Dillaway, D.N., Kruger, E.L. and Townsend, P.A. (2012), Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature, Journal of Experimental Botany, 63(1):489502.Google Scholar
Serbin, S.P., Singh, A., McNeil, B.E., Kingdon, C.C. and Townsend, P.A. (2014), Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species, Ecological Applications, 24(7):16511669.Google Scholar
Senn, G. (1908), Die Gestalts- und Lageveränderung der Pflanzen-Chromatophoren, Wilhelm Engelmann, Leipzig, 397 pages.Google Scholar
Serôdio, J., Cruz, S., Cartaxana, P., and Calado, R. (2014), Photophysiology of kleptoplasts: photosynthetic use of light by chloroplasts living in animal cells, Philosophical Transactions of The Royal Society B, 369(1640):20130242.Google Scholar
Serrano, L. (2008), Effects of leaf structure on reflectance estimates of chlorophyll content, International Journal of Remote Sensing, 29(17):52655274.Google Scholar
Seybold, A. (1933a), Uber die Optischen Eigenschaften der Laubblätter. III: Planta, 20(3):577601.Google Scholar
Seybold, A. (1933b), Uber die Optischen Eigenschaften der Laubblätter. IV: Planta, 21(2):251265.Google Scholar
Seybold, A. (1956), Hat die Chloroplastenverlagerung in Laubblättern eine Bedeutung? Naturwissenschaften, 43(4):9091.Google Scholar
Seyfried, M. and Fukshansky, L. (1983), Light gradients in plant tissue, Applied Optics, 22(9):14021408.Google Scholar
Seyfried, M. and Schafer, E. (1983), Changes in the optical properties of cotyledons of Cucurbita pepo during the first seven days of their development, Plant, Cell & Environment, 6(8):633640.Google Scholar
Shacklette, H.T., Lakin, H.W., Hubert, A.E. and Curtin, G.C. (1970), Absorption of gold by plants, USGS Bulletin, 1314-B, 23 pages.Google Scholar
Shakespeare, T. and Shakespeare, J. (2003), A fluorescent extension to the Kubelka-Munk model, Color Research and Applications, 28(1):414.Google Scholar
Sharifi, M.R., Gibson, A.C. and Rundel, P.W. (1997), Surface dust impacts on gas exchange in Mojave Desert shrubs, Journal of Applied Ecology, 34(4):837846.Google Scholar
Shashar, N., Cronin, T.W., Wolff, L.B. and Condon, M.A. (1998), The polarization of light in a tropical rain forest, Biotropica, 30(2):275285.Google Scholar
Shenk, J.S., Westerhaus, M.O. and Hoover, M.R. (1979), Analysis of forage by infrared reflectance, Journal of Dairy Science, 63(5):807812.Google Scholar
Shenk, J.S., Landa, I., Hoover, M.R. and Westerhaus, M.O. (1981), Description and evaluation of near infrared reflectance spectro-computer for forage and grain analysis, Crop Science, 21(3):355358.Google Scholar
Shepherd, K.D., Palm, C.A., Gachengo, C.N. and Vanlauwe, B. (2003), Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy, Agronomy Journal, 95(5):13141322.Google Scholar
Sheue, C.R., Pao, S.H., Chien, L.F., Chesson, P. and Peng, C.I. (2012), Natural foliar variegation without costs? The case of Begonia, Annals of Botany, 109(6):10651074.Google Scholar
Shi, R.H., Zhuang, D.F. and Li, S. (2005), Study on the extraction of plant biochemical information from canopy reflectance spectra, in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS ‘05), Seoul, Republic of Korea, 25–29 July 2005, IEEE, Vol. 5, pp. 31323134.Google Scholar
Shi, R.H., Zhuang, D.F. and Niu, Z. (2006), Simulated hyperspectral data analysis using continuum removal: case study on leaf chlorophyll prediction, in Proc. 8th International Conference on Signal Processing, Beijing, China, 16–20 November 2006Vol. 4, 3 pages.Google Scholar
Shi, R.H., Zhuang, D.F. and Niu, Z. (2007), Estimation of optimal mesophyll structure parameter of rice leaves, Journal of Remote Sensing – Beijing, 11(5):626631.Google Scholar
Shi, R.H. and Sun, J. (2007), Estimating leaf biochemical information from leaf reflectance spectrum using artificial neural network, in Proc. 6th International Conference on Machine Learning and Cybernetics, Hong Kong, 19–22 August 2007, IEEE, Vol. 4, pp. 22242228.Google Scholar
Shi, R.H., Zhang, H., Sun, J., Gao, W., Zhuang, D.F. and Niu, Z. (2008), Responses of plant biochemical substances to reflectance spectra at leaf and canopy scales, in Proc. Remote Sensing and Modeling of Ecosystems for Sustainability V (Gao W. and Wang H., Eds), San Diego, CA, 13 August 2008, SPIE, Vol. 7083, 70830X.Google Scholar
Shibghatallah, M.A.H., Khotimah, S.N., Suhandono, S., Viridi, S. and Kesuma, T. (2013), Measuring leaf chlorophyll concentration from its color: a way in monitoring environment change to plantations, AIP Conference Proceedings, 1554(1):210213.Google Scholar
Shimazaki, K., Sakaki, T., Kondo, N. and Sugahara, K. (1980), Active oxygen participation in chlorophyll destruction and lipid peroxidation in SO2-fumigated leaves of spinach, Plant and Cell Physiology, 21(8):11931204.Google Scholar
Shimshi, D. and Livne, A. (1967), The estimation of the osmotic potential of plant sap by refractometry and conductimetry: a field method, Annals of Botany, 31(3):505511.Google Scholar
Shipley, B. and Vu, T.T. (2002), Dry matter content as a measure of dry matter concentration in plants and their parts, New Phytologist, 153(2):359364.Google Scholar
Shul’gin, I.A. and Kleshnin, A.F. (1959), Correlation between optical properties of plant leaves and their chlorophyll content, Doklady Botanical Sciences Sections, 125:119121 (cover-to-cover translation from Doklady Akademii Nauk SSSR).Google Scholar
Shul’gin, I.A., Khazanov, V.S. and Kleshnin, A.F. (1960a), On the reflection of light as related to leaf structure, Doklady Botanical Sciences Sections, 134:197199 (cover-to-cover translation from Doklady Akademii Nauk SSSR, 134(2):471–474).Google Scholar
Shul’gin, I.A., Kleshnin, A.F. and Podol’nyi, V.Z. (1960b), Optical properties of plant leaves in the ultraviolet region, Soviet Plant Physiology, 7: 116118 (cover-to-cover translation from Fiziologiya Rastenii, 7(2):141144).Google Scholar
Shul’gin, I.A. and Khzanov, V.S. (1961), On the problem of light conditions in plant associations, Doklady Botanical Sciences Sections, 141: 210212 (cover-to-cover translation from Doklady Akademii Nauk SSSR, 141(6):14931496).Google Scholar
Shul’gin, I.A. and Moldau, K.H. (1965), On coefficients of brightness of leaves in natural and polarized light, Doklady Botanical Sciences Sections, 162(6):99101 (cover-to-cover translation from Doklady Akademii Nauk SSSR, 162(6):1430–1433).Google Scholar
Shull, C.A. (1928), Reflection of light from the surface of leaves, Science, 67(1726):107108.Google Scholar
Shull, C.A. (1929), A spectrophotometric study of reflection of light from leaf surfaces, Botanical Gazette, 87(5):583607.Google Scholar
Shuplyak, V.I., Atrashevskii, Y.I., Sikorskii, V.V., Stel’makh, G.F. and Fomichev, A.Y. (1994), Spectral and polarization characteristics of light reflected from the leaves of plants infected with bacterial cancer, Journal of Applied Spectroscopy, 60 (3–4):175179 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 60(3–4):226–232).Google Scholar
Shuplyak, V.I., Belyaev, B.I., Belyaev, Y.V., Chumakov, A.V., Kurikina, T.M. and Nekrasov, V.P. (1997), Investigation of the spectropolarization characteristics angular dependence of radiation reflected by potato leaves, in Proc. Earth Surface Remote Sensing, London, UK, 21–25 September 1997, SPIE, Vol. 3222, pp. 8895.Google Scholar
Sieghardt, H. (1990), Effects of dust pollution on optical properties of leaves of Erysimum sylvestre, Phyton, 30(2):305311.Google Scholar
Simon, R., Holderied, M.W., Koch, C.U., von Helversen, O. (2011), Floral acoustics: conspicuous echoes of a dish-shaped leaf attract bat pollinators, Science, 333:631633.Google Scholar
Simpson, L. and Moore, R. (1984), Cellular structure and light absorption in leaves of Frithia pulchra (Mesembryanthemaceae), Annals of Botany, 53(3):413420.Google Scholar
Simpson, N. (2011), Colour and contemporary digital botanical illustration, Optics & Laser Technology, 43(2):330336.Google Scholar
Sims, D.A. and Gamon, J.A. (2002), Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sensing of Environment, 81(2–3):337354.Google Scholar
Sinclair, R. and Thomas, D.A. (1970), Optical properties of leaves of some species in arid south Australia, Australian Journal of Botany, 18(3):261273.Google Scholar
Sinclair, T.R., Hoffer, R.M. and Schreiber, M.M. (1971),Reflectance and internal structure of leaves from several crops during a growing season, Agronomy Journal, 63(6):864867.Google Scholar
Sinclair, T.R., Schreiber, M.M. and Hoffer, R.M. (1973), Diffuse reflectance hypothesis for the pathway of solar radiation through leaves, Agronomy Journal, 65(2):276283.Google Scholar
Singh, A., Yadav, K., Sen, A.K. (2012), Sal (Shorea robusta) leaves lignin epoxydation and its use in epoxy based coatings, American Journal of Polymer Science, 2(1):1418.Google Scholar
Singhroy, V., Kenny, F. and Springer, J. (1989), Reflectance spectra of vegetation growing on mine site in the Canadian Shield, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’89), Vancouver, BC, 10–14 July 1989, IEEE, Vol. 2, pp. 665669.Google Scholar
Singhroy, V., Saint-Jean, R., Levesque, J. and Barnett, P. (2000), Reflectance spectra of the boreal forest over mineralized sites, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS 2000), Honolulu, Hawaii, 24–28 July 2000, IEEE, Vol. 4, pp. 13791381.Google Scholar
Sinkkonen, A., Somerkoski, E., Paaso, U., Holopainen, J.K., Rousi, M. and Mikola, J. (2012), Genotypic variation in yellow autumn leaf colours explains aphid load in silver birch, New Phytologist, 195(2):461469.Google Scholar
Sinton, W.M. (1957), Spectroscopic evidence for vegetation on Mars, The Astrophysical Journal, 126(2):231239.Google Scholar
Sinton, W.M. (1959), Further evidence of vegetation on Mars, Science, 130(3384):12341237.Google Scholar
Slaton, M.R., Hunt, E.R. and Smith, W.K. (2001), Estimating near-infrared leaf reflectance from leaf structural characteristics, American Journal of Botany, 88(2):278284.Google Scholar
Slavik, B. (1959), The relation of the refractive index of plant cell sap to its osmotic pressure, Biologia Plantarum, 1(1):4853.Google Scholar
Sliney, D.H. (2007), Radiometric quantities and units used in photobiology and photochemistry: recommendations of the Commission Internationale de l’Eclairage (International Commission on Illumination), Photochemistry and Photobiology, 83(2):425432.Google Scholar
Slonecker, T., Haack, B. and Price, S. (2009), Spectroscopic analysis of arsenic uptake in Pteris ferns, Remote Sensing, 1(4):644675.Google Scholar
Smith, A.P. (1986), Ecology of a leaf color polymorphism in a tropical forest species: habitat segregation and herbivory, Oecologia, 69(2):283287.Google Scholar
Smith, A.M. and Blackshaw, R.E. (2002), Crop/weed discrimination using remote sensing, in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS’02), Toronto, ON, 24–28 June 2002, IEEE, Vol. 4, pp. 19621964.Google Scholar
Smith, A.M. and Blackshaw, R.E. (2003), Weed-crop discrimination using remote sensing: a detached leaf experiment, Weed Technology, 17(4):811820.Google Scholar
Smith, E.G., Ketchum, R.N. and Burt, J.A. (2017), Host specificity of Symbiodinium variants revealed by an ITS2 metahaplotype approach, The ISME Journal, 11(6):15001503.Google Scholar
Smith, J.H.C., Shibata, K. and Hart, R.W. (1957), A Spectrophotometer accessory for measuring absorption spectra of light-scattering samples: spectra of dark-grown albino leaves and of adsorbed chlorophylls, Archives of Biochemistry and Biophysics, 72(2):457464.Google Scholar
Smith, J.L. II and Hare, J.D. (2004), Spectral properties, gas exchange, and water potential of leaves of glandular and non-glandular trichome types in Datura wrightii (Solanaceae), Functional Plant Biology, 31(3):267273.Google Scholar
