【Objective】Soil salinization, being one of the main causes of land degradation in arid and semi-arid regions, poses a great threat to sustainable development of the local social economy and ecological system. 【Method】How to extract the information of spatial distribution of soil salinization is the foundation for management of soil salinization. Therefore, the Weigan-Kuqa Oasis, an area rather fragile in ecology, was selected as an object in this study, using the GF-1 satellite multispectral images of the date of July 19, 2014 as the main data source. A total of 16 spectral indices i.e. Normalized difference vegetation index(NDVI), soil adjusted vegetation index (SAVI), normalized differential salinity index (NDSI), salinity index (SI-T), brightness index (BI), salinity index (SI), salinity index 1 (SI1), salinity index 2 (SI2), salinity index 3 (SI3), salinity index (S1), salinity index (S2), salinity index (S3), salinity index (S5), salinity index (S6), intensity index 1 (Int1), intensity index 2 (Int2), and four bands, i.e. band 1 (B1), band 2 (B2), band 3 (B3) and band 4 (B4), were chosen for analysis. The images in pretreatment were computed by band in line with the spectral index formulas with the aid of software ENVI4.8. Hence, gray scale maps of different spectral indices were derived and pixel values of the 36 sampling points corresponding to the gray scale maps were extracted. The data of electrical conductivities in the surface soil layers (0～10 cm) of those sampling sites during 22～28 July 2014 were also collected for analysis of Pearson correlation with the pixel values using software SPSS 19.0. Thus sensitivities of different spectral indices to the data of soil salinization were figured out. PLSR models were built and validated for relationships of the mathematical formulas for five different electrical conductivities (i.e. measured conductivity, reciprocal of measured conductivity, logarithm of measured conductivity, MSR of measured conductivity and reciprocal of the logarithm of measured conductivity) with spectral indices. Measured conductivities of 24 samples were used for modeling and the remaining 12 samples for validation with the aid of the Unscrambler X10.3 software. 【Result】Results show: 1) the measured surface soil conductivities are closely related to spectral indices, and moderately to SAVI, NDVI, NDSI, SI, SI1, S5, B3 and B4, with all correlations being significant at the 0.01 level; 2) based on the GF-1 satellite images PLSR was used for modeling of surface soil salinization in the Weigan - Kuqa Oasis. The model based on reciprocal of electrical conductivity is better than all the others with R² =0.69 and RMSE=0.58 dS m^-1. For the validation model R² is 0.78 and RMSE 0.53 dS m^-1. In the images of satellite vertical projection salinized and non salinized patches in the land cover can be clearly distiquished from each other, with little confusing or mistaking information. Characteristic texture of salinization relative to degree is distinct, showing a clear layered structure, easy to distinquish and making the visual interpretation of the images more consistent with the actual degree of soil salinization. Consequently the effect of information extraction of soil salinization is quite good and high in precision; 3) this study made full use of the information contained in GF-1 images, thus improving precision of the extraction of soil salinization information from GF-1 images. Non-salinized soil and slightly salinized soil in the oasis accounts for 42.88% and 17.16%, respectively, of total in area. Soil salinization is quite mild in the middle by east and southeast of the oasis, which suggests that the oasis may expand towards that direction in future. Moderately salinized soil, severely salinized soil and salinized soil in the oasis accounts for 29.51%, 8.57% and 1.88%, respectively, of the total in area. The severely salinized soil is distributed closely on the north/west and southwest of the oasis and has already posed a serious threat to healthy development of the oasis economy and calls urgently for management. 【Conclusion】The study on use of GF-1 images to evaluate soil salinization may provide a scientific basis for prediction of salinization in future and assessment of the current situation and for the government in decision-making for management of soil salinization.