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UAV-satellite remote sensing scale-up transformation method can effectively improve the monitoring accuracy of soil salt content. Sand trench canal irrigation area in Hetao Irrigation Area of Inner Mongolia was taken as the study area, the surface soil in bare soil period in April was taken as the research object. The dominant class variability-weighted method, local average method and nearest neighbor method were used to scale up the quadruple-band image (0.1 m) of UAV in the experimental area to the same scale as GF-1 satellite (16m). Subsequently, three combinations of variables were introduced as the input variables of the model for the UAV dataset and GF-1 satellite dataset, and the quantitative monitoring model of soil salt content was constructed by using multivariable linear regression (MLR) and back propagation neural networks (BPNN). On this basis, the GF-1 satellite data was modified by the mean band ratio method, and the scale-up inversion of soil salinity in the study area based on satellite factors was realized. The results showed that the dominant class variability-weighted method had the best monitoring effect, followed by the local average method. The nearest neighbor method had the worst monitoring effect among the three UAV-satellite remote sensing scale-up transformation methods;after comparing the four statistical evaluation indexes of mean value, standard deviation, information entropy and average gradient with the original UAV image, it was found that the quadruple-band UAV image pushed by the three methods had scale differences with the original image data to different degrees;by comparing R2 and RMSE of three variable combinations based on different data sources, it was found that the accuracy of the model constructed by the dominant class variability-weighted method was better than that of the other three data sources as a whole, and the scale-up dataset of the dominant class variability-weighted method based on mixed variable groups achieved the best monitoring effect in MLR model and BPNN model;the monitoring model with the best validation effect was multivariate linear regression model, its validation R2 was 0.420, RMSE was 0.219%. The research results can provide reference for integrated monitoring of farmland soil salt content in bare soil period by multi-spectral remote sensing of satellite and unmanned aerial vehicle.