Smith, K.F. and Flinn, P.C. (1991), Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy, Australian Journal of Experimental Agriculture, 31(2):205210.Google Scholar
Smith, K.F., Willis, S.E. and Flinn, P.C. (1991), Measurement of the magnesium concentration in perennial ryegrass (Lolium perenne) using near infrared reflectance spectroscopy, Australian Journal of Agricultural Research, 42(8):13991404.Google Scholar
Smith, K.L., Steven, M.D. and Colls, J.J. (2004a), Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks, Remote Sensing of Environment, 92(2):207217.Google Scholar
Smith, K.L., Steven, M.D. and Colls, J.J. (2004b), Spectral responses of pot-grown plants to displacement of soil oxygen, International Journal of Remote Sensing, 25(20):43954410.Google Scholar
Smith, K.L., Steven, M.D. and Colls, J.J. (2005), Plant spectral responses to gas leaks and other stresses, International Journal of Remote Sensing, 18(26):40674081.Google Scholar
Smith, M.O., Ustin, S.L., Adams, J.B. and Gillespie, A.R. (1990), Vegetation in deserts. I: A regional measure of abundances from multispectral images, Remote Sensing of Environment, 31(1):126.Google Scholar
Smith, M.O., Roberts, D.A., Hill, J., Mehl, W., Hosgood, B., Venderbout, J., et al. (1994), A new approach to quantifying abundancies of materials in multispectral images, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’94), Pasadena, CA, 8–12 August 1994, IEEE, Vol. 4, pp. 23722374.Google Scholar
Smith, S.E. and Read, D.J. (2008), Mycorrhizal Symbiosis, Academic Press, 800 pages.Google Scholar
Smith, W.K., Vogelmann, T.C., DeLucia, E.H., Bell, D.T. and Shepherd, K.A. (1997), Leaf form and photosynthesis, Bioscience, 47(11):785793.Google Scholar
Snyder, L.R. (1974), Classification of the solvent properties of common liquids, Journal of Chromatographic A, 92(2):223234.Google Scholar
Sobol’, I.M. (1993), Sensitivity analysis for nonlinear mathematical models, Mathematical Modeling and Computational Experiment, 1:407414.Google Scholar
Solovchenko, A. (2010), Photoprotection in Plants – Optical Screening-based Mechanisms, Springer-Verlag, Berlin, 168 pages.Google Scholar
Soltau, U., Dötterl, S. and Liede-Schumann, S. (2009), Leaf variegation in Caladium steudneriifolium (Araceae): a case of mimicry? Evolutionary Ecology, 23(4):503512.Google Scholar
Song, X.D., Jiang, H., Yu, S.Q., Zhou, G.M. and Jiang, Z.S. (2010), Relationship between simulated acid rain stress and leaf reflectance, Spectroscopy and Spectral Analysis, 30(1):165169 (in Chinese).Google Scholar
Sonobe, R. and Wang, Q. (2016), Assessing the xanthophyll cycle in natural beech leaves with hyperspectral reflectance, Functional Plant Biology, 43(5):438447.Google Scholar
Sonobe, R. and Wang, Q. (2017a), Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests, Ecological Informatics, 37:19.Google Scholar
Sonobe, R. and Wang, Q. (2017b), Towards a universal hyperspectral index to assess chlorophyll content in deciduous forests, Remote Sensing, 9(3):191.Google Scholar
Sorby, H.C. (1871a), Memoirs: on the colour of leaves at different seasons of the year, Journal of Cell Sciences, 2–11(43):215234.Google Scholar
Sorby, H.C. (1871b), On the various tints of autumnal foliage, The Quarterly Journal of Science, 8(29–32):6477.Google Scholar
Sorby, H.C. (1872), On comparative vegetable chromatology, Proceedings of the Royal Society of London, 21(139–147):442483.Google Scholar
Sorby, H.C. (1884), On the autumnal tints of foliage, Nature, 31:105106.Google Scholar
Soukupova, J., Rock, B.N. and Albrechtova, J. (2002), Spectral characteristics of lignin and soluble phenolics in the near infrared – A comparative study, International Journal of Remote Sensing, 23(15):30393055.Google Scholar
Souza, R.P. and Valio, I.F.M. (2003), Leaf optical properties as affected by shade in saplings of six tropical tree species differing in successional status, Revista Brasileira de Fisiologia Vegetal, 15(1):4954.Google Scholar
Sparks, W.B., Hough, J.H., Germer, T.A., Chen, F., DasSarma, S., DasSarma, P., et al. (2009a), Detection of circular polarization in light scattered from photosynthetic microbes, Proceedings of the National Academy of Sciences of the United States of America, 106(19):78167821.Google Scholar
Sparks, W.B., Hough, J.H., Kolokolova, L., Germer, T.A., Chen, F., DasSarma, S., et al. (2009b), Circular polarization in scattered light as a possible biomarker, Journal of Quantitative Spectroscopy & Radiative Transfer, 110(14–16):17711779.Google Scholar
Spomer, L.A., Smith, M.A.L. and Sawwan, J.S. (1988), Rapid nondestructive measurement of chlorophyll content in leaves with nonuniform chlorophyll distribution, Photosynthesis Research, 16(3):277284.Google Scholar
Sridhar, B.B.M., Han, F.X., Diehl, S.V., Monts, D.L. and Su, Y. (2007a), Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium, International Journal of Remote Sensing, 28(5):10411054.Google Scholar
Sridhar, B.B.M., Han, F.X., Diehl, S.V., Monts, D.L. and Su, Y. (2007b), Monitoring the effects of arsenic and chromium accumulation in Chinese brake fern, International Journal of Remote Sensing, 28(5):10551067.Google Scholar
Stahl, E. (1896), Uber bunte Laubblätter. Ein Beitrag zur Pflanzenbiologie. II, Annales du Jardin Botanique de Buitenzorg, 13:137216.Google Scholar
Stahl, G.E. (1715), Opusculum chymico-physico-medicum, Typis & impsensis Orphanotrophei, Halae Magdeburgicae, Orphanotrophei, Halle Madeburg, 896 pages.Google Scholar
Stahl, U., Tusov, V.B., Paschenko, V. and Voigt, J. (1989), Spectroscopic investigation of fluorescence behaviour, role and function of the long-wavelength pigments of Photosystem I: Biochimica et Biophysica Acta, 973(2):198204.Google Scholar
Stamm, A.J. and Sanders, H.T. (1966), Specific gravity of the wood substance of loblolly pine as affected by chemical composition, Tappi, 49:397400.Google Scholar
Stearn, W.T. (1967), The anatomy of plants with an idea of a philosophical history of plants and several other lectures read before the Royal Society by Nehemiah Grew, The British Journal for the History of Science, 3(3):291293.Google Scholar
Stearn, W.T. (2010), Botanical Latin, 4th Edition, Timber Press, 546 pages.Google Scholar
Steddom, K., Heidel, G., Jones, D. and Rush, C.M. (2003), Remote detection of rhizomania in sugar beets, Phytopathology, 93(6):720726.Google Scholar
Sterner, R.W. and Elser, J.J. (2002), Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere, Princeton University Press, Princeton, NJ, 584 pages.Google Scholar
Sterzik, M., Bagnulo, S., Azua, A., Salinas, F., Alfaro, J. and Vicuna, R. (2010), Astronomy meets biology: EFOSC2 and the chirality of life, The Messenger, 142:2527.Google Scholar
Stokes, G.G. (1852a), On the change of refrangibility of light, Philosophical Transactions of the Royal Society of London, 142:463562.Google Scholar
Stokes, G.G. (1852b), On the composition and resolution of streams of polarized light from different sources, Transactions of the Cambridge Philosophical Society, 9:399416.Google Scholar
Stokes, G.G. (1862), On the intensity of the light reflected from or transmitted through a pile of plates, Proceedings of the Royal Society of London. Series B, 11:545556.Google Scholar
Stone, C., Chisholm, L. and Coops, N. (2001), Spectral reflectance characteristics of eucalypt foliage damaged by insects, Australian Journal of Botany, 49(6):687698.Google Scholar
Stone, C., Chisholm, L.A. and McDonald, S. (2003), Spectral reflectance characteristics of Pinus radiata needles affected by dothistroma needle blight, Canadian Journal of Botany, 81(6):560569.Google Scholar
Stone, C., Chisholm, L.A. and McDonald, S. (2005), Effects of leaf age and psyllid damage on the spectral reflectance properties of Eucalyptus saligna foliage, Australian Journal of Botany, 53(1):4554.Google Scholar
Strout, G., Russell, S.D., Pulsifer, D.P., Erten, S., Lakhtakia, A. and Lee, D.W. (2013), Silica nanoparticles aid in structural leaf coloration in the Malaysian tropical rainforest understorey herb Mapania caudata, Annals of Botany, 112(6):11411148.Google Scholar
Stuckens, J., Verstraeten, W.W., Delalieux, S., Swennen, R. and Coppin, P. (2009), A dorsiventral leaf radiative transfer model: development, validation and improved model inversion techniques, Remote Sensing of Environment, 113(12):25602573.Google Scholar
Stuppy, W.H., Maisano, J.A., Colbert, M.W., Rudall, P.J. and Rowe, T.B. (2003), Three-dimensional analysis of plant structure using high-resolution X-ray computed tomography, Trends in Plant Science, 8(1):26.Google Scholar
Stylinski, C.D., Oechel, W.C., Gamon, J.A., Tissue, D.T., Miglietta, F. and Raschi, A. (2000), Effects of lifelong [CO2] enrichment on carboxylation and light utilization of Quercus pubescens Willd. examined with gas exchange, biochemistry and optical techniques, Plant, Cell & Environment, 23(12):13531362.Google Scholar
Stylinski, C.D., Gamon, J.A. and Oechel, W.C. (2002), Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species, Oecologia, 131(3):366374.Google Scholar
Su, Y., Maruthi Sridhar, B.B., Han, F.X., Diehl, S.V. and Monts, D.L. (2007), Effect of bioaccumulation of Cs and Sr natural isotopes on foliar structure and plant spectral reflectance of Indian mustard (Brassica juncea), Water, Air, and Soil Pollution, 180(1–4):6574.Google Scholar
Sui, X. Y., Li, S., Zhang, X., et al. (2010), Measurement of cotton leaf thickness with hyper spectrum, Transactions of the Chinese Society of Agricultural Engineering, 26(1):262266 (in Chinese).Google Scholar
Sui, X., Zhang, X., Wang, Y. and Li, S. (2012), Estimation of leaf thickness with remote sensing, Applied Mechanics and Materials, 263–266:339345.Google Scholar
Sullivan, J.H., Howells, B.W., Ruhland, C.T. and Day, T.A. (1996), Changes in leaf expansion and epidermal screening effectiveness in Liquidambar styraciflua and Pinus taeda in response to UV-B radiation, Physiologia Plantarum, 98(2):349357.Google Scholar
Sultanova, N., Kasarova, S. and Nikolov, I. (2009), Dispersion properties of optical polymers, Acta Physica Polonica A, 116(4):585587.Google Scholar
Sumriddetchkajorn, S. and Intaravanne, Y. (2014), Single-wavelength based rice leaf color analyzer for nitrogen status estimation, Optics and Lasers in Engineering, 53:179184.Google Scholar
Sun, C.X., Yuan, F., Zhang, Y.L., Chen, Z.H., Chen, L.J. and Wu, Z.J. (2012), Study of photosynthetic characteristics of transgenic barley based on reflectance of single leaf, Spectroscopy and Spectral Analysis, 32(1):204208 (in Chinese).Google Scholar
Sun, C.X., Yuan, F., Zhang, Y.L., Cui, Z.B., Chen, Z.H., Chen, L.J. et al. (2013), Unintended effects of genetic transformation on photosynthetic gas exchange, leaf reflectance and plant growth properties in barley (Hordeum vulgare L.), Photosynthetica, 51(1):2232.Google Scholar
Sun, J., Nishio, J.N. and Vogelmann, T.C. (1996), High-light effects on CO2 fixation gradients across leaves, Plant, Cell & Environment, 19(11):12611270.Google Scholar
Sun, J., Nishio, J.N. and Vogelmann, T.C. (1998), Green light drives CO2 fixation deep within leaves, Plant & Cell Physiology, 39(10):10201026.Google Scholar
Sun, P., Grignetti, A., Liu, S., Casacchia, R., Salvatori, R., Pietrini, F., et al. (2008), Associated changes in physiological parameters and spectral reflectance indices in olive (Olea europaea L.) leaves in response to different levels of water stress, International Journal of Remote Sensing, 29(6):17251743.Google Scholar
Sušila, P. and Nauš, J.A. (2007), Monte Carlo study of the chlorophyll fluorescence emission and its effect on the leaf spectral reflectance and transmittance under various conditions, Photochemical & Photobiological Sciences, 6(8):894902.Google Scholar
Sykioti, O., Paronis, D., Stagakis, S. and Kyparissis, A. (2011), Band depth analysis of CHRIS/PROBA data for the study of a Mediterranean natural ecosystem. Correlations with leaf optical properties and ecophysiological parameters, Remote Sensing of Environment, 115(2):752766.Google Scholar
