Soil moisture retrieval from Sentinel-1: Lessons learned after more than a decade in orbit

作者：Mehdi Rahmati, Anna Balenzano, Michel Bechtold, Luca Brocca, Anke Fluhrer, Thomas Jagdhuber, Kleanthis Karamvasis, David Mengen, Rolf H. Reichle, Seung-Bum Kim, Ruhollah Taghizadeh‐Mehrjardi, Jeffrey P. Walker, Liujun Zhu, Carsten Montzka · 发表于：Remote Sensing of Environment · 年份：2025 · DOI：10.1016/j.rse.2025.115146 · 被引用次数：25 · 研究领域：Soil Moisture and Remote Sensing、Synthetic Aperture Radar (SAR) Applications and Techniques、Precipitation Measurement and Analysis

Soil moisture is a critical variable for hydrology, agriculture and climate. However, large-scale soil moisture observation remains difficult due to sparse in situ networks and the inability of optical sensors to capture it under cloud cover. Synthetic aperture radar (SAR) missions, e.g., Sentinel-1, yield unique all-weather, day and night observations with a fine spatial and temporal resolution that makes them of interest for development of global soil moisture monitoring. Consequently, this review discusses the application of C-band SAR observations from the Sentinel-1 satellite mission to estimate high-resolution near-surface soil moisture. First, the importance of SAR backscatter monitoring from Sentinel-1 is emphasized. Next, the current state-of-the-art in soil moisture retrieval from Sentinel-1 is presented. Although considerable progress has been made in near-surface soil moisture retrieval, several limitations remain. Factors such as the effects of vegetation and surface roughness on the signal, sensor and scattering model limitations, spatial and temporal constraints, and uncertainties, e.g. in data assimilation, pose challenges to its usage. While Artificial Intelligence (AI)-based retrieval methods have shown promise, their interpretability, dependence on large datasets, vulnerability to data quality, and computational burden have been major challenges. Beyond methods that rely on backscatter, there have been recent works indicating that SAR interferometric observ...