With the ever-increasing number of satellites and the availability of data free of charge, the
integration of multi-sensor images in coherent time series offers new opportunities for land
cover and crop type classification. This article investigates the potential of structural
biophysical variables as common parameters to consistently combine multi-sensor time
series and to exploit them for land/crop cover classification. Artificial neural networks were
trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR …