Identification of High-Variation Fields based on Open Satellite Imagery

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.