Crop-Rotation Structured Classification using Multi-Source Sentinel Images and LPIS for Crop Type Mapping

Automatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel Identification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km
²
study area located in France and with a 25 categories national nomenclature. The classification results are globally improved.