Accurate estimation of soil moisture content (SMC) is of great significance for precision agriculture and water resources management in arid areas. Traditional estimation methods and field measurements are time consuming and labor intensive. Therefore, we obtain hyperspectral image data of winter wheat plots in Fukang City, Xinjiang by unmanned aerial vehicle platform, and the original hyperspectral data are preprocessed through first derivative, second derivative, absorbance, first derivative of absorbance (FDA), and second derivative of absorbance. Random forest (RF), gradient boosted regression tree (GBRT), and extreme gradient boost (XGBoost) are used to select the importance of feature variables. A model is established based on geographical weighted regression (GWR). The results show that the pretreatment effect of FDA is the best. The model based on FDA-GBRT is optimal. The determination coefficient (R²) of the modeling set and the verification set are 0.890 and 0.891, respectively, and the quartile interval reaches 3.490. Compared with RF and XGBoost algorithms, the advantages of the GBRT algorithm are more prominent. The R² of most of the model modeling set and the verification set are greater than 0.600. This indicates that the GWR model is effective in predictive modeling of SMC and can provide theoretical support for the management and protection of agro ecosystem in arid regions.