Abstract: Soil salinization is a major deserti?cation and land degradation that threatens especially the stability of ecosystems in arid land. Either natural factors or human unreasonable use of the soil can cause soil salinization, and also has impact on the sustainable development of resources and the environment. There is an urgent need for intensive monitoring and quick assessment of salinization through hyperspectral remote sensing as a tool for combating soil salinizaiton in such ecosystems. In this paper, estimation the soil salinization of Ebniur lake basin, Xinjiang, China by a multiple regression model was carried out using Analytical Spectral Devices (ASD) data and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) image data and soil reflectance spectra. A total of 11 spectral transformation forms of reflectance were used to relate with the measured soil salinity. Spectral reflectance experienced root mean square, logarithmic, and reciprocal transformation, then the first and second derivative of original and transformation forms were calculated. In addition, the first derivative of reciprocal of logarithmic was also calculated. A relationship between the sensitive bands of soil salinity was used to build models of soil salinity. The transformation and model establishment were conducted again after resampling ASD spectral reflectance data. Finally, the model after resampling was correlated with that before resampling to increase the model accuracy. A total of 50 sampling points were obtained and 30 of them were randomly selected for model establishment and the other 20 was used for validation. The results showed that the model with second derivative of reflectance was best with R2 of 0.59 and 0.75, and the root means square of error (RMSE) of 1.29 and 1.24 g/kg. ASD resampling improved the model accuracy with the second derivative of logarithmic transformation as the best model, which yielded the R2 of 0.80-0.82, and RMSE of 0.97-1.05 g/kg. Through the regression analysis, a linear model was established between ASD resampling spectral inversion model and ASTER spectral model with R2 of 0.88. Using the linear model and based on the ASTER spectral model and ASD resampling model, the ASTER spectral model was calibrated. Using measured soil salinity to validate the calibrated model, the resulted showed that the calibrated model improved the model accuracy to 0.91 and reduced the RMSE to 0.96 g/kg. Therefore, the multiple regression method could be used for model calibration for ASTER spectral model based on ASD resampling spectral inversion model, which has great potential for estimation of soil salinity in the arid land. This paper made contribution to the dynamic monitoring of soil salinization, realized the scale transformation from the measured field scale to spectral scale of multi-spectral remote sensing, and also can provide valuable information for future research.