Soil salinity is an important factor for measuring soil quality, and it is also a basic condition for the growth of crops. Therefore, it is urgent to find a method that can understand soil salt content quickly. This paper is based on the Landsat8 OLI multispectral remote sensing image for the Ebinur Lake Wetland Nature Reserve, and we use the salt content of 36 soil surface samples in the study area as the data source, and choose several multispectral remote sensing indices which have the superior correlation with soil salinity to analyze the soil salinity distribution in the study area. The linear, logarithmic and quadratic function models were constructed with the measured soil salinity, and optimum inversion model of soil salt content was selected. The result shows that: (1)Among these multispectral remote sensing indices, the enhanced vegetation indices show the closest correlation with soil salinity, and the correlation coefficient range is between -0.67 and -0.70. The second is the traditional vegetation indices, and the correlation coefficient range is between -0.46 and -0.58. The correlation of soil salt index is the farthest t,and its range is between 0.16 and -0.45, and there is no correlation between SI3, SI4 and soil salt content. (2)Comparing and analyzing the salt distribution map inverted by measured soil salinity values and the spatial distribution of Enhanced Vegetation indices, we found that the soil salt content around the Ebinur Lake of the northwest and south direction and tne Yan Chi Bridge in the northeast is higher, but the enhanced vegetation indices are lower. The result shows that the salt distribution map inverted by measured soil salinity values is consistent with the spatial distribution of Enhanced Vegetation indices. It indicates that the enhanced vegetation indices have a higher sensitivity to soil salinity,which can better reverse the spatial distribution of soil salinity in the study area. (3)From the comparison and analysis of those models, which builds the three enhanced vegetation indices and measured soil salt content respectively.We found that the enhanced ratio vegetation index is the best choice to construct the quadratic function model. The determination coefficient of its validation set (R2) is 0.92, and the root mean square error (RMSE) is 2.48, and the relative analysis error (RPD) is 2.09. The data show that this model is more accurate and reliable. In summary, ERVI is more sensitive to soil salinity and predict the soil salinity content, while is more suitable for inversion of soil salinity in this study area. Therefore, the study indicates that it is feasible to invert the soil salinity by the enhanced vegetation index constructed by the b6 and b7 band of Landsat8 multipectral remote sensing imagery. And its inversion effect is better than that of traditional visible light band. Therefore, this study not only provides a theoretical reference for remote sensing inversion, but also has important implications for the quantitative estimation and dynamic monitoring of soil salinity for the study area. Otherwise, it can be used as an alternative offer for prediction of soil salt content in other regions.