Hyperspectral remote sensing data have special advantages, i.e., they have high spectral resolution and strong band continuity, and a great number of spectral information could be widely used in soil properties monitoring research. Using hyperspectral remote sensing technique to analyze saline soil properties makes great significance for the crop growth in the irrigation district and agricultural sustainable development. 221 soil samples were collected from Manasi River Basin to measure soil electrical conductivity (EC), soil organic matter (SOM) and 3 kinds of cation concentrations including Na+, Ca2+ and Mg2+, which were used to obtain sodium adsorption ration value (SAR). The soil hyperspectral curves were also measured. EC, SOM and SAR models were established based on the six spectral-related indices, including raw reflectance (R), standard normal variable (SNV), normalized difference vegetation index (NDVI), logarithm of the reciprocal (LR), the first derivative reflectance (FDR) and continuum-removal reflectance (CR) by the stepwise linear regression method. The results showed that, compared to the other five models, the model of log (EC)~R had the highest accuracy with r value of 0.782 and RMSE value of 0.256. The model of SOM vs. NDVI had the highest accuracy with r value of 0.670 and RMSE value of 5.352. The model of SAR vs. FDR had the highest accuracy with r value of 0.647 and RMSE value of 1.932. As to the model accuracy of the studied soil physico-chemical properties, the log(Ec) model was the most effective one, followed by the SOM model, the SAR model was the most inaccurate. The sensitive wavelengths for EC, SOM and SAR distributed in 395~1 801 nm, 352~1 144 nm and 394~1 011 nm, respectively. Since soil physico-chemical properties were highly spatially variable, there were large differences for the model establishment and validation of the soil properties. This research could be a reference of hyperspectral remote sensing monitoring of salinized soils.