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International audience; Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and-2 acquisitions, this paper investigates the automation of land parcel identification system (LPIS) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and-2 time series. The temporal evolution of crop types was modeled with a linearchain conditional random field, trained with time series of multi-modal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km 2) which show different crop types, various parcel sizes, and agricultural practices: .the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark positive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops. Crop Rotation Integration Crop rotation knowledge can be used to improve agricultural yields (Berzsenyi, Györffy, and Lap et al. 2000) and soil quality (Karlen, Hurley, Andrews et al. 2006). Crop type prediction can also be improved using prior knowledge on crop rotations per parcel since a crop type is strongly correlated to past crop types. Modeling such temporal structures from

International audience; 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.