Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-7479d7b7d-k7p5g Total loading time: 0 Render date: 2024-07-12T01:34:39.188Z Has data issue: false hasContentIssue false

6 - Satellite Sensors and Platforms

from Section Two - Remote Sensing

Published online by Cambridge University Press:  05 June 2016

Derek Eamus ,
Alfredo Huete  and
Qiang Yu
Derek Eamus
Affiliation:
University of Technology, Sydney
Alfredo Huete
Affiliation:
University of Technology, Sydney
Qiang Yu
Affiliation:
University of Technology, Sydney
Get access

Summary

Introduction

There are numerous Earth orbiting satellite sensors that provide observations useful in assessing land cover conditions and landscape dynamics. These orbiting sensors measure spatial patterns of reflected and emitted energy from the land surface that can be used to generate geospatial image products of soil, vegetation, water and biogeochemical features. They measure changes over time through their repeat observations across a range of spatial and temporal scales. Satellite imagery extending back to the 1970s now provides a forty-plus year observation data record of dynamic ecosystem conditions and land surface changes. The synoptic coverage, higher-quality and consistency of satellite imagery have greatly improved the mapping of Earth resources compared with aerial photography.

A basic understanding of the variety of sensor designs and their characteristics is beneficial in correctly applying remote sensing tools to achieve various science and resource management objectives (Fig. 6.1). It is also important to know which sensors yield the appropriate type of remote sensing data to best answer specific ecological questions. Various sensor-dependent properties, such as pixel size, spectral bands, temporal repeat period, radiometric fidelity, polarization and viewing geometry are utilized to measure and characterize the Earth's surface. Each sensor system will have unique measurement strengths and limitations in its ability to characterise and retrieve land cover information.

There are many ways to classify the multitude of orbiting sensors and imagery available for ecosystem landscape studies. Important differentiating criteria may include the region of the electromagnetic spectrum (e.g., microwave, thermal, visible, near-infrared) being sensed, whether the energy source is active versus passive, sensor orbital characteristics (e.g., geostationary), frequency of image acquisition and sensor spatial resolution. In this chapter we introduce basic sensor principles, their design and properties, and discuss their respective capabilities and limitations in assessing land cover status, ecological variables, and landscape processes. Examples of the various types of sensor systems used in landscape studies are also highlighted.

Sensor Resolution

The resolution properties of a sensor define the magnitude and extent to which a sensor is able to discriminate variations and changes in landscape properties. In general, improved surface characterisations are achieved with finer resolution imaging capabilities. However, all sensors are limited by resolution constraints and signal noise limitations.

