Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe.

The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1's Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution. HIGHLIGHTS: Dense vegetation reduces the ability to retrieve surface soil moisture at a kilometre scale from Sentinel-1 backscatter which is currently available on the Copernicus Global Land Service portal. Applying selective masking for vegetation during the resampling phase improves Sentinel-1 sensitivity to soil moisture. A novel vegetation-corrected Sentinel-1 surface soil moisture product is processed over Europe for the period 2016–2022 included. The Sentinel-1 forest mask improves the Sentinel-1 SSM product correlation and seasonality compared to both modelled and in-situ datasets. [ABSTRACT FROM AUTHOR]