Soil salinization is one of the major constraints to agricultural production, and accurate monitoring of soil salinization is particularly important. The ground-based measured salinity data and unmanned aerial vehicle (UAV) data collected during April 8-12, 2023, in four experimental areas of Hetao Irrigation District were used to construct partial least squares regression (PLSR), random forest (RF), backpropagation neural network (BPNN) and support vector machine regression (SVR) inversion models for soil salt content (SSC). The experimental results obtained from the inversion of the optimal model were analyzed. The soil salt distribution maps of the experimental area obtained from the inversion of the optimal model were resampled to be 1m, 5m, and 10m by using the Nearest, Bilinear, and Cubic methods, respectively, and the average values of the corresponding image elements of the Sentinel-2A satellites in the same period were calculated as the salt content of the inversion model constructed by the satellites, and then compared with the optimal model at all scales. The optimal model at each scale was analyzed and the soil salinity distribution map of Hetao Irrigation Area was drawn. The results showed that the correlation of the Bilinear method at three scales was slightly better than the other two resampling methods. The accuracy ranking of the models constructed at five scales, from the highest to the lowest, was 0.07m, 1m, 5m, 10m, and the original SSC (OSSC), the best model determination coefficient R2 of the training set and validation set at the optimal scale of 0.07m was 0.24 and 0.30 higher than that of OSSC, respectively, and the root mean square error (RMSE) was 0.06 percentage points and 0.19 percentage points lower. The research explored the promotion effect of multi-scale soil salinity on the accuracy of soil salinity model inversion by satellite multispectral remote sensing platform, which provided an effective theoretical basis for multi-source remote sensing large-scale accurate soil salinity inversion.