Retrieval of Sub-Kilometric Relative Surface Soil Moisture With Sentinel-1 Utilizing Different Backscatter Normalization Factors

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil's ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values.

Automated summary

The spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. This study monitored the relative surface soil moisture (rSSM) in the Thames Valley, U.K., using Sentinel-1 data and the TU-Wien Change Detection Algorithm. The study explored the effects of normalization factors and spatial averaging on rSSM values at different spatial resolutions. Comparisons with in situ soil moisture data showed temporal trends agreement but difficulties in comparison due to measurement depth and vegetation impacts. The study found that rSSM trends can be retrieved at resolutions as low as 100 m and RMSE decreases with increasing spatial resolution. The study also highlighted the impact of vegetation on rSSM.