Syvertsen, J.P. and Cunningham, G.L. (1979), The effects of irradiating adaxial or abaxial leaf surface on the rate of net photosynthesis of Perezia nana and Helianthus annuus, Photosynthetica, 13:287293.Google Scholar
Szalay, L., Tombácz, E. and Singhal, G.S. (1974), Effect of solvent on the absorption spectra and Stokes’ shift of absorption and fluorescence of chlorophylls, Acta Physica Academiae Scientiarum Hungaricae, 35(1–4):2936.Google Scholar
Tageyeva, S.V. and Brandt, A.B. (1960a), Study of the optical properties of leaves in relation to the angle of incidence of light, Biophysics, 5: 354365 (cover-to-cover translation from Biofizika, 5(3):308317).Google Scholar
Tageyeva, S.V. and Brandt, A.B. (1961a), Study of optical properties of leaves depending on the angle of light incidence, in Progress in Photobiology (Christensen, B.C. and Buchmann, B., Eds), Elsevier Publishing Company, Amsterdam, pp. 163169.Google Scholar
Tageyeva, S.V., Brandt, A.B. and Derevyanko, V.G. (1960), Changes in optical properties of leaves in the course of the growing season, Doklady Botanical Sciences Sections, 135:266268.Google Scholar
Tageyeva, S.V., Brandt, A.B. and Derevyanko, V.G. (1961), Peculiarities of the optical properties of leaves during vegetation, in Progress in Photobiology (Christensen, B.C. and Buchmann, B., Eds), Elsevier Publishing Company, Amsterdam, pp. 158162.Google Scholar
Taiz, L. and Zeiger, E. (2010), Plant Physiology, 5th Edition, Sinauer Associates, Inc., 782 pages.Google Scholar
Takahashi, K., Mineuchi, K., Nakamura, T., Koizumi, M. and Kano, H. (1994), A system for imaging transverse distribution of scattered light and chlorophyll fluorescence in intact leaves, Plant, Cell & Environment, 17(1):105110.Google Scholar
Takahashi, T., Fujii, T. and Yasuoka, Y. (2004), Estimation and comparison of acid detergent lignin and acetyl bromide lignin in fallen leaves using near-infrared spectroscopy, International Journal of Remote Sensing, 25(24):55855600.Google Scholar
Takayama, K., Nishina, H. and Sakai, Y. (2008), Detection of water stress induced photosynthetic dysfunction in tomato plant leaf with PRI measurement, Acta Horticulturae, 801:12131219.Google Scholar
Takiuchi, M. and Hashimoto, Y. (1977), Measurement of leaf temperature by means of infrared thermometer in connection with plant physiological information, Transactions of the Society of Instrument and Control Engineers, 13(5):482488 (in Japanese).Google Scholar
Talmage, D.A. and Curran, P.J. (1986), Remote sensing using partially polarized light, International Journal of Remote Sensing, 7(1):4764.Google Scholar
Tan, H.S. (1981), Microwave measurements and modelling of the permittivity of tropical vegetation samples, Applied Physics, 25(3):351355.Google Scholar
Tanaka, M. (1968), Some leaf characters of flue-cured Tobacco leaves sprayed with transpiration suppressant, Japanese Journal of Crop Science, 37(3):436441 (in Japanese).Google Scholar
Tanaka, Y., Grottoli, A.G., Matsui, Y., Suzuki, A. and Sakai, K. (2015), Partitioning of nitrogen sources to algal endosymbionts of corals with long-term 15N-labelling and a mixing model, Ecological Modelling, 309–310:163169.Google Scholar
Tanner, V. and Eller, B.M. (1986), Veränderungen der spektralen Eigenschaften der Blätter der Buche (Fagus silvatica L.) von Laubaustrieb bis Laubfall, Allgemeine Forst und Jagdzeitung, 157(6):108117 (in German): Variations of the optical properties of the leaves of the European beech (Fagus silvatica L.) during the growing season).Google Scholar
Taoutaou, A., Socaciu, C., Pamfil, D., Fetea, F., Balazs, E., Botez, C., et al. (2010), Fourier-transformed infrared spectroscopy applied for studying compatible interaction in the pathosystem Phytophtora infestans-Solanum tuberosum, Notulae Botanicae Horti AgrobotaniciCluj-Napoca, 38(3):6975.Google Scholar
Taoutaou, A., Socaciu, C., Pamfil, D., Fetea, F., Balazs, E. and Botez, C. (2012), New markers for potato late blight resistance and susceptibility using FTIR spectroscopy, Notulae Botanicae Horti AgrobotaniciCluj-Napoca, 40(1):150154.Google Scholar
Tarantola, A. (2005), Inverse Problem Theory and Model Parameter Estimation, Society for Industrial and Applied Mathematic, 352 pages.Google Scholar
Tarpley, L., Reddy, K.R. and Sassenrath Cole, G.F. (2000), Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration, Crop Science, 40(6):18141819.Google Scholar
Taylor, S.E. (1975), Optimal leaf form. In Perspectives of Biophysical Ecology (Gates, D.M. and Schmerl, R.B., Eds), Springer-Verlag, New York, pp. 7386.Google Scholar
Taylor-Hell, J.F., Baranoski, G.V.G. and Rokne, J.G. (2005), State of the art in the realistic modeling of plant venation systems, International Journal of Image and Graphics, 5(3):663678.Google Scholar
Temizel, K.E., Odabas, M.S., Senyer, N., Kayhan, G., Bajwa, S.G., Caliskan, O. et al. (2014), Comparison of some models for estimation of reflectance of hypericum leaves under stress conditions, Central European Journal of Biology, 9(12):12261234.Google Scholar
Teng, P.S. and Close, R.C. (1977), Spectral reflectance of healthy and leaf rust-infected barley leaves, Australian Plant Pathology Society Newsletter, 6(1):79.Google Scholar
Terashima, I. and Saeki, T. (1983), Light environment within a leaf. I: Optical properties of paradermal sections of Camellia leaves with a special reference to differences in the optical properties of palisade and spongy tissues, Plant & Cell Physiology, 24(8):14931501.Google Scholar
Terashima, I. and Saeki, T. (1985), A new model for leaf photosynthesis incorporating the gradients of light environment and of photosynthetic properties of chloroplasts within a leaf, Annals of Botany, 56(4):489499.Google Scholar
Terashima, I. and Inoue, Y. (1984), Comparative photosynthetic properties of palisade tissue chloroplasts and spongy tissue chloroplasts of Camellia japonica L.: functional adjustment of the photosynthetic apparatus to light environment within a leaf, Plant & Cell Physiology, 25(4):555563.Google Scholar
Terashima, I. (1986), Dorsiventrality in photosynthetic light response curves of a leaf, Journal of Experimental Botany, 37(176):399405.Google Scholar
Terashima, I. (1989), Productive structure of a leaf, in Photosynthesis (Briggs, W.R., Ed), Alan R. Liss, New York, pp. 207226.Google Scholar
Terashima, I. and Hikosaka, K. (1995), Comparative ecophysiology of leaf and canopy photosynthesis, Plant, Cell & Environment, 18(10):11111128.Google Scholar
Terashima, I., Fujita, T., Inoue, T., Chow, W.S. and Oguchi, R. (2009), Green light drives leaf photosynthesis more efficiently than red light in strong white light: revisiting the enigmatic question of why leaves are green, Plant Cell Physiology, 50(4):684697.Google Scholar
Terashima, I., Hanba, Y.T., Tholen, D. and Niinemets, U. (2011), Leaf functional anatomy in relation to photosynthesis, Plant Physiology, 155(1):108116.Google Scholar
Thenkabail, P.S., Lyon, J.G. and Huete, A. (2011), Hyperspectral Remote Sensing of Vegetation, CRC Press, 781 pages.Google Scholar
Thenot, F., Méthy, M. and Winkel, T. (2002), The Photochemical Reflectance Index (PRI) as a water-stress index, International Journal of Remote Sensing, 23(23):51355139.Google Scholar
Thérézien, M., Palmroth, S., Brady, R. and Oren, R. (2007), Estimation of light interception properties of conifer shoots by an improved photographic method and a 3D model of shoot structure, Tree Physiology, 27(10):13751387.Google Scholar
Thom, R. (1980), Paraboles et catastrophes, Flammarion, 193 pages.Google Scholar
Thomas, J.R., Wiegan, C.L. and Myer, V.I. (1967), Reflectance of cotton leaves and its relation to yield, Agronomy Journal, 59(6):551554.Google Scholar
Thomas, J.R. and Oerther, G.F. (1972), Estimating nitrogen content of sweet pepper leaves by reflectance measurements, Agronomy Journal, 64(1):1113.Google Scholar
Thomas, J.R. and Gausman, H.W. (1977), Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops, Agronomy Journal, 69(5):799802.Google Scholar
Thomas, K.R., Kolle, M., Whitney, H.M., Glover, B.J. and Steiner, U. (2010), Function of blue iridescence in tropical understorey plants, Journal of the Royal Society Interface, 7(53):16991707.Google Scholar
Thomas, S.C. (2005), Increased leaf reflectance in tropical trees under elevated CO2, Global Change Biology, 11(2):197202.Google Scholar
Thomas, S., Wahabzada, M., Kuska, M.T., Rascher, U. and Mahlein, A.K. (2017), Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements, Functional Plant Biology, 44(1):2334.Google Scholar
Thompson, D.W. (1992), On Growth and Form: The Complete Revised Edition, Cambridge University Press, Cambridge. 1116 pages.Google Scholar
Thompson, J.A., Schweitzer, L.E. and Nelson, R.L. (1996), Association of specific leaf weight, an estimate of chlorophyll, and chlorophyll concentration with apparent photosynthesis in soybean, Photosynthesis Research, 49(1):110.Google Scholar
Thygesen, L.G. (1994), Determination of dry matter content and the basic density of Norway spruce by near infrared reflectance and transmittance spectroscopy, Journal of Near Infrared Spectroscopy, 2(3):127135.Google Scholar
Tian, Q., Tong, Q., Pu, R., Guo, X. and Zhao, C. (2001), Spectroscopic determination of wheat water status using 1650–1850 nm spectral absorption features, International Journal of Remote Sensing, 22(12):23292338.Google Scholar
Tian, Y., Zhao, C., Lu, S. and Guo, X. (2010), Separating pigment components of leaf color image using FastICA, in Advanced Intelligent Computing Theories and Applications (Huang, D.S., Zhao, Z., Bevilacqua, V. and Figueroa, J.C., Eds), Springer-Verlag, Berlin, pp. 430437.Google Scholar
Tian, Y., Zhao, C., Lu, S. and Guo, X. (2011), Multiple classifier combination for recognition of wheat leaf diseases, Intelligent Automation and Soft Computing, 17(5):519529.Google Scholar
Tian, Y., Zhao, C., Lu, S. and Guo, X. (2012a), Advances in appearance modeling and photorealistic rendering of plant leaf, Journal of Image and Graphics, 17(5):613620 (in Chinese).Google Scholar
Tian, Y., Zhao, C., Lu, S. and Guo, X. (2012b), SVM-based multiple classifier system for recognition of wheat leaf diseases, in Proc. World Automation Congress, Puerto Vallarta, Mexico, 24–28 June 2012, pp. 189193.Google Scholar
Tidestrom, I. (1909), Notes on Populus, Plinius, American Midland Naturalist, 1(5):113118.Google Scholar
Tikhov, G. (1960), L’énigme des planètes, Editions en langues étrangères, 183 pages.Google Scholar
Tikhov, G.A. (1966), Observations of the Moon, Mars, Uranus, and the stars. Optical properties of plants, NASA, 320 pages.Google Scholar
Tilley, D.R., Ahmed, M., Son, J.H. and Badrinarayanan, H. (2007), Hyperspectral reflectance response of freshwater macrophytes to salinity in a Brackish subtropical marsh, Journal of Environmental Quality, 36(3):780789.Google Scholar
Timiriazeff, C. (1903), Croonian lecture: the cosmical function of the green plant, Proceedings of the Royal Society of London, 72:424461.Google Scholar
Toomey, M., Roberts, D. and Nelson, B. (2009), The influence of epiphylls on remote sensing of humid forests, Remote Sensing of Environment, 113(8):17871798.Google Scholar
Torella, J.P., Gagliardi, C.J., Chen, J.S., et al. (2015), Efficient solar-to-fuels production from a hybrid microbial–water-splitting catalyst system, Proceedings of the National Academy of Science of the United States of America, 112(8):23372342.Google Scholar
Torii, T., Okamoto, T. and Kitani, O. (1988), Non-destructive measurement of water content of a plant using ultrasonic technique, Acta Horticulturae, 230:389396.Google Scholar
Torrance, K.E. and Sparrow, E.M. (1967), Theory for off-specular reflection from roughened surfaces, Journal of the Optical Society of America, 57(9):11051114.Google Scholar
