Spatiotemporal Evaluation of Downscaled and Native High-Resolution Satellite Soil Moisture Products

Since the launch of ESA’s Sentinel-1 mission, openly accessible soil moisture products at ~1 km spatial sampling have become popular for regional scientific studies and numerous applications in agriculture, on land use/change, and water resource management, among others. However, while community-agreed approaches exist for estimating data quality in the temporal domain - e.g., through comparison with in situ measurement time series - comparable quantitative assessments of spatial performance are still largely lacking. This is due to the limited availability of reference measurements and the lack of methods that can effectively exploit them for spatial evaluation.
Recently, a new Point-Scale-Downsampling (PSD) framework was proposed, which enables the computation of both temporal and spatial performance metrics between satellite observations and in situ point measurements. The framework uses coarse-scale benchmark data to assess relative differences between products across spatial scales.

In this presentation, we show results from a recent intercomparison study of native (Sentinel-1) and downscaled (SMAP, SMOS, ASCAT, ESA CCI) 1 km soil moisture products over Europe. We compute temporal and spatial performance metrics using the PSD framework with respect to reference in situ measurements from the International Soil Moisture Network (ISMN). We place our findings in the context of traditional temporal quality assessments and correlogram-based spatial variability characteristics. We conclude that, while high-resolution products overall outperform coarse-resolution benchmark products in terms of spatial information content, the downscaled products at this stage tend to show better spatio-temporal agreement with the available in situ measurements than native SAR retrievals. Additional reference measurements and novel, qualitative approaches to assess the suitability of satellite soil moisture for specific applications could further improve the understanding and reliability of these data in the future.

This study received funding from the European Space Agency (ESA) "Hyper-resolution Earth observations and land-surface modeling for a better understanding of the water cycle" (4Dhydro) project, with tender reference: ESA AO/1-11298/22/I-EF.