In order to explore the feasibility of GF-1 satellite inversion of farmland soil moisture content（SMC）under the condition of vegetation coverage, taking Shahaoqu District of Hetao Irrigation Area as study area, and GF-1 satellite remote sensing images as the data source. Simultaneously, the soil moisture content data were collected with various depths at 0～20cm, 20～40cm, 40～60cm, 0～40cm,and 0～60cm. Then a set of independent variables, including four bands and 15 spectral indices were obtained based on the GF-1 data, and the full subset selection was used to select the optimal combination of independent variables at five depths. Based on these, the combinations before and after full subset selection were used to build soil moisture content inversion models（multiple linear regression, MLR;back propagation neural network, BPNN;support vector machines, SVM）at five depths in the vegetated area, and evaluate the sensitivity of GF-1 to SMC at different depths and the inversion capability of the models. The model performance was assessed by using adjusted coefficient of determination (R2adj) and root mean square error (RMSE). The results showed that the model inversion accuracy was greatly improved after the full subset selection, and the overfitting phenomenon can be reduced. The sensitivity of GF-1 to the SMC at different depths under vegetation coverage was ordered from the largest to the smallest as follows: 0~40cm, 0~60cm, 20~40cm, 0~20cm, and 40~60cm. The SMC inversion capabilities of all the three models under vegetation coverage ordered from the largest to the smallest were as follows: BPNN, SVM, and MLR. After the full subset selection, the R2adj of the modeling set and verification set of BPNN at depth of 0~40cm can reach more than 0.50, and the RMSE was within 0.02%. The research result can provide a reference for using GF-1 satellite to monitor SMC of farmland under vegetation coverage.