Sentinel-1 soil moisture at 1 km resolution: a validation study

This study presents an assessment of a pre-operational soil moisture product at 1 km resolution derived from satellite data acquired by the European Radar Observatory Sentinel-1 (S-1), representing the first space component of the Copernicus program. The product consists of an estimate of surface soil volumetric water content Theta [m(3)/m(3)] and its uncertainty [m(3)/m(3)], both at 1 km. The retrieval algorithm relies on a time series based Short Term Change Detection (STCD) approach, taking advantage of the frequent revisit of the S-1 constellation that performs C-band Synthetic Aperture Radar (SAR) imaging. The performance of the S-1 Theta product is estimated through a direct comparison between 1068 S-1 Theta images against in situ Theta measurements acquired by 167 ground stations located in Europe, America and Australia, over 4 years between January 2015 and December 2020, depending on the site. The paper develops a method to estimate the spatial representativeness error (SRE) that arises from the mismatch between the S-1 Theta retrieved at 1 km resolution and the in situ point-scale Theta observations. The impact of SRE on standard validation metrics, i.e., root mean square error (RMSE), Pearson correlation (R) and linear regression, is quantified and experimentally assessed using S-1 and ground Theta data collected over a dense hydrologic network (4-5 stations/km(2)) located in the Apulian Tavoliere (Southern Italy). Results show that for the dense hydrological network the RMSE and correlation are similar to 0.06 m(3)/m(3) and 0.71, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km(2), the SRE increases the RMSE by similar to 0.02 m(3)/m(3) (70% Confidence Level). Globally, the S-1 Theta product is characterized by an intrinsic (i.e., with SRE removed) RMSE of similar to 0.07 m(3)/m(3) over the Theta range [0.03, 0.60] m(3)/m(3) and R of 0.54. A breakdown of the RMSE per dry, medium and wet Theta ranges is also derived and its implications for setting realistic requirements for SAR-based Theta retrieval are discussed together with recommendations for the density of in situ Theta observations.

Automated summary

This study evaluates a pre-operational soil moisture product at 1 km resolution derived from Sentinel-1 radar satellite data. The retrieval algorithm relies on short term change detection using SAR imaging. Results show that for dense hydrological networks, the RMSE and correlation are around 0.06 m(3)/m(3) and 0.71, respectively, while in sparse networks, the RMSE increases by approximately 0.02 m(3)/m(3) (70% Confidence Level). Globally, the S-1 Theta product has an intrinsic RMSE of about 0.07 m(3)/m(3) and a correlation of 0.54.