The exploitation of the unprecedented capacity of Sentinel-1 (S1) and Sentinel-2 (S2) data offers new opportunities for crop mapping. In the framework of the SenSAgri project, this work studies the synergy of very high-resolution Sentinel time series to produce accurate early seasonal binary cropland mask and crop type map products.

This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified.