Accurate soil moisture (SM) estimation is critical for precision agriculture, water resources management and climate monitoring. Yet, existing satellite SM products are too coarse (>1km) for farm-level applications. We present a high-resolution (10m) SM estimation framework for vegetated areas across Europe, combining Sentinel-1 SAR, Sentinel-2 optical imagery and ERA-5 reanalysis data through machine learning. Using 113 International Soil Moisture Network (ISMN) stations spanning diverse vegetated areas, we compare modality combinations with temporal parameterizations, using spatial cross-validation, to ensure geographic generalization. We also evaluate whether foundation model embeddings from IBM-NASA's Prithvi model improve upon traditional hand-crafted spectral features. Results demonstrate that hybrid temporal matching - Sentinel-2 current-day acquisitions with Sentinel-1 descending orbit - achieves R^2=0.514, with 10-day ERA5 lookback window improving performance to R^2=0.518. Foundation model (Prithvi) embeddings provide negligible improvement over hand-crafted features (R^2=0.515 vs. 0.514), indicating traditional feature engineering remains highly competitive for sparse-data regression tasks. Our findings suggest that domain-specific spectral indices combined with tree-based ensemble methods offer a practical and computationally efficient solution for operational pan-European field-scale soil moisture monitoring.

翻译：精确的土壤湿度估算对于精准农业、水资源管理和气候监测至关重要。然而，现有卫星土壤湿度产品的空间分辨率过于粗糙（>1公里），难以满足农田尺度的应用需求。本研究提出了一种适用于欧洲植被覆盖区的高分辨率（10米）土壤湿度估算框架，该框架通过机器学习方法融合了Sentinel-1合成孔径雷达数据、Sentinel-2光学影像以及ERA-5再分析数据。利用覆盖多种植被类型的113个国际土壤湿度网络站点，我们通过空间交叉验证比较了不同模态组合与时间参数化方案，以确保模型的地理泛化能力。同时，我们评估了IBM-NASA Prithvi模型生成的基础模型嵌入特征是否优于传统手工构建的光谱特征。结果表明：采用混合时间匹配策略——即当前日期的Sentinel-2影像与Sentinel-1降轨数据相结合——可实现R^2=0.514的精度；而引入10天ERA5数据回溯窗口可将性能提升至R^2=0.518。基础模型（Prithvi）嵌入特征相较于手工特征仅带来微小改进（R^2=0.515 vs. 0.514），这表明在稀疏数据回归任务中，传统特征工程方法仍具有高度竞争力。我们的研究结果表明，结合领域专用光谱指数与基于树的集成方法，可为业务化泛欧农田尺度土壤湿度监测提供实用且计算高效的解决方案。