A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain)

Abstract

This paper proposes a methodology for deriving an agreement map between the Spanish Land Parcel Information System (LPIS), also known as SIGPAC, and a classification map obtained from multitemporal Sentinel-1 and Sentinel-2 data. The study area comprises the province of València (Spain). The approach exploits predictions and class probabilities obtained from an ensemble method of decision trees (boosting trees). The overall accuracy reaches 91.18% when using only Sentinel-2 data and increases up to 93.96% when Sentinel-1 data are added in the training process. Blending both Setninel-1 and Sentinel-2 data causes a remarkable classification improvement ranging from 3.6 to 8.7 percentage points over shrubs, forest, and pasture with trees, which are the most confusing classes in the optical domain as demonstrated by a spectral separability analysis. The derived agreement map is built upon combining per pixel classifications, their probabilities, and the Spanish LPIS. This map can be exploited into the decision-making chain for subsidies payment to cope with the 2020+ European Common Agricultural Policy (CAP).