Fast acquisition of soil salt content under vegetation cover is the objective need of saline soil management and utilization. Four kinds of croplands with different salinization values in Shahaoqu District of Hetao Irrigation Area were set as the study areas. The UAV equipped with a multispectral camera obtained the remote sensing image data of August, meanwhile, the soil salinity with depth of 0~40cm was tested. The sensitive band group, spectral index group and full variable group were introduced as model input variables. Four regression methods, including support vector machine (SVM), BP neural network (BPNN), random forest (RF) and multiple linear regression (MLR), were used to establish soil salinity inversion models which were based on three groups of input variables, respectively. Firstly, the model precision was evaluated, and then the effects of different input variables and different regression methods on the model precision were compared, finally the best salt inversion model was evaluated and optimized. The results indicated that comparing the R2 and RMSE of three variable groups, the spectral index group achieved the best inversion effect between the four regression model methods, and the sensitive band group and the full variable group had advantages and disadvantages in different regression algorithms. Between the four regression methods, the inversion accuracy of three machine learning regression algorithms was significantly higher than that of the MLR model. Moreover, both the sensitive band group and the full variable group in the MLR model showed the phenomenon of “overfitting”. And RF algorithm performed best between the three machine learning algorithms. Besides, SVM algorithm and BPNN algorithm performed better and worse in the model with different variable groups. The RF salt inversion model based on the spectral index group achieved the best inversion effect among the 12 models, the R2c and R2v reached 0.72 and 0.67, respectively, and the RMSEv error was only 0.112%. The research result can provide a theoretical reference for soil salinity monitoring in arid and semiarid areas.