Torres Netto, A., Campostrini, E., Gonçalves de Oliveira, J. and Yamanishi, O.K. (2002), Portable chlorophyll meter for the quantification of photosynthetic pigments, nitrogen and the possible use for assessment of the photochemical process in Carica papaya L., Brazilian Journal of Plant Physiology, 14(3):203210.Google Scholar
Toth, R. (1982), An introduction to morphometric cytology and its application to botanical research, American Journal of Botany, 69(10):16941706.Google Scholar
Trench, R.K. (1979), The cell biology of plant-animal symbiosis, Annual Review of Plant Physiology, 30:485531.Google Scholar
Trigui, A. (1983), Evolution des propriétés biophysiques et optiques des jeunes feuilles d’olivier (Olea europaea L.) au cours de leur phase de croissance, Comptes Rendus de l’Académie des Sciences Paris, 297(3):543545.Google Scholar
Trojan, A. and Gabrys, H. (1996), Chloroplast distribution in Arabidopsis thaliana (L.) depends on light conditions during growth, Plant Physiology, 111(2):419425.Google Scholar
Trombetti, M., Riaño, D., Rubio, M.A., Cheng, Y.B. and Ustin, S.L. (2008), Multi-temporal vegetation canopy water content retrieval using artificial neural networks for the USA, Remote Sensing of Environment, 112(1):203215.Google Scholar
Tsel’niker, Y.L. (1975), Effect of light intensity on optical properties of chloroplasts and leaf tissues in trees, Soviet Plant Physiology, 22:592597 (cover-to-cover translation from Fiziologiya rastenii, 22(4):695701).Google Scholar
Tsukaya, H., Okada, H. and Mohamed, M. (2004), A novel feature of structural variegation in leaves of the tropical plant Schismatoglottis calyptrata, Journal of Plant Research, 117(6):477480.Google Scholar
Tucker, C.J. and Garratt, M.W. (1977), Leaf optical system modeled as a stochastic process, Applied Optics, 16(3):635642.Google Scholar
Tucker, C.J. (1980), Remote sensing of leaf water content in the near infrared, Remote Sensing of Environment, 10(1):2332.Google Scholar
Tuckerman, L.B. (1947), On the intensity of the light reflected from or transmitted through a pile of plates, Journal of the Optical Society of America, 37(10):818825.Google Scholar
Turnbull, M.C., Traub, W.A., Jucks, K.W., et al. (2006), Spectrum of a habitable world: Earthshine in the near-infrared, The Astrophysical Journal, 644(1):551559.Google Scholar
Turunen, M.T., Vogelmann, T.C. and Smith, W.K. (1999), UV screening in lodgepole pine (Pinus contorta ssp. latifolia) cotyledons and needles, International Journal of Plant Sciences, 160(2):315320.Google Scholar
Tyree, M.T. and Hammel, H.T. (1972), The measurement of the turgor pressure and the water relations of plants by the pressure-bomb technique, Journal of Experimental Botany, 23(1):267282.Google Scholar
Tyree, M.T. and Jarvis, P.G. (1982), Water in tissues and cells, in Physiological Plant Ecology II. Water Relations and Carbon Assimilation (Lange, O.L., Nobel, P.S., Osmond, C.B. and Ziegler, H., Eds), Springer-Verlag, Berlin, pp. 3677.Google Scholar
Uddling, J., Gelang-Alfredsson, J., Piikki, K. and Pleijel, H. (2007), Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings, Photosynthesis Research, 91:3746.Google Scholar
Ulaby, F.T. and Jedlicka, R.P. (1984), Microwave dielectric properties of plant materials, IEEE Transactions on Geoscience and Remote Sensing, 22(4):406415.Google Scholar
Ulissi, V., Antonucci, F., Benincasa, P., et al. (2011), Nitrogen concentration estimation in tomato leaves by VIS-NIR non-destructive spectroscopy, Sensors, 11(6):64116424.Google Scholar
Ullah, S., Groen, T.A., Schlerf, M., Skidmore, A.K., Nieuwenhuis, W. and Vaiphasa, C. (2012a), Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5–14 μm) to discriminate vegetation species, Sensors, 12(7):87558769.Google Scholar
Ullah, S., Schlerf, M., Skidmore, A.K. and Hecker, C. (2012b), Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra, Remote Sensing of Environment, 118:95102.Google Scholar
Ullah, S., Skidmore, A.K., Naeem, M. and Schlerf, M. (2012c), An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis, Science of the Total Environment, 437:145152.Google Scholar
Ullah, S., Skidmore, A.K., Naeem, M. and Schlerf, M. (2012d), Estimation of leaf water content from far infrared (2.5–14 µm) spectra using continuous wavelet analysis, in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS12), Munich, Germany, IEEE, pp. 4817–4820.Google Scholar
Ullah, S., Skidmore, A.K., Groen, T.A. and Schlerf, M. (2013), Evaluation of three proposed indices for the retrieval of leaf water content from the mid-wave infrared (2–6 µm) spectra, Agricultural and Forest Meteorology, 171–172:6571.Google Scholar
Ullah, S., Skidmore, A.K., Ramoelo, A., Groen, T.A., Naeem, M. and Ali, A. (2014), Retrieval of leaf water content spanning the visible to thermal infrared spectra, ISPRS Journal of Photogrammetry and Remote Sensing, 93:5664.Google Scholar
Umezaki, E. and Shimadaa, T. (1999), Measurement of temperature on Scindapsus leaves subjected to ultraviolet radiation using infrared thermography techniques, in Proc. Optical Engineering for Sensing and Nanotechnology (Yamaguchi I., Ed), Yokohama, Japan, 16–18 June 1999, SPIE, Vol. 3740, pp. 358361.Google Scholar
Underwood, E., Ustin, S.L. and DiPietro, D. (2003), Mapping nonnative plants using hyperspectral imagery, Remote Sensing of Environment, 86(2):150161.Google Scholar
Upchurch, G.R. (1995), Dispersed angiosperm cuticles: their history, preparation, and application to the rise of angiosperms in Cretaceous and Paleocene coals, southern western interior of North America, International Journal of Coal Geology, 28(2–4):161227.Google Scholar
Ursprung, A. (1918), Über die Absorptionskurve des grünen Farbstoffes lebender Blätter, Berichte der Deutschen Botanischen Gesellschaft, 36(2):7385.Google Scholar
Ustin, S.L., Adams, J.B., Elvidge, C.D., Rejmanek, M., Rock, B.N., Smith, M.O., et al. (1986), Thematic Mapper studies of semiarid shrub communities, BioScience, 36:446452.Google Scholar
Ustin, S.L. and Curtiss, B. (1990), Spectral characteristics of ozone-treated conifers, Environmental and Experimental Botany, 30(3):293308.Google Scholar
Ustin, S.L., Smith, S. Jacquemoud, M.M. Verstraete, Y. Govaerts, (1999), Geobotany: vegetation mapping for earth sciences, in Remote Sensing for the Earth Sciences, Manual of Remote Sensing, 3rd Edition, Vol. 3 (Rencz, A., Ed), John Wiley & Sons, New York. pp. 189248.Google Scholar
Ustin, S.L., Jacquemoud, S. and Govaerts, Y. (2001), Simulation of photon transport in a three-dimensional leaf: implications for photosynthesis, Plant, Cell & Environment, 24(10):10951103.Google Scholar
Ustin, S.L., Jacquemoud, S., Zarco-Tejada, P.J. and Asner, G.P. (2004), Remote sensing of the environment: state of the science and new directions, in Manual of Remote Sensing, Vol. 4: Remote Sensing for Natural Resource Management and Environmental Monitoring (Ustin, S.L., Ed), John Wiley & Sons, pp. 679729.Google Scholar
Ustin, S.L., Gitelson, A.A., Jacquemoud, S., Schaepman, M.E., Asner, G.P., Gamon, J.A., et al. (2009), Retrieval of foliar information about plant pigment systems from high resolution spectroscopy, Remote Sensing of Environment, 113(S1):S67S77.Google Scholar
Ustin, S.L. and Gamon, J.A. (2010), Remote sensing of plant functional types, New Phytologist, 186(4):795816.Google Scholar
Uto, K. and Kosugi, Y. (2012), Extraction of Lambert parameter from leaf scale hyperspectral images, in Proc. 4th Workshop on Hyperspectral Image and Signal Processing, Shanghai, China, 4-7 June 2012, IEEE, 4 pages.Google Scholar
Uto, K. and Kosugi, Y. (2013a), Estimation of Lambert parameter based on leaf-scale hyperspectral images using dichromatic model-based PCA, International Journal of Remote Sensing, 34(4):13861412.Google Scholar
Uto, K. and Kosugi, Y. (2013b), Leaf parameter estimation based on leaf scale hyperspectral imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2):699707.Google Scholar
van den Berg, A.K. and Perkins, T.D. (2004), Evaluation of a portable chlorophyll meter to estimate chlorophyll and nitrogen contents in sugar maple (Acer saccharum Marsh.) leaves, Forest Ecology and Management, 200(1–3):113117.Google Scholar
van den Berg, A.K. and Perkins, T.D. (2005), Nondestructive estimation of anthocyanin content in autumn sugar maple leaves, HortScience, 40(3):685686.Google Scholar
Vanderbilt, V.C. and Grant, L. (1985), Plant canopy specular reflectance model, IEEE Transactions on Geoscience and Remote Sensing, 23(5):722730.Google Scholar
Vanderbilt, V.C. and Grant, L. (1986), Polarization photometer to measure bidirectional reflectance factor R(55°,0°,55°,180°) of leaves, Optical Engineering, 25(4):566571.Google Scholar
Vanderbilt, V.C., DeWitt, D.P. and Robinson, B.F. (1987), Integrating sphere transmissiometer for field measurement of leaf transmittance, Optical Engineering, 26(12):11911196.Google Scholar
Vanderbilt, V.C., Grant, L. and Ustin, S.L. (1991), Polarization of light by vegetation. 2. Scattering by single leaves, in Photon-Vegetation Interactions (Myneni, R.B. and Ross, J., Eds), Springer-Verlag, New York, pp. 191228.Google Scholar
Vanderbilt, V.C. and Daughtry, C.S.T. (2012), Mueller matrix of a dicot leaf, in Proc. Polarization: Measurement, Analysis, and Remote Sensing X (Chenault, D.B. and Goldstein, D.H., Eds), Baltimore, MD, 23 April 2012, SPIE, Vol. 8364, 83640R-1.Google Scholar
Vanderbilt, V.C., Daughtry, C.S.T. and Biehl, L.L. (2014), Is there spectral variation in the polarized reflectance of leaves? in Proc. Polarization: Measurement, Analysis, and Remote Sensing XI (Chenault D.B. and Goldstein D.H., Eds), Baltimore, MD, 5 May 2014, SPIE, Vol. 9099, 909916.Google Scholar
van Deventer, H. and Cho, M.A. (2014), Assessing leaf spectral properties of Phragmites australis impacted by acid mine drainage, South African Journal of Science, 110(7–8):2013–0184.Google Scholar
van Deventer, H., Cho, M.A., Mutanga, O., Naidoo, L. and Dudeni-Tlhone, N. (2015), Reducing leaf-level hyperspectral data to 22 components of biochemical and biophysical bands optimizes tree species discrimination, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6):31613171.Google Scholar
van Maarschalkerweerd, M., Bro, R., Egebo, M. and Husted, S. (2013), Diagnosing latent copper deficiency in intact barley leaves (Hordeum vulgare, L.) using near infrared spectroscopy, Journal of Agricultural and Food Chemistry, 61(46):1090110910.Google Scholar
Van Wittenberghe, S., Verrelst, J., Rivera, J.P., Alonso, L., Moreno, J. and Samson, R. (2014), Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset, Journal of Photochemistry and Photobiology B: Biology, 134:3748.Google Scholar
Vargas, W.E. and Niklasson, G.A. (1997), Applicability conditions of the kubelka-Munk theory, Applied Optics, 36(22):55805586.Google Scholar
Velázquez-Rosas, N., Barradas, V.L., Vázquez-Santana, S., Cruz-Ortega, R., García-Jiménez, F., Toledo-Alvarado, E. et al. (2010), Optical and morpho-functional traits of the leaves of tree species growing in a mountain cloud forest, Acta Oecologica, 36(6):587598.Google Scholar
Verboven, P., Herremans, E., Helfen, L., Ho, Q.T., Abera, M., Baumbach, T., et al. (2015), Synchrotron X-ray computed laminography of the three-dimensional anatomy of tomato leaves, The Plant Journal, 81(1):169182.Google Scholar
Veres, J.S., Cofer, G.P. and Johnson, G.A. (1991), Distinguishing plant tissues with magnetic resonance microscopy, American Journal of Botany, 78(12):17041711.Google Scholar
Veres, J.S., Cofer, G.P. and Johnson, G.A. (1993), Magnetic resonance imaging of leaves, New Phytologist, 123(4):769774.Google Scholar
Verhoef, W. and Bach, H. (2003), Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models, Remote Sensing of Environment, 87(1):2341.Google Scholar
