Abstract: Abstract: In order to explore the intrinsic relationship between soil moisture and hyperspectral vegetation indices, and achieve fast and accurate monitoring of soil moisture, 26 vegetation indices were chosen and figured out based on the hyperspectral data measured by ASD spectrometer and the HSI image data in a typical study area. These hyperspectral vegetation indices and soil moisture data measured in laboratory, which were collected from the northeast of delta oasis of Weigan and Kuqa rivers, were analyzed with the gray relative analysis method, and there were 5 indices which had higher correlation with soil moisture among 26 vegetation indices. These indices were selected; using the multiple linear regression, the soil moisture inversion models based on the measured spectral index and the image spectral index were established respectively, and then the inversion model based on the HSI hyperspectral image vegetation indices was corrected with that based on the measured indices. The purpose of this paper was to determine the optimal model through comparing the precision of the model and the correction between the 2 models, and thus could solve the difficulty that hyperspectral vegetation index estimated soil moisture in the oasis of arid area, and improve the precision of estimation. The results showed that: The fittings of 2 kinds of soil moisture inversion models were satisfied, and the 2 models' determination coefficients (R2) were both higher than 0.589 and the models had better stability; the estimation accuracies of 2 kinds of soil moisture inversion models in the soil depth of 0-10 cm were the best. The accuracy of soil moisture inversion model based on the measured hyperspectral vegetation indices was higher than that based on the HSI image vegetation indices. In the soil moisture inversion model based on the measured hyperspectral vegetation indices, the best combination of vegetation indices included mSR705, SAVI2, mNDVI705, SARVI and VOG3, and the value of R2 was 0.668, and through the 0.001 significance level inspection, the R2 value of inspection sample was 0.730, the root mean square error (RMSE) was 0.0021. In the soil moisture inversion model based on the HSI hyperspectral image, the best combination of vegetation indices included ARVI, RVI, MSR, NDVI and OSAVI, and the coefficient of determination was 0.589, and through the 0.001 significance level inspection, the R2 value of inspection sample was 0.610, the RMSE was 0.0020. After being corrected, the accuracy of HSI image soil moisture inversion model was greatly improved, and the coefficient of determination was raised from 0.589 to 0.711 and the RMSE was decreased from 0.0020 to 0.0014. The corrected HSI image soil moisture inversion model can improve the accuracy under the regional scale. This paper studies the scale transformation of the soil moisture content high spectral inversion model from the point to the continuous surface of dispersed point and then to the surface of the soil. Through application of this method, it will be feasible to carry out remote sensing monitoring of soil moisture content, and provide reference for further improvement of the accuracy of quantitative remote sensing monitoring of soil moisture content under the regional scale.