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A multi-temporal analysis of AMSR-E data has shown the potential of this sensor in monitoring land surface features by using appropriate combinations of frequency and polarization channels. An algorithm based on a neural network has been implemented, tested with experimental data collected during SMEX02, and used to generate global maps of soil moisture

In this paper an empirical approach for retrieving leaf area index from multifrequency SAR data is presented. From previous results obtained through multifrequency-multipolarization SAR data analysis collected on agricultural fields over the past years, a clear relationship between L-band data and the leaf area index of certain crop types has been observed, mainly at HV polarization. Since other bands, such as C- and P-bands, have been found sensitive to vegetation biomass as well, although to a lesser extent, a multifrequency approach has been tried in order to extend this relation to other crops. In doing so the backscattering coefficent at P-, L- and C-bands has been related to a volumetric leaf area index (i.e., multiplied by the average leaf thickness) normalized to the wave number. The obtained correlation has been used to retrieve leaf area index from multifrequency SAR data.