Verhoef, W. and Bach, H. (2007), Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data, Remote Sensing of Environment, 109(2):166182.Google Scholar
Verhoef, W. (2011), Modelling vegetation fluorescence observations, in Proc. 7th EARSEL workshop, Edinburgh, Scotland, 11–13 April 2011, EARSEL, pp. 4142.Google Scholar
Verdeil, M.F. (1851), Recherches sur la matière colorante verte des plantes et sur la matière rouge su sang, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 33:689690.Google Scholar
Verrelst, J., Rivera, J.P., van der Tol, C., Magnani, F., Mohammed, G. and Moreno, J. (2015), Global sensitivity analysis of the SCOPE model: what drives simulated canopy-leaving sun-induced fluorescence? Remote Sensing of Environment, 166:821.Google Scholar
Vidaver, W.E., Lister, G.R., Brooke, R.C. and Binder, W.D. (1991), A Manual for the Use of Variable Chlorophyll Fluorescence in the Assessment of the Ecophysiology of Conifer Seedlings, FRDA Report 163, 60 pages.Google Scholar
Vieira Santos, C. (2004), Regulation of chlorophyll biosynthesis and degradation by salt stress in sunflower leaves, Scientia Horticulturae, 103(1):9399.Google Scholar
Vigneron, J.P., Rassart, M., Vértesy, Z., et al (2005), Optical structure and function of the white filamentary hair covering the edelweiss bracts, Physical Review E, 71:011906.Google Scholar
Vignolini, S., Moyroud, E., Glover, B.J. and Steiner, U. (2013), Analysing photonic structures in plants, Journal of the Royal Society Interface, 10(87):20130394.Google Scholar
Vignolini, S., Moyroud, E., Hingant, T., et al. (2015), The flower of Hibiscus trionum is both visibly and measurably iridescent, New Phytologist, 205(1):97101.Google Scholar
Vile, D., Garnier, E., Shipley, B., et al. (2005), Specific leaf area and dry matter content estimate thickness in laminar leaves, Annals of Botany, 96(6):11291136.Google Scholar
Vilfan, N., van der Tol, C., Muller, O., Rascher, U. and Verhoef, W. (2016), Fluspect-B: A model for leaf fluorescence, reflectance and transmittance spectra, Remote Sensing of Environment, 186:596-615.Google Scholar
Vilfan, N., van der Tol, C., Yang, P., Wyber, R., Malenovský, Z., et al. (2018), Extending Fluspect to simulate xanthophyll driven leaf reflectance dynamics, Remote Sensing of Environment, 211:345-356.Google Scholar
Ville, G. (1889), Recherches sur les relations qui existent entre la couleur des plantes et la richesse des terres en agents de fertilité, Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 109:397400.Google Scholar
Villena, J.F., Domínguez, E., and Heredia, A. (2000), Monitoring biopolymers present in plant cuticles by FT-IR spectroscopy, Journal of Plant Physiology, 156(3):419422.Google Scholar
Vincent, J.F.V. (1999), From cellulose to cell, The Journal of Experimental Biology, 202:32633268.Google Scholar
Visser, A.J., Tosserams, M., Groen, M.W., Kalis, G., Kwant, R., Magendas, G.W.H. et al. (1997), The combined effects of CO2 concentration and enhanced UV-B radiation on faba bean. 3: Leaf optical properties, pigments, stomatal index and epidermal cell density, Plant Ecology, 128(1–2):209222.Google Scholar
Vitale, A.T. (1997), Leaves in Myth, Magic and Medicine, Stewart, Tabori & Chang, 352 pages.Google Scholar
Vogelmann, J.E., Rock, B.N. and Moss, D.M. (1993), Red edge spectral measurements from sugar maple leaves, International Journal of Remote Sensing, 14(8):15631575.Google Scholar
Vogelmann, T.C. and Björn, L.O. (1984), Measurement of light gradients and the spectral regime in plant tissue with a fiber optic probe, Physiologia Plantarum, 60(3):361368.Google Scholar
Vogelmann, T.C. and Björn, L.O. (1986), Plants as light traps, Physiologia Plantarum, 68(4):704708.Google Scholar
Vogelmann, T.C. (1986), Light within the plant, in Photomorphogenesis in Plants (Kendrick, R.E. and Kronenberg, G.H.M., Eds), Martinus Nijhoff Publishers, Dordrecht, pp. 307337.Google Scholar
Vogelmann, T.C. 1989), Penetration of light into plants, Photochemistry and Photobiology, 50(6):895902.Google Scholar
Vogelmann, T.C. Vogelmann, T.C. (1993), Plant tissue optics, Annual Review of Plant Physiology and Plant Molecular Biology, 44:231251.Google Scholar
Vogelmann, T.C., Knapp, A.K., McClean, T.M. and Smith, W.K. (1988), Measurements of light within thin plant tissues with fiber optic microprobes, Physiologia Plantarum, 72(3):623630.Google Scholar
Vogelmann, T.C., Bornman, J.F. and Josserand, S. (1989), Photosynthetic light gradients and spectral regime within leaves of Medicago sativa, Philosophical Transactions of the Royal Society of London. Series B, 323(1216):411421.Google Scholar
Vogelmann, T.C., Martin, G., Chen, G. and Buttry, B. (1991), Fibre optic microprobes and measurement of the light microenvironment within plant tissues, Advances in Botanical Research, 18:255295.Google Scholar
Vogelmann, T.C. and Martin, G. (1993), The functional significance of palisade tissue: penetration of directional versus diffuse light, Plant, Cell & Environment, 16(1):6572.Google Scholar
Vogelmann, T.C., Bornman, J.F. and Yates, D.J. (1996a), Focusing of light by leaf epidermal cells, Physiologia Plantarum, 98(1):4356.Google Scholar
Vogelmann, T.C., Nishio, N. and Smith, W.K. (1996b), Leaves and light capture: light propagation and gradients of carbon fixation within leaves, Trends in Plant Science, 1(2):6570.Google Scholar
Vogelmann, T.C. and Han, T. (2000), Measurement of gradients of absorbed light in spinach leaves from chlorophyll fluorescence profiles, Plant, Cell & Environment, 23(12):13031312.Google Scholar
Vogelmann, T.C. and Evans, J.R. (2002), Profiles of light absorption and chlorophyll within spinach leaves from chlorophyll fluorescence, Plant, Cell & Environment, 25(10):13131323.Google Scholar
Vogelmann, T.C. and Gorton, H.L. (2014), Leaf: light capture in the photosynthetic organ, in The Structural Basis of Biological Energy Generation (Hohmann-Marriott, M.F., Ed), Springer, The Netherlands, pp. 363377.Google Scholar
Von Schönermark, M., Geiger, B., Röser, H.P. (2004), Reflection Properties of Vegetation and Soils, Wissenschaft und Technik Verlag, 352 pages.Google Scholar
Voshchula, I.V., Zhumar, A.Y. and Tsaryuk, O.V. (2007), Elliptical polarization of laser light reflected from plant leaves and characteristics of the leaf cuticle, Biophysics, 52(4):418422.Google Scholar
Vrindts, E. and De Baerdemaeker, J. (1998), Optical weed detection and evaluation using reflection measurements, in Proc. Conference on Precision Agriculture and Biological Quality (Meyer G.E. and DeShazer J.A., Eds), Boston, MA, 01 November 1998, SPIE, Vol. 3543, pp. 279289.Google Scholar
Vukusic, P. and Sambles, J.R. (2003), Photonic structures in biology, Nature, 424:852855.Google Scholar
Wada, M., Kagawa, T. and Sato, Y. (2003), Chloroplast movement, Annual Review of Plant Biology, 54:455468.Google Scholar
Wägele, H. and Klussmann-Kolb, A. (2005), Opisthobranchia (Mollusca, Gastropoda) – more than just slimy slugs. Shell reduction and its implications on defense and foraging, Frontiers in Zoology, 2: 3.Google Scholar
Walczak, T. and Gabrys, H. (1980), New type of photometer for measurements of transmission changes corresponding to chloroplast movements in leaves, Photosynthetica, 14(1):6572.Google Scholar
Waller, C. and Seibert, A. (1955), Studies of the refractive indices of binary wax mixtures, Journal of the American Oil Chemists’ Society, 32(12):709712.Google Scholar
Wallihan, E.F. (1973), Portable reflectance meter for estimating chlorophyll concentrations in leaves, Agronomy Journal, 65(4):659662.Google Scholar
Walter, H. and Koch, W. (1981), Optical parameters of leaves of crops and weeds, in Proc. Signatures spectrales d’objets en télédétection, (Guyot G. and Verbrugghe M., Eds), Avignon, France, 8–11 September 1981, INRA, pp. 225232.Google Scholar
Walter, L., Balling, A., Zimmermann, U., Haase, A. and Kuhn, W. (1989), Nuclear-magnetic-resonance imaging of leaves of Mesembryanthemum crystallinum L. plants grown at high salinity, Planta, 178(4):524530.Google Scholar
Walter-Shea, E.A., Norman, J.M. and Blad, B.L. (1989), Leaf bidirectional reflectance and transmittance in corn and soybean, Remote Sensing of Environment, 29(2):161174.Google Scholar
Walter-Shea, E.A. and Biehl, L.L. (1990), Measuring vegetation spectral properties, Remote Sensing Reviews, 5(1):179205.Google Scholar
Walter-Shea, E.A., Norman, J.M., Blad, B.L. and Robinson, B.F. (1991), Leaf reflectance and transmittance in soybean and corn, Agronomy Journal, 83(3):631636.Google Scholar
Walthall, C., Daughtry, C.S.T., Pachepsky, L., Erbe, E., Lydon, J., Higgins, M., et al. (2006), Detection of illegal Cannabis cultivation using remote sensing in Proc. IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS’06), Denver, CO, 31 July–4 August 2006, IEEE, pp. 22812284.Google Scholar
Wang, B.J. and Ju, W. (2017), Limitations and improvements of the leaf optical properties model Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY), Remote Sensing, 9(5):431.Google Scholar
Wang, H., Shi, R.H., Liu, P.D. and Gao, W. (2016), Dual NDVI ratio vegetation index: a kind of vegetation index assessing leaf carotenoid content based on leaf optical properties, Spectroscopy and Spectral Analysis, 36(7):21892194 (in Chinese).Google Scholar
Wang, J., Xu, R., Ma, Y., Miao, L., Cai, R. and Chen, Y. (2008), The research of air pollution based on spectral features in leaf surface of Ficus microcarpa in Guangzhou, China, Environmental Monitoring and Assessment, 142(1–3):7383.Google Scholar
Wang, K., Shen, Z.Q., Abou-Ismail, O., Yaghi, A. and Wang, R.C. (1997), Preliminary study on canopy and leaf reflectance characteristics of rice with various potassium levels, Bulletin of Science and Technology, 13(4):211214 (in Chinese).Google Scholar
Wang, L. and Bai, Y. (2005), Correlation between corn leaves spectra reflectance and nutrient content under different potassium levels, in Proc. 9th International Symposium on Physical Measurements & Signatures in Remote Sensing (Liang S., Liu J., Li X., Liu R. and Schaepman M.E., Eds), Beijing, China, 17–19 October 2005, ISPRS, 3 pages.Google Scholar
Wang, L., Wang, W., Dorsey, J., Yang, X., Guo, B. and Shum, H.Y. (2005), Real-time rendering of plant leaves, in Proc. SIGGRAPH 2005, Los Angeles, CA, 31 July–4 August 2005, ACM, pp. 167174.Google Scholar
Wang, L., Bai, Y.L. and Yang, L.P. (2007), Spectral response and diagnosis of phosphorus nutrition in corn, Plant Nutrition and Fertilizer Science, 13 (5):802808 (in Chinese).Google Scholar
Wang, L., Luo, Y.Q., Huang, H.G., Shi, J., Keliövaara, K., Teng, W.X. et al. (2009a), Reflectance features of water stressed Larix gmelinii needles, Forestry Studies in China, 11(1):2833.Google Scholar
Wang, L. and Sousa, W.P. (2009), Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance, International Journal of Remote Sensing, 30(5):12671281.Google Scholar
Wang, L., Hunt, E.R., Qu, J.J., Hao, X. and Daughtry, C.S.T. (2011a), Estimating dry matter content of fresh leaves from the residuals between leaf and water reflectance, Remote Sensing Letters, 2(2):137145.Google Scholar
Wang, L., Qu, J.J., Hao, X. and Hunt, E.R. (2011b), Estimating dry matter content from spectral reflectance for green leaves of different species, International Journal of Remote Sensing, 32(22):70977109.Google Scholar
Wang, P., Liu, X.N. and Huang, F. (2010), Retrieval model for subtle variation of contamination stressed maize chlorophyll using hyperspectral data, Spectroscopy and Spectral Analysis, 30(1):197201.Google Scholar
Wang, Q., Chen, J. and Li, Y. (2004), Nondestructive and rapid estimation of leaf chlorophyll and nitrogen status of peace lily using a chlorophyll Meter, Journal of Plant Nutrition, 27(3):557569.Google Scholar
