Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-28T20:34:47.646Z Has data issue: false hasContentIssue false
  • English
  • Français

Monitoring crop growth and key agronomic parameters through multitemporal observations and time series analysis from remote sensing big data

Published online by Cambridge University Press:  01 June 2017

K. Karantzalos ,
A. Karmas  and
A. Tzotsos
K. Karantzalos*
Affiliation:
Remote Sensing Laboratory, National Technical University of Athens, Athens, Greece
A. Karmas
Affiliation:
EOfarm P.C., Athens, Greece
A. Tzotsos
Affiliation:
EOfarm P.C., Athens, Greece
*
†E-mail: karank@central.ntua.gr
Get access

Abstract

In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state.

Keywords

Remote SensingMultispectral dataWebGISDecision makingCrop management
Type
Satellite Applications
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 394 - 399
Copyright
© The Animal Consortium 2017 

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

Baumann, P 2009. Array databases and raster data management. In: T. Ozsu, L. Liu (eds.), Encyclopedia of Database Systems. Springer.Google Scholar
Bégué, A, Todoroff, P and Pater, J 2008. Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series. Precision Agriculture 9 (3), 161–171.CrossRefGoogle Scholar
Janssen, SJC, Porter, CH, Moore, AD, Athanasiadis, IN, Foster, I, Jones, JW and Antle, JM 2016. Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural Systems, Available online 10 November 2016.Google Scholar
Karantzalos, K, Karmas, A and Tzotsos, A 2015. RemoteAgri: Processing Online Big Earth Observation Data for Precision Agriculture. 10th European Conference on Precision Agriculture, pp. 421–428.CrossRefGoogle Scholar
Karmas, A, Tzotsos, A and Karantzalos, K 2016. Big Geospatial Data for Environmental and Agricultural Applications. Yu and Guo (eds.), Big Data Concepts, Theories and Applications, Springer International Publishing, DOI 10.1007/978-3-319-27763-9.CrossRefGoogle Scholar
Karmas, A and Karantzalos, K 2016. Scalable Geospatial Services for the Production of Time Series and Value-added Maps in Agriculture and Water Quality Monitoring. Proceedings of the 2016 Conference on Big Data from Space (ESA BiDS'16).Google Scholar
Lokers, R, Knapen, R, Janssen, S, Randen, Y and Jansen, J 2016. Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software 84, 494–504.CrossRefGoogle Scholar
Thorp, KR and Tian, LF 2004. A Review on Remote Sensing of Weeds in Agriculture. Precision Agriculture 5 (5), 477–508.CrossRefGoogle Scholar
Wagner, P and Hank, K 2013. Suitability of aerial and satellite data for calculation of site-specific nitrogen fertilisation compared to ground based sensor data. Precision Agriculture 14 (2), 135–150.CrossRefGoogle Scholar