This work has been focused on the integration of microwave data coming from different sensors (SMAP, Sentinel-1, AMSR2), in order to obtain an improved estimation of hydrological parameters and in particular of the Soil Moisture (SMC). The failure of radar sensor in SMAP satellite induced to look for other available microwave frequencies, both from active (e.g. Sentinel-1, C band) and passive sensors (e.g. AMSR2, from C to Ka bands).
The contribution of higher frequencies, which are more suitable for monitoring vegetation and other surface features, has been evaluated for compensating the vegetation and roughness effects on the SM retrieval accuracy. In particular, the contribution of C band SAR from Sentinel-1 and of the multi-frequency AMSR2 radiometer (C to Ka bands) have been considered. Moreover, a disaggregation technique, based on the Smoothing filter based intensity modulation (SFIM), enabled achieving a more suitable ground resolution for comparing SAR and radiometric data.
Disaggregated microwave data were used as inputs of a retrieval algorithm based on Artificial Neural Network (ANN), able to exploit the synergy between active and passive acquisitions. The algorithm was defined basing on the data available from the SMEX02 and SMAPVEX12 campaigns, and on data simulated by electromagnetic forward models. The algorithm validation on these datasets returned encouraging results, while the disaggregation technique enabled adapting the algorithm to work with Sentinel, SMAP and AMSR2 synergic acquisitions