COSMO-SKYMED AND RADARSAT IMAGE INTEGRATION FOR SOIL MOISTURE AND VEGETATION BIOMASS MONITORING

This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on two Italian test areas, located in South-Tyrol and in Tuscany, close to Florence, to estimate soil moisture (SMC, in %) and vegetation biomass (PWC, in kg/m(2)). Two retrieval approaches based on Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been applied to these areas. Looking at the preliminary results, it has been noted that the integration of X and C band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and set of samples. On the South Tyrol test area, SVR methods provided an accuracy in the estimate of SMC with determination coefficient, R-2> 0.85 and root mean square error, RMSE.<= 4%. By using X band alone, the result obtained is worse: ANN algorithm resulted in R2 from 0.35 to 0.8 and RMSE from 6 to 2 (% SMC), depending on the polarization combinations considered as input. X band allowed instead retrieving the PWC of cereal fields with a satisfactory accuracy (R-2=0.94 and RMSE=0.35 Kg/m(2)).