Wang, T., Liu, Y., Wu, H.Y. and Zuo, Y.M. (2012), Influence of foliar dust on crop reflectance spectrum and nitrogen monitoring, Spectroscopy and Spectral Analysis, 32(7):18951898 (in Chinese).Google Scholar
Wang, Q. and Li, P. (2012), Hyperspectral indices for estimating leaf biochemical properties in temperate deciduous forests: comparison of simulated and measured reflectance data sets, Ecological Indicators, 14(1):5665.Google Scholar
Wang, Q. and Jin, J. (2015), Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis, iForest, 9:3037.Google Scholar
Wang, X., Zhao, C., Lu, S. and Guo, X. (2009b), Survey on modeling and visualization of plant leaf color, in Proc. Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Beijing, China, 9–13 November 2009, IEEE, pp. 417424.Google Scholar
Wang, Y. and Cuitiño, A.M. (2000), Three-dimensional nonlinear open-cell foams with large deformations, Journal of the Mechanics and Physics of Solids, 48(5):961988.Google Scholar
Wang, Y., Hao, W., Wang, G., Ning, X., Tang, J., Shi, Z., et al. (2013), A method of realistic leaves modeling based on point cloud, in Proc. 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, Hong Kong, 17–19 November 2013, pp. 123130.Google Scholar
Wang, Z., Skidmore, A., Darvishzadeh, R., Heiden, U., Heurich, M. and Wang, T. (2015a), Leaf nitrogen content indirectly estimated by leaf traits derived from the PROSPECT model, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6):31723182.Google Scholar
Wang, Z., Skidmore, A.K., Wang, T., Darvishzadeh, R. and Hearne, J. (2015b), Applicability of the prospect model for estimating protein and cellulose + lignin in fresh leaves, Remote Sensing of Environment, 168:205218.Google Scholar
Waters, K.R., Mobley, J. and Miller, J.G. (2005), Causality-imposed (Kramers-Kronig) relationships between attenuation and dispersion, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 52(5):822833.Google Scholar
Watté, R., Aernouts, B., Van Beers, R., Herremans, E., Ho, Q.T., Verboven, P., et al. (2015), Modeling the propagation of light in realistic tissue structures with MMC-fpf: a Meshed Monte Carlo method with free phase function, Optics Express, 23(13):1746717486.Google Scholar
Waugh, G.R. and Clark, K.B. (1986), Seasonal and geographic variation in chlorophyll level of Elysia tuca (Ascoglossa: Opisthobranchia), Marine Biology, 92(4):483–387.Google Scholar
Weiss, A. (1990), Leaf wetness: measurements and models, Remote Sensing Reviews, 5(1):215224.Google Scholar
Weiss, M., Troufleau, D., Baret, F., Chauki, H., Prévot, L., Olioso, A., et al. (2001), Coupling canopy functioning and radiative transfer models for remote sensing data assimilation, Agricultural and Forest Meteorology, 108(2):113128.Google Scholar
Welch, A.J., Gardner, C., Richards-Kortum, R., Chan, E., Criswell, G., Pfefer, J. et al. (1997), Propagation of fluorescent light, Lasers in Surgery and Medicine, 21(2):166178.Google Scholar
Venn, A.A., Loram, J.E., Trapido-Rosenthal, H.G., Joyce, D.A. and Douglas, A.E. (2008), Importance of time and place: patterns in abundance of symbiodinium Clades A and B in the tropical sea anemone Condylactis gigantean, The Biological Bulletin, 215(3):243252.Google Scholar
Wessman, C.A., Aber, J.D., Peterson, D.L. and Melillo, J.M. (1988a), Foliar analysis using near infrared reflectance spectroscopy, Canadian Journal of Forest Research, 18(1):611.Google Scholar
Wessman, C.A., Aber, J.D., Peterson, D.L. and Melillo, J.M. (1988b), Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems, Nature, 335(6186):154156.Google Scholar
Wessman, C.A., Aber, J.D. and Peterson, D.L. (1989), An evaluation of imaging spectrometry for estimating forest canopy chemistry, International Journal of Remote Sensing, 10(8):12931316.Google Scholar
Wessman, C.A. (1990), Evaluation of canopy biochemistry. In Remote Sensing of Biosphere Functioning (Hobbs, R.J. and Mooney, H.A., Eds), Springer-Verlag, New York, pp. 135156.Google Scholar
Wessman, C.A. (1992), Spatial scales and global change: bridging the gap from plots to CGM grid cells, Annual Review of Ecology and Systematics, 23:175200.Google Scholar
Westman, W.E. and Price, C.V. (1988), Spectral changes in conifers subjected to air pollution and water stress: experimental studies, IEEE Transactions on Geoscience and Remote Sensing, 26(1):1121.Google Scholar
Weybrew, J.A. and Green, P.E. (1952), Variation in coloring rates of tobacco, Science, 115(2991):466468.Google Scholar
White, J.D., Trotter, C.M., Brown, C.J. and Scott, N. (2000), Nitrogen concentration in New Zealand vegetation foliage derived from laboratory and field spectrometry, International Journal of Remote Sensing, 21(12):25252531.Google Scholar
White, N.S., Errington, R.J., Fricker, M.D. and Wood, J.L. (1996), Aberration control in quantitative imaging of botanical specimens by multidimensional fluorescence microscopy, Journal of Microscopy, 181(2):99116.Google Scholar
Wickham, J., Chesley, M., Lancaster, J. and Mouat, D. (1993), Remote Sensing for the Geobotanical and Biogeochemical Assessment of Environmental Contamination, Desert Research Institute, Reno, NV, DOE/NV/10845–27, 79 pages.Google Scholar
Wilkinson, D.M., Sherratt, T.N., Phillip, D.M., Wratten, S.D., Dixon, A.F.G. and Young, A.J. (2002), The adaptive significance of autumn leaf colours, Oikos, 99(2):402407.Google Scholar
Williams, D.L. (1991), A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected conifer species, Remote Sensing of Environment, 35(2–3):7993.Google Scholar
Williams, J.A. and Ashenden, T.W. (1992), Differences in the spectral characteristics of white clover exposed to gaseous pollutants and acid mist, New Phytologist, 120(1):6975.Google Scholar
Williams, P. and Norris, K. (1987), Near-Infrared Technology in the Agricultural and Food Industries, American Association of Cereal Chemists, St Paul, MN, 330 pages.Google Scholar
Williams, W.E., Gorton, H.L. and Witiak, S.M. (2003), Chloroplast movements in the field, Plant, Cell & Environment, 26(12):20052014.Google Scholar
Willstätter, R. and Stoll, A. (1918), Untersuchungen über die Assimilation der Kohlensäure, Verlag von Julius Springer, Berlin, 448 pages.Google Scholar
Wilson, P.S. and Dunton, K.H. (2009), Laboratory investigation of the acoustic response of seagrass tissue in the frequency band 0.5–2.5 kHz, The Journal of the Acoustical Society of America, 125(4):19511959.Google Scholar
Wilts, B.D., Whitney, H.M., Glover, B.J., Steiner, U. and Vignolini, S. (2014), Natural helicoidal structures: morphology, self-assembly and optical properties, Materials Today: Proceedings, 1:177185.Google Scholar
Windham, W.R., Poole, G.H., Park, B., Heitschmidt, G., Hawkins, S.A., Albano, J.P., et al. (2011), Rapid screening of Huanglongbing-infected citrus leaves by near infrared reflectance spectroscopy, Transactions of the ASABE, 54(6):22532258.Google Scholar
Wing, S.L. (1992), High-resolution leaf X-radiography in systematics and paleobotany, American Journal of Botany, 79(11):13201324.Google Scholar
Winkel-Shirley, B. (2001), Flavonoid biosynthesis. A colorful model for genetics, biochemistry, cell biology, and biotechnology, Plant Physiology, 126(2):485493.Google Scholar
Witkowski, E.T.F. and Lamont, B.B. (1991), Leaf specific mass confounds leaf density and thickness, Oecologia, 88:486493.Google Scholar
Witt, C., Pasuquin, J.M.C.A., Mutters, R. and Buresh, R.J. (2005), New leaf color chart for effective nitrogen management in rice, Better Crops, 89(1):3639.Google Scholar
Woessner, P. and Hapke, B. (1987), Polarization of light scattered by clovers, Remote Sensing of Environment, 21(3):243261.Google Scholar
Wold, S., Ruhe, A., Wold, H. and Dunn, W.J. (1984), The collinearity problem in linear regression. The partial leastsquares (PLS) regression approach to generalized inverses, Siam Journal on Scientific and Statistical Computing, 5(3):735743.Google Scholar
Wold, S., Sjöström, M. and Eriksson, L. (2001), PLS-regression: a basic tool of chemometrics, Chemometrics and Intelligent Laboratory Systems, 58(2):109130.Google Scholar
Wolfe, J.A. and Upchurch, G.R. (1986), Vegetation, climatic and floral changes at the Cretaceous-Tertiary boundary, Nature, 324:148152.Google Scholar
Wolfe, J.A. and Upchurch, G.R. (1987), Leaf assemblages across the Cretaceous-Tertiary boundary in the Raton Basin, New Mexico and Colorado, Proceedings of the National Academy of Sciences of the United States of America, 84(15):50965100.Google Scholar
Wolstencroft, R.D., Tranter, G.E. and Le Pevelen, D.D. (2002), Diffuse reflectance circular dichroism for the detection of molecular chirality: an application in remote sensing of flora, in Proc. Bioastronomy 2002: Life Among the Stars (Norris R. & Stootman F., eds), Astronomical Society of the Pacific, pp. 149153.Google Scholar
Wong, C.L. and Blevin, W.R. (1967), Infrared reflectances of plant leaves, Australian Journal of Biological Sciences, 20(3):501508.Google Scholar
Woodall, G.S., Dodd, I.C. and Stewart, G.R. (1998), Contrasting leaf development within the genus Syzygium, Journal of Experimental Botany, 49(318):7987.Google Scholar
Woodward, F.I., Lomas, M.R. and Kelly, C.K. (2004), Global climate and the distribution of plant biomes, Proceedings of the Royal Society B, 359(1450):14651476.Google Scholar
Woolf, N.J., Smith, P.S., Traub, W.A. and Jucks, K.W. (2002), The spectrum of earthshine: a pale blue dot observed from the ground, The Astrophysical Journal, 574(1):430433.Google Scholar
Woolley, J.T. (1971), Reflectance and transmittance of light by leaves, Plant Physiology, 47(5):656662.Google Scholar
Woolley, J.T. (1975), Refractive index of soybean leaf cell walls, Plant Physiology, 55(2):172174.Google Scholar
Wozniak, B. and Dera, J. (2007), Light Absorption in Sea Water, Springer-Verlag, New York, 453 pages.Google Scholar
Wright, I.J, Reich, P.B., Westoby, M., Ackerly, D.D., Baruch, Z., Bongers, F., et al. (2004). The worldwide leaf economics spectrum, Nature, 428:821827.Google Scholar
Wu, C. and Wang, X. (2014), Effects of foliar dust on plant reflectance spectra and physiological ecology: a review, Chinese Journal of Applied & Environmental Biology, 20 (6):11321138 (in Chinese).Google Scholar
Wu, D., Feng, L., Zhang, C. and He, Y. (2008), Early detection of Botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy, Transactions of the ASABE, 51(3):11331139.Google Scholar
Wu, J., Feld, M.S. and Rava, R.P. (1993), An analytical model for extracting intrinsic fluorescence in a turbid media, Applied Optics, 32(19):35853595.Google Scholar
Wu, J., Zhang, J., , A. and Zhou, L. (2012), An exploratory analysis of spectral indices to estimate vegetation water content using sensitivity function, Remote Sensing Letters, 3(2):161169.Google Scholar
Wu, J., Chavana-Bryant, C., Prohaska, N., Serbin, S.P., Guan, K., Albert, L.P., et al. (2017), Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests, New Phytologist, 214(3):10331048.Google Scholar
Wu, T., Zhang, L., Cen, Y., Huang, C., Sun, X., Zhao, H. et al. (2013), Polarized spectral measurement and analysis of Sedum spectabile Boreau using a field imaging spectrometer system, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2):724730.Google Scholar
Wuenscher, J.E. (1970), The effect of leaf hairs of Verbascum thapsus on leaf energy exchange, New Phytologist, 69(1):6573.Google Scholar
Wuhrmann-Meyer, K. and Wuhrmann-Meyer, M. (1941), Untersuchungen über die Absorption ultravioletter Strahlen durch Kutikular- und Wachsschichten von Blättern. I, Planta, 32(1):4350.Google Scholar
Wuyts, N., Palauqui, J.C., Conejero, G., Verdeil, J.L., Granier, C. and Massonnet, C. (2010), High-contrast three-dimensional imaging of the Arabidopsis leaf enables the analysis of cell dimensions in the epidermis and mesophyll, Plant Methods, 6:17.Google Scholar
