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Identification of High-Variation Fields based on Open Satellite Imagery

Published online by Cambridge University Press:  01 June 2017

J. H. Jeppesen ,
R. H. Jacobsen ,
R. N. Jørgensen ,
A. Halberg  and
T. S. Toftegaard
J. H. Jeppesen*
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
R. H. Jacobsen
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
R. N. Jørgensen
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
A. Halberg
Affiliation:
Airinov Denmark, Vejstrupgaards Alle 5882 Vejstrup, Denmark
T. S. Toftegaard
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
*
†E-mail: [email protected]
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Abstract

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

Keywords

Remote sensingvegetation indexzonal statisticsopen datadecision support
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. 388 - 393
Copyright
© The Animal Consortium 2017 

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