Abstract
The present work exploits multi-frequency SAR and optical imagery in order to assess soil and vegetation water content values in agricultural areas in Italy and Argentina. Sentinel-1, Sentinel-2, ALOS-2, RADARSAT-2, and COSMO-SkyMed imagery were used in a retrieval algorithm based on Support Vector Machine (SVR) approach. Preliminary results indicate that the integration of optical and SAR data (C and L band) by the mean of machine learning techniques leads to an accurate retrieval of soil moisture (RMSE = 4%). Moreover, a preliminary test for the retrieval of Vegetation Water Content (VWC) indicates that ALOS-2 L-Band backscattering, combined with L-Band simulated backscattering, leads to a reliable model to compute VWC, without any optical information.