Xiao, Y., Zhao, W., Zhou, D. and Gong, H. (2014), Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales, IEEE Transactions on Geoscience and Remote Sensing, 5(7):40144024.Google Scholar
Xiao, Y., Tholen, D. and Zhu, X.G. (2016), The influence of leaf anatomy on the internal light environment and photosynthetic electron transport rate: exploration with a new leaf ray tracing model, Journal of Experimental Botany, 67(21):60216035.Google Scholar
Xie, D.H., Wang, P.J. and Zhu, Q.J. (2010), Modeling polarimetric BRDF of leaves surfaces, Spectroscopy and Spectral Analysis, 30(12):33243328 (in Chinese).Google Scholar
Xie, D.H., Qin, W.H., Wang, P.J., Shuai, Y.M., Zhou, Y.Y. and Zhu, Q.J. (2017), Influences of leaf-specular reflection on canopy BRF characteristics: a case study of real maize canopies with a 3-D scene BRDF model, IEEE Transactions on Geoscience and Remote Sensing, 55(2):619631.Google Scholar
Xie, L.J., Ying, Y.B. and Ying, T.J. (2007), Quantification of chlorophyll content and classification of nontransgenic and transgenic tomato leaves using visible/near-infrared diffuse reflectance spectroscopy, Journal of Agricultural and Food Chemistry, 55(12):46454650.Google Scholar
Xie, X., Jiang, H. and Yua, S. (2009), Study on leaf reflectance and red edge characteristics of Chinese fir (Cunninghamia lanceolata) caused by acid rain with hyperspectral, in Proc. Remote Sensing and GIS Data Processing and Other Applications (Maître H., Sun H., Lei B. and Feng J., Eds), Yichang, China, 30 October 2009, SPIE, Vol. 7498, 74980Q.Google Scholar
Xu, C. and Gertner, G. (2011), Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST), Computational Statistics & Data Analysis, 55(1):184198.Google Scholar
Xu, H.R., Ying, Y.B. and Ye, Z. (2005), Application of near infrared spectroscopy to predict plant diseases, in Proc. Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality (Chen Y.R., Meyer G.E. and Tu S.I., Eds), Boston, MA, 23–26 October 2005, SPIE, Vol. 5996, 59960A.Google Scholar
Xu, H.R., Ying, Y.B., Fu, X.P. and Zhu, S.P. (2007), Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf, Biosystems Engineering, 96(4):447454.Google Scholar
Xu, H.R., Yu, P., Fu, X.P. and Ying, Y.B. (2009), On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy, Journal of Zhejiang University SCIENCE B, 10(2):126132.Google Scholar
Xu, J.H. and Yu, J.T. (2013), Air dustfall impact on spectrum of Ficus microcarpa’s leaf, Advanced Materials Research, 655–657:813815.Google Scholar
Xu, R. and Ma, Y. (2004), Remote sensing research in biogeochemistry of the Hetai gold deposit, Guangdong Province, China, International Journal of Remote Sensing, 25(2):437453.Google Scholar
Xue, L. and Yang, L. (2009), Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance, ISPRS Journal of Photogrammetry and Remote Sensing, 64(1):97106.Google Scholar
Xue, Z., Gao, H. and Zhao, S. (2014), Effects of cadmium on the photosynthetic activity in mature and young leaves of soybean plants, Environmental Science and Pollution Research, 21(6):46564664.Google Scholar
Yadava, U.L. (1986), A rapid nondestructive method to determine chlorophyll in intact leaves, HortScience, 21(6):14491450.Google Scholar
Yamada, N. and Fujimura, S. (1991), Nondestructive measurement of chlorophyll pigment content in plant leaves from three-color reflectance and transmittance, Applied Optics, 30(27):39643973.Google Scholar
Yamamoto, A., Nakamura, T., Adu-Gyamfi, J.J. and Saigusa, M. (2002), Relationship between chlorophyll content in leaves of sorghum and pigeonpea determined by extraction method and by chlorophyll meter (SPAD-502), Journal of Plant Nutrition, 25(10):22952301.Google Scholar
Yamamoto, H., Suzuki, Y. and Hayakawa, S. (1993), Estimation of amount of volcanic ashes piled on plant leaves by spectral reflectance, Journal of the Remote Sensing Society of Japan, 13(3):240248 (in Japanese).Google Scholar
Yamamoto, H., Suzuki, Y., Kojima, T., Hayakawa, S., Inoue, Y. and Tanaka, M. (1994), Estimation of leaf water content of plants by spectral reflectance of near infrared range, Journal of the Remote Sensing Society of Japan, 14(4):293301 (in Japanese.Google Scholar
Yamamoto, H., Suzuki, Y. and Hayakawa, S. (1995), The effects of overlapping, thickness and water content of plant leaves in spectral reflectance, Journal of the Remote Sensing Society of Japan, 15(5):463470 (in Japanese).Google Scholar
Yamazaki, K. (2010), Leaf mines as visual defensive signals to herbivores, Oikos, 119(5):796801.Google Scholar
Yan, X., Shi, W., Zhao, W. and Luo, N. (2014), Estimation of atmospheric dust deposition on plant leaves based on spectral features, Spectroscopy Letters, 47(7):536542.Google Scholar
Yáñez-Rausell, L., Schaepman, M.E. and Clevers, J.G.P.W. (2014a), Minimizing measurement uncertainties of coniferous needle-leaf optical properties, Part I: Methodological review, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2):399405.Google Scholar
Yáñez-Rausell, L., Malenovský, Z., Clevers, J.G.P.W. and Schaepman, M.E. (2014b), Minimizing measurement uncertainties of coniferous needle-leaf optical properties, Part II: Experimental setup and error analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2):406420.Google Scholar
Yang, B., Knyazikhin, Y., Lin, Y., Yan, K., Chen, C., Park, T., et al. (2016a), Analyses of impact of needle surface properties on estimation of needle absorption spectrum: case study with coniferous needle and shoot samples, Remote Sensing, 8(7):563.Google Scholar
Yang, F., Lu, Y., Zhou, G., Pan, Y. and Hu, H. (2009), Heavy metal content estimation in leaf by spectrum features of plant in De-Xing copper mining area, in Proc. 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications (Ye S. and Zhang G., Eds), Beijing, China, 16–19 November 2008, SPIE, Vol. 7160, 71601B.Google Scholar
Yang, G., Zhao, C., Pu, R., Feng, H., Li, Z., Li, H. et al. (2015), Leaf nitrogen spectral reflectance model of winter wheat (Triticum aestivum) based on PROSPECT: simulation and inversion, Journal of Applied Remote Sensing, 9(1):095976–1.Google Scholar
Yang, W.H., Peng, S., Huang, J., Sanico, A.L., Buresh, R.J. and Witt, C. (2003), Using leaf color charts to estimate leaf nitrogen status of rice, Agronomy Journal, 95(1):212217.Google Scholar
Yang, X., Tang, J. and Mustard, J.F. (2014), Beyond leaf color: comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest, Journal of Geophysical Research: Biogeosciences, 119(3):181191.Google Scholar
Yang, X., Tang, J., Mustard, J.F., Wu, J., Zhao, K., Serbin, S. et al. (2016), Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests, Remote Sensing of Environment, 179:112.Google Scholar
Yang, X.S., Heisler, G.M., Montgomery, M.E., Sullivan, J.H., Whereat, E.B. and Miller, D.R. (1995), Radiative properties of hardwood leaves to ultraviolet-irradiation, International Journal of Biometeorology, 38(2):6066.Google Scholar
Yang, Y., Liu, Z., Hu, B., Man, Y. and Wu, W. (2010), Bionic composite material simulating the optical spectra of plant leaves, Journal of Bionic Engineering, 7(S1):S43S49.Google Scholar
Yang, Y., Liu, Z., Hu, B. and Wu, W. (2011a), Design of plant leaf bionic camouflage materials based on spectral analysis, Spectroscopy and Spectral Analysis, 31(6):16681672 (in Chinese).Google Scholar
Yang, Y., Hu, B. and Wu, W. (2011b), Design and preparation of bionic camouflage materials by simulating plant leaves, Journal of National University of Defense Technology, 33(5):5053 (in Chinese).Google Scholar
Yanovskaya, E.A., Yanovskii, A.F. and Dlugunovich, V.A. (1991), Polarizational characteristics of radiation reflected from potato leaves, Journal of Applied Spectroscopy, 55(4):10291032 (Translated from Zhurnal Prikladnoi Spektroskopii, 55(A):630634).Google Scholar
Yao, X., Tang, S.P., Cao, W.X., Tian, Y.C. and Zhu, Y. (2011), Estimating the nitrogen content in wheat leaves by near-infrared reflectance spectroscopy, Chinese Journal of Plant Ecology, 35(8):844852 (in Chinese).Google Scholar
Yao, Y., Li, W., Wen, S. and Zhao, Y. (2012), Vegetational spectral characteristics in Hongtoushan mining area, Liaoning Province, in Proc. 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China, 1–3 June 2012, IEEE, 4 pages.Google Scholar
Yates, D.J. (1981), Effect of the angle of incidence of light on the net photosynthesis rates of Sorghum almum leaves, Australian Journal of Plant Physiology, 8(3):335346.Google Scholar
Ye, H., Yuan, Z. and Zhang, S. (2013), The heat and mass transfer analysis of a leaf, Journal of Bionic Engineering, 10(2):170176.Google Scholar
Ye, H., Gao, Y., Li, S. and Guo, L. (2015), Bionic leaves imitating the transpiration and solar spectrum reflection characteristics of natural leaves, Journal of Bionic Engineering, 12(1):109116.Google Scholar
Yeh, P. (1988), Optical Waves in Layered Media, John Wiley & Sons, 406 pages.Google Scholar
Yi, Q.X., Huang, J.F., Wang, F.M., Wang, X.Z. and Liu, Z.Y. (2007), Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network, Environmental Science & Technology, 41(19):67706775.Google Scholar
Yi, S.L., Deng, L., He, S.L., Zheng, Y.Q. and Mao, S.S. (2010a), A spectrum based models for monitoring leaf potassium content of Citrus sinensis (L) cv. Jincheng orange, Scientia Agricultura Sinica, 43(4):780786.Google Scholar
Yi, S.L., Deng, L., He, S.L., Zheng, Y.Q., Wang, L. and Zhao, X.A. (2010b), Research on zinc content in leaf of Olinda Valencia orange using visible near infrared spectroscopy model, Spectroscopy and Spectral Analysis, 30(11):29272931 (in Chinese).Google Scholar
Yoder, B.J. and Daley, L.S. (1990), Development of a visible spectroscopic method for determining chlorophyll a and b in vivo in leaf samples, Spectroscopy, 5(8):4450.Google Scholar
Yoder, B.J. and Pettigrew-Crosby, R.E. (1995), Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales, Remote Sensing of Environment, 53(3):199211.Google Scholar
Yoshimura, H., Kobasi, S., Ohte, K. and Senoo, T. (1991a), Characteristics and colorimetric analysis of the spectral reflectance of tree leaves, Journal of the Remote Sensing Society of Japan, 11(2):199211 (in Japanese).Google Scholar
Yoshimura, H., Tanaka, S., Kobasi, S., Ohte, K., Senoo, T. and Kunitomo, M. (1991b), Seasonal change of near-infrared reflectance of single-leaf and multiple-leaf, Journal of the Remote Sensing Society of Japan, 11(4):607621 (in Japanese).Google Scholar
Yoshimura, H. (1998), Spectral properties of tree leaves with ageing and the effect of leaf stacking in the near-infrared region, Journal of the Remote Sensing Society of Japan, 18(1):4256 (in Japanese).Google Scholar
Yoshimura, H. (2001), The mechanism of remote detection concerning the xanthophyll cycle pigments, Journal of the Remote Sensing Society of Japan, 21(4):373376 (in Japanese).Google Scholar
Yoshimura, H., Zhu, H., Wu, Y. and Ma, R. (2010), Spectral properties of plant leaves pertaining to urban landscape design of broad-spectrum solar ultraviolet radiation reduction, International Journal of Biometeorology, 54(2):179191.Google Scholar
Younes, H.A., Abdel-Aal, R.M., Khodair, M.M. and Abdel-Samie, A.G. (1974), Spectral reflectance studies on mineral deficiency in corn plants, in Proc. 9th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, 15–19 April 1974, ERIM, Vol. 2, pp. 11051125.Google Scholar
Young, A.J. (1991), The photoprotective role of carotenoids in higher plants, Physiologia Plantarum, 83:702708.Google Scholar
Yu, G.R., Miwa, T., Nakayama, K., Matsuoka, N. and Kon, H. (2000), A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties, Plant and Soil, 227(1–2):4758.Google Scholar
Yuan, L., Zhang, J., Zhao, J., Cai, S. and Wang, J. (2013), Selection of leaf orientation insensitive bands for yellow rust detection, in Computer and Computing Technologies in Agriculture VI (Li, D. and Chen, Y., Eds), Springer-Verlag, Berlin, pp. 7884.Google Scholar
