用卫星数据实现欧洲农田级土壤湿度高精度估算，10米分辨率实用性强。
Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
- 融合哨兵1号、2号和气象再分析数据，机器学习建模提升精度。
- 10天气象数据回溯使相关系数达0.518，优于单一数据源。
- 传统特征工程比大模型嵌入更有效，适合实际业务部署。
精准的土壤湿度（SM）估计对精准农业、水资源管理和气候监测至关重要。现有卫星产品空间分辨率过低（>1公里），难以满足农田级应用需求。本文提出一种面向欧洲植被区的10米高分辨率土壤湿度估算框架，融合哨兵-1 SAR、哨兵-2光学影像及ERA-5再分析数据，采用机器学习方法进行建模。基于覆盖多样植被区的113个国际土壤湿度网络（ISMN）站点数据，通过空间交叉验证评估不同模态组合与时间参数化策略，确保地理泛化能力。同时检验了IBM-NASA Prithvi基础模型嵌入是否优于传统手工设计的光谱特征。结果表明，混合时间匹配策略——当日哨兵-2影像搭配哨兵-1下降轨道数据——达到R²=0.514；引入10天前的ERA5数据回溯后性能提升至R²=0.518。Prithvi模型嵌入相比传统特征仅带来微小提升（R²=0.515 vs. 0.514），表明在数据稀疏的回归任务中，传统特征工程仍具竞争力。研究显示，结合领域特定光谱指数与树基集成方法，可为全欧洲范围的田块尺度土壤湿度监测提供高效且实用的解决方案。
原文摘要 · Abstract (English)
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.
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