Type
Chapter
Information
Vegetation Dynamics
A Synthesis of Plant Ecophysiology, Remote Sensing and Modelling
 , pp. 184 - 205
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anyamba, A and Tucker, CJ, (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments 63, 596–614.Google Scholar
Asner, GP, (2009). Tropical forest carbon assessment: integrating satellite and airborne mapping approaches. Environmental Research Letters 4, 4003–4009.Google Scholar
Asner, GP, Powell, GVN, Mascaro, J, Knapp, DE, Clark, JK, Jacobson, J, Kennedy-Bowdoin, T, Balaji, A, Paez-Acosta, G, Victoria, E, Secada, L, Valqui, M and Hughes, RF, (2010). High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences 107, 16738–42.Google Scholar
Cihlar, J, Ly, H, Li, Z, Chen, J, Pokrant, H and Huang, F, (1997). Multi-temporal, multichannel AVHRR data sets for land biosphere studies—Artifacts and corrections. Remote Sensing of Environment 60, 35–57.Google Scholar
Diner, DJ, Asner, GP, Davies, R, Knyazikhin, Y, Muller, JP, Noli, AW and Pinty, B B, (1999). New directions in earth observing: Scientific applications of multiangle remote sensing. Bulletin of the American Meteorological Society 80 (11), 2209–2228.Google Scholar
Eamus, D, Zolfaghar, S, Villalobos-Vega, R, Cleverly, J and Huete, AR (2015). Groundwater- dependent ecosystems: a review of new techniques and an ecosystem-scale threshold response to differences in depth-to-groundwater. Hydrological and Earth System Science 19, 1–77.Google Scholar
Fensholt, R, Sandholt, I, Stisen, S and Tucker, C, (2006). Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor. Remote Sensing of Environment 101, 212–29.Google Scholar
Ferrazzoli, P and Guerriero, L, (1996). Passive microwave remote sensing of forests: a model investigation. Geoscience and Remote Sensing, IEEE Transactions 34, 433–43.Google Scholar
Frankenberg, C, O'Dell, C, Berry, J, Guanter, L, Joiner, J, Köhler, P, Pollock, R and Taylor, TE, (2014). Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing of Environment 147, 1–12.Google Scholar
Gao, F, Masek, J, Schwaller, M and Hall, F, (2006). On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. Geoscience and Remote Sensing IEEE Transactions 44, 2207–18.Google Scholar
Gobron, N, Pinty, B, Verstraete, NM and Widlowski, JL, (2000). Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications. Geoscience and Remote Sensing IEEE Transactions 38, 2489–505.Google Scholar
Guanter, L, Zhang, Y, Jung, M, Joiner, J, Voigt, M, Berry, JA, Frankenberg, C, Huete, AR, Zarco-Tejada, P, Lee, J-E, Moran, MS, Ponce-Campos, G, Beer, C, Camps-Valls, G, Buchmann, N, Gianelle, D, Klumpp, K, Cescatti, A, Baker, JM and Griffis, TJ, (2014). Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence.Proceedings of the National Academy of Sciences 111, E1327–E1333.Google Scholar
Hermosilla, T, Wulder, WA, White, JC, Coops, NC and Hobart, GW, (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220–324.Google Scholar
Hopkins, PF, Maclean, AL and Lillesand, TM, (1988). Assessment of Thematic Mapper imagery for forestry applications under Lake States conditions. Photogrammetric Engineering and Remote Sensing 54, 61–8.Google Scholar
IPCC 2007, Climate change (2007)-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC, Solomon, S, D Qin, M Manning, Z Chen, M Marquis, KB. Averyt, M Tignor and HL Miller (eds.), vol. 4, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Irons, JR, Dwyer, JL and Barsi, JA, (2012). The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment 122, 11–21.Google Scholar
Justice, CO, Vermote, E, Townshend, JRG, DeFries, R, Roy, DP, Hall, DK, Salomonson, VV, Privette, JL, Riggs, G, Strahler, A, Lucht, W, Myneni, RB, Knyazikhin, Y, Running, SW, Nemani, RR, Zhengming, W, Huete, AR, Leeuwen, W Van, Wolfe, RE, Giglio, L, Muller, JP, Lewis, P and Barnsley, MJ, (1998). The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. Geoscience and Remote Sensing, IEEE Transactions 36, 1228–49.Google Scholar
Kawanishi, T, Sezai, T, Ito, Y, Imaoka, K, Takeshima, T, Ishido, Y, Shibata, A, Miura, M, Inahata, H and Spencer, RW, (2003). ‘The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies’. Geoscience and Remote Sensing, IEEE Transactions 41, 184–94.Google Scholar
Leblanc, MJ, Tregoning, P, Ramillien, G, Tweed, SO and Fakes, A, (2009). Basin-scale, integrated observations of the early 21st century multiyear drought in southeast Australia. Water Resources Research 45, W04408.Google Scholar
Lefsky, MA, Cohen, WB, Parker, GG and Harding, DJ, (2002). Lidar remote sensing for ecosystem studies. BioScience 52, p. 19.Google Scholar
Li, R, Min, Q and Lin, B, (2009). Estimation of evapotranspiration in a mid-latitude forest using the Microwave Emissivity Difference Vegetation Index (EDVI). Remote Sensing of Environment 113, 2011–2018.Google Scholar
Ma, X, Huete, AR, Yu, Q, Coupe, N Restrepo, Davies, K, Broich, M, Ratana, P, Beringer, J, Hutley, LB, Cleverly, J, Boulain, N, Eamus, D, (2013). Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect. Remote Sensing of Environment 139, 97–115.Google Scholar
Meroni, M, Rossini, M, Guanter, L, Alonso, L, Rascher, U, Colombo, R and Moreno, J, (2009). Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sensing of Environment 113, 2037–2051.Google Scholar
Min, Q and Lin, B, (2006). Remote sensing of evapotranspiration and carbon uptake at Harvard Forest. Remote Sensing of Environment 100, 379–87.Google Scholar
Miura, T, Turner, JP and & Huete, AR (2013). Spectral Compatibility of the NDVI Across VIIRS, MODIS, and AVHRR: An Analysis of Atmospheric Effects Using EO-1 Hyperion. Geoscience and Remote Sensing, IEEE Transactions 51, 1349–1359.Google Scholar
O'Reilly, JE, Maritorena, S, Mitchell, BG, Siegel, DA, Carder, KL, Garver, SA, Kahru, M, McClain, C, (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research 103, 24937–24953.Google Scholar
Pampaloni, P and Paloscia, S, (1986). Microwave emission and plant water content: A Comparison between field measurements and theory. Geoscience and Remote Sensing, IEEE Transactions GE-24, 900–905.Google Scholar
Rodell, M, Velicogna, I and Famiglietti, JS JS, (2009). Satellite-based estimates of groundwater depletion in India. Nature 460, 999–1002.Google Scholar
Roy, DP, Ju, J, Lewis, P, Schaaf, C, Gao, F, Hansen, M and Lindquist, E, (2008). Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment 112, 3112–3130.Google Scholar
Salomonson, VV, Barnes, W, Maymon, PW, Montgomery, HE and Ostrow, H H, (1989). MODIS: advanced facility instrument for studies of the Earth as a system. Geoscience and Remote Sensing, IEEE Transactions 27, 145–53.Google Scholar
Tapley, BD, Bettadpur, S, Watkins, M and Reigber, C, (2004). The gravity recovery and climate experiment: Mission overview and early results. Geophysical Research Letters 31, L09607.Google Scholar
Townshend, J and Justice, C C, (1981). Information extraction from remotely sensed data. International Journal of Remote Sensing 2, 313–29.Google Scholar
Tucker, CJ, Grant, DM and Dykstra, JD, (2004). NASA's global orthorectified Landsat data set. Photogrammetric Engineering and Remote Sensing 70, 313–22.Google Scholar
Ungar, SG, Pearlman, JS, Mendenhall, JA and Reute, D, (2003). Overview of the Earth Observing One (EO-1) mission. Geoscience and Remote Sensing IEEE Transactions 41, 1149–59.Google Scholar
Veefkind, JP, Aben, I, McMullan, K, Förster, H, Vries, J de, Otter, G, Claas, J, Eskes, HJ, Haan, JF de, Kleipool, Q, Weele, M van, Hasekamp, O, Hoogeveen, R, Landgraf, J, Snel, R, Tol, P, Ingmann, P, Voors, R, Kruizinga, B, Vink, R, Visser, H and Levelt, PF, (2012). ‘TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone lay applications’. Remote Sensing of Environment 120, 70–83.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×