During recent years

theoretical modeling and field experiments have established the fundamentals of active microwave remote sensing as an important tool in determining physical properties of soil. But

its application to hydrologi-cal and agricultural sciences has been hampered by natural variability and the complexity of the vegetation canopy and surface roughness that significantly affect the sensitivity of radar backscattering to soil moisture. Vegetation cover will cause an under-estimation of soil moisture and an over-estimation of surface roughness when we apply the algorithm for bare surface soil moisture estimation to vegetation covered regions.A polarimetric SAR backscatter measurements

by using eigenvalues and eigenvectors of the covariance matrix

can be decomposed into three components based on the scattering types:an odd number of reflections

an even number reflections

and a cross-polarized scattering power. This decomposition technique allows us to obtain the estimation of single and double reflection components of backscattering coefficients for VV and HH polarization.In this study

we evaluate the usage of the decomposition theory in application of estimating soil moisture for vegetated surface with the temporal fully polarimetric L-band SAR measurements. Using the decomposed scattering measurements from JPL/AIRSAR image data

we evaluated their usage to reduce the vegetation effect on estimation soil moisture under configurations of a single-frequency （L-band）and multi-pass with a same incidence. The results indicate the decomposition technique can be used to estimate the soil moisture change and their magnitudes for vegetated surface

it provide a powerful tool for monitoring surface soil moisture

especially with the moderate vegetated surface.