High resolution surface soil moisture microwave products: intercomparison and evaluation over Spain

Surface soil moisture (SSM) products at high spatial resolution are increasingly available, either from the disaggregation of coarse-resolution products such as SMAP and SMOS, or from high-resolution radar data such as Sentinel-1. In contrast to coarse resolution products, there is a lack of intercomparison studies of high spatial resolution products, which are more relevant for applications requiring the plot scale. In this context, the objective of this work is the evaluation and intercomparison of three high spatial resolution SSM products on a large database of in situ SSM measurements collected on two different sites in the Urgell region (Catalonia, Spain) in 2021. The satellite SSM products are: i) SSMTheia product at the plot scale derived from a synergy of Sentinel-1 and Sentinel-2 using a machine learning algorithm; ii) SSM product at 14 m resolution derived from the Sentinel-1 backscattering coefficient and interferometric coherence using a brute-force algorithm; and iii) SSMSMAP20m product at 20 m resolution obtained from the disaggregation of SMAP using Sentinel-3 and Sentinel-2 data. Evaluation of the three products over the entire database showed that SSMTheia and SSM yielded a better estimate than SSMSMAP20m, and SSM is slightly better than SSMTheia. In particular, the correlation coefficient is higher than 0.4 for 72%, 40% and 27% of the fields using SSM, SSMTheia and SSMSMAP20m, respectively. The lower performance of SSMTheia compared to SSM is due to the saturation of SSMTheia at 0.3 m3/m3. The time series analysis shows that SSMSMAP20m is able to detect rainfall events occurring at large scale while irrigation at the plot scale are not caught. This is explained by the use of Sentinel-2 reflectances, which are not linked to surface water status, for the disaggregation of Sentinel-3 land surface temperature. The approach can therefore be improved by using high spatial and temporal resolution thermal data in the perspective of new missions such as TRISHNA and LSTM. Finally, the results show that although reasonable estimates are obtained for annual crops using SSMTheia and SSM, poor performance is observed for trees, suggesting the need for better representation of canopy components for tree crops in SSM inversion approaches.