Abstract: As a global problem, soil salinization poses a serious threat to the limited soil resources and ecosystem health in arid and semi-arid areas, and is one of the most important causes of land desertification and land degradation. Soil salinity is an effective evaluation index of soil salinization, and there is temporal and spatial difference. Dynamic monitoring can fully understand the status of soil salinization and effectively provide more quantitative information for soil restoration and land reclamation. Compared with traditional laboratory analysis, satellite remote sensing technology has major advantages in observing the ground at large spatial scales and high temporal resolution. As a new generation of spaceborne multi-spectral instrument (MSI), Sentinel-2A has novel spectral functions (namely, three red-edge bands and two near-infrared bands), which provides a broad prospect for quantitative evaluation of soil properties. At present, only a few studies were associated with red edge spectral index, vegetation index and topographic index in soil salinization mapping, and it has become a great challenge to choose the best modeling technology in soil mapping for a specific landscape area, although many algorithm have been successfully applied in the prediction of soil properties. Therefore, in this study, we used Sentinel-2A red-edge bands, vegetation indexes and digital elevation model (DEM) derived variables to conduct soil salt analysis based on machine learning methods in the Ebinur Lake wetland in the northwestern Xinjiang of China. 24 red edge spectral indices, 11 vegetation indices and 8 topographic indices were selected to participate in the modeling by the XGBoost algorithm, and the Random Forest (RF), Extreme Learning Machine (ELM) and Partial Least Squares Regression (PLSR) three machine learning models based on 78 sampling sites were applied to extract soil Electrical Conductivity (EC). The coefficient of determination (R2), root mean square error (RMSE) and ratio of performance to deviation (RPD) were used to evaluate the prediction accuracy of the above models. The results showed that the optimal red edge spectral index combined with RF could basically predict EC. The verification set R2, RMSE, and RPD were 0.63, 7.14 dS/m, and 2.09, respectively. The prediction accuracy of the combined modeling of the red edge spectral index and the vegetation index is better than that of the combination with the terrain index, and the prediction effect of the RF model was better than that of ELM and PLSR, and its training set (R2=0.83, RMSE=4.84 dS/m), validation set (R2=0.76, RMSE=5.36 dS/m, RPD=2.79). The prediction accuracy of the combined modeling of the red edge spectral index, vegetation index and terrain index combined with RF reached the best. The R2, RMSE and RPD of the verification set were 0.83, 4.81 dS/m and 3.11, respectively. In addition, with the continuous increase of input feature variables, the prediction effect of each model were improved to varying degrees. Soil salinization mapping based on the optimal variable combination (red edge spectral index + terrain index + vegetation index) and the best prediction model (RF), showed that the degree of soil salinization in the central and eastern regions was particularly serious in the study area.