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Soil moisture is the key to the study of energy and material exchange in the soil-plant-atmosphere circulatory system. Using the scale conversion method to push up the remote sensing data from the UAV to correct the satellite data can effectively improve the accuracy of the satellite remote sensing inversion model. Taking Hetao Irrigation Area as the research object, the resampling and TsHARP scale-up method were adopted respectively, and algorithms such as multiple linear regression (MLR), BP neural network (BPNN) and support vector machine (SVM) were introduced to construct UAV-satellite scale-up soil moisture content inversion model under different soil depths. The research results showed that the overall accuracy of the resampling scale-up method was SVM, MLR and BPNN from high to low in different soil depths, among which the SVM model was the best when the soil depth was 0~60cm, R2 was 0.571, and RMSE was 0.022%. The overall accuracy of the model of TsHARP scale-up method under different soil depths was BPNN, SVM and MLR from high to low, among which the BPNN model was the best under 0~60cm soil depth, R2 was 0.829, and RMSE was 0.015%. Compared with the corresponding soil depth model before scaling up, both scale-up methods can significantly improve the retrieval accuracy of soil moisture content from satellite remote sensing, but TsHARP scale-up method was better than resampling method as a whole; R2 of resampling method was increased from 0.413 to 0.571, and RMSE was decreased from 0.026% to 0.022% (a decrease of 15.4%); R2 of TsHARP scale-up method was increased from 0.428 to 0.829, and RMSE was decreased from 0.025% to 0.015% (a decrease of 40.0%). The research result can provide theoretical and technical support for highprecision monitoring of soil moisture in large-scale irrigation areas.