Yuan, L., Huang, Y., Loraamm, R.W., Nie, C., Wang, J. and Zhang, J. (2014a), Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects, Field Crops Research, 156:199207.Google Scholar
Yuan, Z., Ye, H. and Li, S. (2014b), Bionic leaf simulating the thermal effect of natural leaf transpiration, Journal of Bionic Engineering, 11(1):9097.Google Scholar
Yusuf, B.L. and He, Y. (2011), Application of hyperspectral imaging sensor to differentiate between the moisture and reflectance of healthy and infected tobacco leaves, African Journal of Agricultural Research, 6(29):62676280.Google Scholar
Yuzhu, H., Xiaomei, W. and Shuyao, S. (2011), Nitrogen determination in pepper (Capsicum frutescens L.) plants by color image analysis (RGB), African Journal of Biotechnology, 10(77):1773717741.Google Scholar
Zakaluk, R. and Ranjan, R.S. (2007), Artificial neural network modelling of leaf water potential for potatoes using RGB digital images: a greenhouse study, Potato Research, 49(4):255272.Google Scholar
Zakharov, V.P., Bratchenko, I.A., Sindyaeva, A.R. and Timchenko, E.V. (2009), Modeling of optical radiation energy distribution in plant tissue, Optics and Spectroscopy, 107(9):903908.Google Scholar
Zakharov, V.P., Bratchenko, I.A. and Timchenko, E.V. (2010), Optical model of plant tissue, Optics and Spectroscopy, 109(2):232236.Google Scholar
Zamblé Fidèle, T.B., Sadaiou Sabas, B.Y., Maxime, A.D. and Dongui, B.K. (2014), Biomonitoring de la pollution urbaine en zone tropicale à partir des caractéristiques spectrales et anatomiques des feuilles de Ficus polita Vahl, International Journal of Innovation and Applied Studies, 8(2):861870.Google Scholar
Zarco-Tejada, P.J., Miller, J.R., Mohammed, G.H. and Noland, T.L. (2000), Chlorophyll fluorescence effects on vegetation apparent reflectance. I: Leaf-level measurements and model simulation, Remote Sensing of Environment, 74(3):582595.Google Scholar
Zarco-Tejada, P.J., Miller, J.R., Mohammed, G.H., Noland, T.L. and Sampson, P.H. (2002), Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery, Journal of Environmental Quality, 31(5):14331441.Google Scholar
Zarco-Tejada, P.J., Pushnik, P.J., Dobrowski, J.C. and Ustin, S.L. (2003a), Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects, Remote Sensing of Environment, 84(2):283294.Google Scholar
Zarco-Tejada, P.J., Rueda, C.A. and Ustin, S.L. (2003b), Water content estimation in vegetation with MODIS reflectance data and model inversion methods, Remote Sensing of Environment, 85(1):109124.Google Scholar
Zarco-Tejada, P.J., Miller, J.R., Harron, J., Hu, B., Noland, T.L., Goel, N., et al. (2004a), Needle chlorophyll content estimation through model inversion using hyperspectral data from Boreal conifer forest canopies, Remote Sensing of Environment, 89(2):19891999.Google Scholar
Zarco-Tejada, P.J., Miller, J.R., Morales, A., Berjona, A. and Agüerad, J. (2004b), Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops, Remote Sensing of Environment, 90(4):463476.Google Scholar
Zeeman, S.C., Smith, S.M., snd Smith, A.M. (2007), The diurnal metabolism of leaf starch, Biochemical Journal, 401(1):1328.Google Scholar
Zhai, Y., Cui, L., Zhou, X., Gao, Y., Fei, T. and Gao, W. (2013), Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial leastsquare regression and support vector machine regression methods, International Journal of Remote Sensing, 34(7):25022518.Google Scholar
Zhang, C., Kovacs, J.M., Wachowiak, M.P. and Flores-Verdugo, F. (2013), Relationship between hyperspectral measurements and mangrove leaf nitrogen concentrations, Remote Sensing, 5(2):891908.Google Scholar
Zhang, H. and Zhang, J.C. (2008), Near-infrared green camouflage of cotton fabrics using vat dyes, Journal of the Textile Institute, 99(1):8388.Google Scholar
Zhang, H., Hu, H., Zhang, X.B., et al. (2011), Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network, Acta Physiologiae Plantarum, 33(6):24612466.Google Scholar
Zhang, J., Sokhansanj, S., Wu, S., Fang, R., Yang, W. and Winter, P. (1998), A transformation technique from RGB signals to the Munsell system for color analysis of tobacco leaves, Computers and Electronics in Agriculture, 19(2):155166.Google Scholar
Zhang, J.C., Pu, R.L., Wang, J.H., Huang, W.J., Yuan, L. and Luo, J.H. (2012a), Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements, Computers and Electronics in Agriculture, 85:1323.Google Scholar
Zhang, J.C., Yuan, L., Wang, J.H., Huang, W.J., Chen, L.P. and Zhang, D.Y. (2012b), Spectroscopic leaf level detection of powdery mildew for winter wheat using continuous wavelet analysis, Journal of Integrative Agriculture, 11(9):14741484.Google Scholar
Zhang, J.C., Yuan, L., Pu, R., Loraamm, R.W., Yang, G. and Wang, J. (2014), Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat, Computers and Electronics in Agriculture, 100:7987.Google Scholar
Zhang, Q., Xiao, X., Braswell, B., Linder, E., Baret, F. and Moore, B. (2005), Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model, Remote Sensing of Environment, 99(3):357371.Google Scholar
Zhang, Q., Li, Q. and Zhang, G. (2012c), Rapid determination of leaf water content using VIS/NIR spectroscopy analysis with wavelength selection, Spectroscopy, 27(2):93105.Google Scholar
Zhang, S. and Wang, Q. (2015), Inverse retrieval of chlorophyll from reflected spectra for assimilating branches of drought-tolerant Tamarix ramosissima, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4):14981505.Google Scholar
Zhang, X.J. and Li, M.Z. (2008), Analysis and estimation of the phosphorus content in cucumber leaf in greenhouse by spectroscopy, Spectroscopy and Spectral Analysis, 28(10):24042408.Google Scholar
Zhang, X. and He, Y. (2013), Rapid estimation of seed yield using hyperspectral images of oilseed rape leaves, Industrial Crops and Products, 42:416420.Google Scholar
Zhang, Y., Chen, J.M. and Thomas, S.C. (2007), Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data, Canadian Journal of Remote Sensing, 33(5):406415.Google Scholar
Zhang, Y., Cong, Q., Xie, Y.F., Yang, J.X. and Zhao, B. (2008), Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 71(4):14081413.Google Scholar
Zhang, Y., Hayashi, T., Hosokawa, M., Yazawa, S. and Li, Y. (2009), Metallic lustre and the optical mechanism generated from the leaf surface of Begonia rex Putz., Scientia Horticulturae, 121(2):213217.Google Scholar
Zhang, Y., Huang, J., Wang, F., Blackburn, G.A., Zhang, H.K., Wang, X., et al. (2017), An extended PROSPECT: advance in the leaf optical properties model separating total chlorophylls into chlorophyll a and b, Scientific Reports, 7:6429.Google Scholar
Zhao, F., Guo, Y., Huang, Y., Reddy, K.N., Lee, M.A., Fletcher, R.S. et al. (2014a), Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion, International Journal of Applied Earth Observation and Geoinformation, 31:7885.Google Scholar
Zhao, F., Huang, Y., Guo, Y., Reddy, K.N., Lee, M.A., Fletcher, R.S., et al. (2014b), Early detection of crop injury from glyphosate on soybean and cotton using plant leaf hyperspectral data, Remote Sensing, 6(2):15381563.Google Scholar
Zhao, F., Guo, Y., Huang, Y., Verhoef, W., van der Tol, C., Dai, B., et al. (2015), Quantitative estimation of fluorescence parameters for crop leaves with Bayesian inversion, Remote Sensing, 7(10):1417914199.Google Scholar
Zhao, J., Yuan, L., Luo, J., Du, S., Huang, L. and Huang, W. (2012), Spectral differences of opposite sides of stripe rust infested winter wheat leaves using ASD’s Leaf Clip, in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS’12), Munich, Germany, 22–27 July 2012, IEEE, pp. 65856588.Google Scholar
Zhao, J., Huang, L., Huang, W., Zhang, D., Yuan, L., Zhang, J. et al. (2014c), Hyperspectral measurements of severity of stripe rust on individual wheat leaves, European Journal of Plant Pathology, 139(2):407417.Google Scholar
Zhao, K., Valle, D., Popescu, S., Zhang, X. and Mallick, B. (2013), Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection, Remote Sensing of Environment, 132:102119.Google Scholar
Zhou, N., Dong, W. and Mei, X. (2006), Realistic simulation of seasonal variant maples, in Proc. 2nd International Symposium on Plant Growth Modeling and Applications, Beijing, China, 13–17 November 2006, pp. 295301.Google Scholar
Zhou, X., Huang, W., Kong, W., Ye, H., Dong, Y. and Casa, R. (2017), Assessment of leaf carotenoids content with a new carotenoid index: development and validation on experimental and model data, International Journal of Applied Earth Observation and Geoinformation, 57:2435.Google Scholar
Zhu, J., Tremblay, N. and Liang, Y. (2012), Comparing SPAD and atLEAF values for chlorophyll assessment in crop species, Canadian Journal of Soil Science, 92(4):645648.Google Scholar
Zhu, Y., Qu, Y., Liu, S. and Chen, S. (2014), A reflectance spectra model for copper-stressed leaves: advances in the PROSPECT model through addition of the specific absorption coefficients of the copper ion, International Journal of Remote Sensing, 35(4):13561373.Google Scholar
Zhumar, A.Y. (1998), Effect of the structure of mesophyll in a leaf on the form of the spectral curve for the first derivatives of the reflection coefficients of leaves over the range 0.68–0.75 μm, Journal of Applied Spectroscopy, 65(6):967972 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 65(6):921–925).Google Scholar
Zhumar, A.Y. and Zaitseva, V.A. (2003a), Influence of the concentration of aqueous solutions of sulfuric acid on the reflection spectra of pine needles, Journal of Applied Spectroscopy, 70(2):292297 (cover-to-cover translation from Zhurnal Prikladnoi Spektroskopii, 70(2):260264).Google Scholar
Zhumar, A.Y. and Zaitseva, V.A. (2003b), Influence of sulfuric acid solutions on the pine needles optical characteristics, in Proc. International Geoscience and Remote Sensing Symposium (IGARSS’03), Toulouse, France, 21–25 July 2003, IEEE, Vol. 4, pp. 28912893.Google Scholar
Zhumar, A.Y. and Tsaryuk, A.V. (2005), Ellipticity of the linearly polarized He-Ne laser radiation reflected by the leaves of plants, Journal of Applied Spectroscopy, 72 (2):249254 (translated from Zhurnal Prikladnoi Spektroskopii, 72(2):236240).Google Scholar
Ziechmann, W. (1964), Spectroscopic investigations of lignin, humic substances and peat, Geochimica et Cosmochimica Acta, 28(10–11):15551566.Google Scholar
Zolotarev, V.M. and Demin, A.V. (1977), Optical constants of water over a broad range of wavelengths, 0.1 Å-1 m, Optics and Spectroscopy, 43(2):157161.Google Scholar
Zuo, J., Zhang, Z.W., He, J., Zhang, L.L., Mu, K.J. and Zhang, C. (2011), The experimental research of leaf water content using terahertz time-domain spectroscopy, in Proc. International Symposium on Photoelectronic Detection and Imaging 2011: Terahertz Wave Technologies and Applications (Zhang X.C., Yao J., Zhang C. and Wang Z., Eds), Beijing, China, 24 May 2011, SPIE, Vol. 8195, 81951F.Google Scholar
Zuppiroli, L. and Bussac, M.N. (2001), Traité des couleurs, Presses polytechniques et universitaires romandes, Lausanne, Switzerland, 442 pages.Google Scholar
Zurzycki, J. (1961), The influence of chloroplast displacements on the optical properties of leaves, Acta Societatis Botanicorum Poloniae, (3–4):503527.Google Scholar
Zwiggelaar, R. (1998), A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops, Crop Protection, 17(3):189206.Google Scholar
Zygielbaum, A.I., Gitelson, A.A., Arkebauer, T.J. and Rundquist, D.C. (2009), Non-destructive detection of water stress and estimation of relative water content in maize, Geophysical Research Letters, 36:L12403.Google Scholar
Zygielbaum, .I., Arkebauer, T.J., Walter-Shea, E.A. and Scoby, D.L. (2012), Detection and measurement of vegetation photoprotection stress response using PAR reflectance, Israel Journal of Plant Sciences, 60(1–2):3747Google Scholar

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