Vegetation and soil are usually both in one pixel and soil moisture content monitoring is inevitably influenced by vegetation spectrum

so it is important to eliminate the interference of vegetation spectrum.Hyperion hyperspectral data were decomposed by decomposition algorithm based on the spectrum matching to eliminate vegetation spectrum

and the first order differential and continuum removal transformation were used to dispose soil spectrum information. Then the sensitive bands were selected to establish the inversion model of soil moisture content.Results show that the best model was established by the bands X661

X1019 and X2067 of the soil continuum-removal spectrum

and the forecasted Rz value was 0.85.When the vegetation spectrum is not eliminated

the best model was established by the bands X541

X979 and X1632 of the soil first order differential spectrum

and the forecasted Rz value was only 0.36.The method of forecasting soil water content by decomposing hyperspectral data to eliminate the vegetation spectrum is feasible and it can provide reference for the research on soil water content forecast by remote sensing.