Intercomparison of very high-resolution surface soil moisture products over Catalonia (Spain)

The surface soil moisture (SSM) is a key variable for monitoring hydrological, meteorological and agricultural processes. It can be estimated from active and passive microwave remote sensing data. While coarse-resolution SSM products (> 1 km) have already been evaluated for a large range of ecosystems, such assessments lack very high-spatial-resolution SSM products, although they are increasingly available thanks to very high-resolution radar data or disaggregation methods applied to coarse-scale products. Within this context, the aim of the current study is to carry out, for the first time, an intercomparison of high-spatial resolution SSM products using a large in situ SSM database collected from 33 fields located in the Ebro basin (Spain) that were cultivated with different crops and irrigated using different techniques. Three products are considered: (i) SSMTheia at the field scale derived from Sentinel-1 and Sentinel-2 data using a machine learning algorithm; ii) SSM rho at 50-m resolution derived from the Sentinel-1 data using both the backscattering coefficient and the interferometric coherence based on the inversion of a simple radiative transfer model; and iii) SSMSMAP20m at 20-m resolution obtained by disaggregating SMAP SSM using Sentinel-3 and Sentinel-2 data. The statistical metrics computed on the whole database show that the two Sentinel-1 products outperform the disaggregated approach and that the SSM rho product exhibits better statistical metrics than the SSMTheia product. This is mainly attributed to the inability of the SSMTheia approach to retrieve SSM >0.3 m(3)/m(3.) The correlation coefficients are >0.4 (up to 0.8) for 72%, 40% and 27% of the fields using SSM rho, SSMTheia and SSMSMAP20m, respectively. Similarly, 80% of the fields had RMSE values between 0.06 m(3)/m(3) and 0.1 m(3)/m(3) using SSM rho product against 36% using SSMTheia and 27% using SSMSMAP20m. In addition, the time series analysis showed that SSMSMAP20m was able to detect large-scale wetting events such as rainfall that impacted the whole SMAP pixel while irrigation at the field scale was not detected, mainly because the very high-resolution Sentinel-2 data used for the disaggregation of Sentinel-3 land surface temperature were not related to the hydric status of the surface. The results show that while both Sentinel-1 products perform reasonably well for cereals and, to a lesser extent, for annuals, a drastic drop of the metrics is observed for tree crops. Finally, the spatial SSM pattern over the study area is also better depicted by the Sentinel-1 products than by the SSMSMAP20m by comparison to the airborne GLORI GNSS-R (Global Navigation Satellite System Reflectometry) SSM maps. This study highlights the limitations of SSM products over tree crops and provides insights for improving irrigation scheduling at the field scale.