This paper proposes an inversion algorithm for extracting soil and vegetation parameters from multi-frequency, multi-polarimetric SAR radar data. The algorithm, based on the determination of probability density functions (pdfs) through a Bayesian methodology, has been initially developed for bare soils and tested on numerous data sets. The pdfs are obtained from the comparison between theoretical backscattering values, derived from the Integral Equation Model (IEM), and the measured ones. The aim of this work is to apply this inversion algorithm to fields that have different levels of vegetation cover. As first approach, the pdfs have been empirically calibrated according to vegetation water content values obtained from a multispectral image (Landsat image).
This allows subtracting the contribution of vegetation backscatter from radar signal and hence isolating the contribution from bare soil. In a second approach, instead of using the IEM, a simple vegetation model, the water-cloud model, has been used. This model relates the radar responses to both soil and vegetation characteristics.
Thus, the comparison between the theoretical and the measured sensor responses leads to the calculation of new pdfs which contain information about both vegetation and soil parameters. The algorithm has been tested on data sets acquired during the SMEX'02 experiment covering a variety of soil moisture and vegetation conditions. The algorithm exploits the use of L and C band at different polarisations. The results have been also compared with those obtained from the algorithm developed only for bare soils.