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

Abstract

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 observables have the potential to estimate soil moisture, especially in arid and semi-arid regions where these are particularly sensitive to moisture changes. To address these challenges, this paper recommends integrating Sentinel-1 with other satellite mission data for a multi-sensor data integration approach (e.g., Sentinel-2 and Soil Moisture Active Passive - SMAP data), refining physical and semi-empirical models, developing advanced AI techniques able to consider physical principles, and combining with emerging data from other high temporal resolution SAR missions (e.g., NASA-ISRO SAR). The review concludes with identification of key research priorities, including standardization of retrieval frameworks, improved validation efforts on standardized reference sets, and cloud processing for real-time user cases. Overall, the review provides a thorough foundation for understanding, refining, and advancing Sentinel-1 based soil moisture retrieval methods.