Accurate estimation of soil moisture content (SMC) is critical for agricultural irrigation, water resource management, and monitoring the ecological environment. The development of multi-sensor UAV platforms offers a novel approach to cross-scale SMC monitoring. This study presents an innovative framework that integrates ground, UAV, and satellite data to estimate SMC and generate county-scale spatial distribution maps of SMC in winter wheat fields. Firstly, UAV images were utilized at the subplot scale to extract winter wheat planting areas through supervised classification, and SMC was estimated by employing partial least squares regression (PLSR). Subsequently, the UAV SMC mapping results were upscaled and integrated with Sentinel-1 synthetic aperture radar (SAR) features and Sentinel-2 multispectral features to develop XGBoost-based satellite-scale SMC estimation model. The study demonstrated that at the plot scale, combining vegetation indices and texture features achieved the highest accuracy (0–20 cm: R² = 0.775, RMSE = 0.018 m³/m³; 20–40 cm: R² = 0.723, RMSE = 0.021 m³/m³). At the satellite scale, the XGBoost model also performed well (0–20 cm: R² = 0.901, RMSE = 0.0071 m³/m³; 20–40 cm: R² = 0.884, RMSE = 0.011 m³/m³). Furthermore, compared to traditional ground-satellite models, the integrated ground-UAV-satellite approach improved accuracy, with R² increasing by 9.53–10.52 %, RMSE decreasing by 11.11–1.25 %, and MAE reducing by 18.19–25.00 %. This cross-scale remote sensing framework enhances SMC monitoring efficiency and accuracy, offering a robust solution for large-scale applications.