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Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study

Published online by Cambridge University Press:  01 June 2017

A. Escolà ,
N. Badia ,
J. Arnó  and
J. A. Martínez-Casasnovas
A. Escolà*
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
N. Badia
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Environmental and Soil Sciences, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
J. Arnó
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
J. A. Martínez-Casasnovas
Affiliation:
Research Group on AgroICT & Precision Agriculture, Department of Environmental and Soil Sciences, University of Lleida - Agrotecnio Center, Lleida, Catalonia, Spain
*
†E-mail: AEscola@eagrof.udl.cat
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Abstract

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models. Yield data were acquired with a yield monitor installed in a combine harvester. Green-based vegetation indices performed slightly better. However, the highest correlation coefficient was 0.48. Better results may be achieved with earlier imagery and other vegetation indices. Sentinel-2 is a promising tool for precision agriculture in large arable crop fields.

Keywords

Remote sensingyieldvegetation indicescrop vigourbarley
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. 377 - 382
Copyright
© The Animal Consortium 2017 

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