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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
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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 

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