The soil tillage layer is the foundation of crop growth and development. Accurately monitoring the moisture content of the soil tillage layer and providing precise irrigation to crops can improve crop yield and water resource utilization efficiency. To achieve efficient monitoring of soil tillage layer moisture content, a soil tillage layer moisture content inversion method based on collaborative Kriging interpolation was proposed. Firstly, Sentinel-1 satellite data, which can obtain soil surface information, was used as the data source. Combined with the reliable XGBoost (extreme gradient boosting) model, it can efficiently invert soil surface moisture content. Using the large-scale soil moisture inversion results as covariates and 115 measured soil tillage layer moisture contents as main variables, the synergistic relationship between soil surface and tillage layer variables was utilized to interpolate the soil tillage layer moisture content using the collaborative Kriging method. The collaborative Kriging method can effectively utilize the synergistic relationship between soil surface and tillage layer variables to improve interpolation accuracy, and to some extent solve the problem of insufficient measured data on soil tillage layer moisture content. Comparing the Kriging interpolation of soil tillage layer moisture content with the linear fitting of surface and tillage layer moisture content, the results showed that using collaborative Kriging interpolation to invert soil tillage layer moisture content can significantly improve prediction accuracy. The coefficient of determination R2 was increased by 0.25 and 0.20, the root mean square error (RMSE) was decreased by 0.029cm3/cm3 and 0.014cm3/cm3, and the average absolute error (MAE) was decreased by 0.028cm3/cm3 and 0.015cm3/cm3, respectively. The accuracy was significantly improved.