This paper reports an attempt in improving surface soil moisture radar algorithm for Hydrosphere State Mission (Hydros). We used a Radiative Transfer Model to simulate a wide range surface dielectric, roughness, vegetation with random orientated disks database for our algorithm development under HYDROS radar sensor (L-band multi-polarizations and 40º incidence) configuration. Through analyses of the model simulated database, we developed a technique to estimate surface soil moisture. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12, 0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations. Test of this algorithm using all simulated data showed that an accuracy for the volumetric soil moisture estimation in terms of Root Mean Square Error (RMSE) of 4.6 % could be achievable.