An empirical model was developed to infer soil moisture and surface roughness from radar data. The model was validated against in situ measurements and tested with data from the University of Michigan's LCX POLARSCAT and the University of Bern's RASAM systems. The model uses co-polarized backscattering coefficients and is based on equations that relate backscattering to incidence angle, dielectric constant, surface roughness, and wavelength. The model's validity is restricted to certain conditions to ensure accuracy. The study examined the effects of vegetation on radar inversion, noting that co-polarized channels are more reliable for inversion due to better calibration and signal-to-noise ratio. Vegetation can affect cross-polarized channels, so areas with high vegetation are masked out to improve accuracy. The L-band ratio of cross-polarized to co-polarized backscattering was found to correlate with the Normalized Difference Vegetation Index (NDVI), allowing for the selection of areas with low vegetation cover for inversion. The inversion technique was applied to SAR data from the AIRSAR and SIR-C sensors. Results showed that the algorithm can estimate soil moisture with an accuracy better than 4% in areas with low vegetation and under specific conditions. The RMS error in soil moisture estimates was found to be 3.5% across various areas. The study concluded that the algorithm can reliably estimate soil moisture using co-polarized radar data, and that current SAR systems like AIRSAR and SIR-C meet the required calibration standards. The ratio of cross-polarized to co-polarized backscattering was shown to be positively correlated with NDVI, indicating its usefulness in determining suitable areas for inversion.An empirical model was developed to infer soil moisture and surface roughness from radar data. The model was validated against in situ measurements and tested with data from the University of Michigan's LCX POLARSCAT and the University of Bern's RASAM systems. The model uses co-polarized backscattering coefficients and is based on equations that relate backscattering to incidence angle, dielectric constant, surface roughness, and wavelength. The model's validity is restricted to certain conditions to ensure accuracy. The study examined the effects of vegetation on radar inversion, noting that co-polarized channels are more reliable for inversion due to better calibration and signal-to-noise ratio. Vegetation can affect cross-polarized channels, so areas with high vegetation are masked out to improve accuracy. The L-band ratio of cross-polarized to co-polarized backscattering was found to correlate with the Normalized Difference Vegetation Index (NDVI), allowing for the selection of areas with low vegetation cover for inversion. The inversion technique was applied to SAR data from the AIRSAR and SIR-C sensors. Results showed that the algorithm can estimate soil moisture with an accuracy better than 4% in areas with low vegetation and under specific conditions. The RMS error in soil moisture estimates was found to be 3.5% across various areas. The study concluded that the algorithm can reliably estimate soil moisture using co-polarized radar data, and that current SAR systems like AIRSAR and SIR-C meet the required calibration standards. The ratio of cross-polarized to co-polarized backscattering was shown to be positively correlated with NDVI, indicating its usefulness in determining suitable areas for inversion.