An operational Sentinel-2 based monitoring system for the management and control of direct aids to the farmers in the context of the Common Agricultural Policy (CAP): A case study in mainland Portugal

• Published in: International journal of applied earth observation and geoinformation 2021-12, Vol.103, p.102469, Article 102469
• Main Authors: Navarro, Ana, Silva, Inês, Catalão, João, Falcão, João
• Format: Article
• Language: English
• Subjects: Automated monitoring system; Common Agricultural Policy (CAP); Knowledge transfer; Machine learning (ML); On-the-spot checks (OTSC); Sentinel-2
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2021.102469

The European Union (EU) member states are expected to develop new procedures, based on automatic earth observation data analysis, for the management and control of direct aids to the farmers, as part of the Common Agricultural Policy (CAP) reform of 2020. Here, we propose an operational monitoring system based on Sentinel-2 surface reflectance (SR) data and machine learning (ML) algorithms, consisting of a hierarchical approach triggering 3 color-coded warning alerts to distinguish among compliant (green), non-compliant (red), and inconclusive (yellow) parcels comparatively to the farmer’s declaration. A Random Forest (RF) model is applied to a 5-day interpolated SR time series to generate a preliminary crop map, where all the parcels whose predicted and declared crop type match are flagged as compliant. Next, a refinement procedure is adopted to improve the discrimination between temporary and permanent crops. At this stage, VI temporal metrics and texture are used as input to a Support Vector Machine (SVM) classifier trained using only the previous compliant parcels. Through a set of decision rules, SVM crop class predictions are flagged as compliant, non-compliant and inconclusive. The system was tested for a significant area in mainland Portugal, using the 2019 Land Parcel Information System (LPIS) data. The system returned 96.5%, 2.5% and 1% of the parcels as compliant, inconclusive, and non-compliant, respectively. Comparison with field inspections for the subsidy control of 2019 revealed that only 1.1% of the correct declarations were classified by the system as non-compliant (5% as admissible value), while less than 5% of the real non-compliant declarations passed through the system (10–20%).