Intercomparison of current soil moisture products from remote sensing and modeling over COSMOS field sites in Germany

Soil moisture (SM) is a critical part of the terrestrial water cycle, drives land–atmosphere interactions, and can represent hydro-climatic extremes such as floods and droughts. Numerous SM products from remote sensing and modeling were developed within the last decades to investigate SM dynamics on a large scale. However, a manifold of their retrieval algorithms, resolutions and coverages of horizontal, vertical, and temporal domains make a fair intercomparison challenging. The focus of this study is the intercomparison of the temporal SM dynamics of 15 selected SM products over 25 field sites in Germany using SM estimations from ground-based sensors of the Cosmic-Ray Soil Moisture Observation System (COSMOS) as a reference. A temporal coverage of 2015–2020 was selected, covering the European drought of 2018/19. SM estimations from COSMOS intrinsically average out the spatial heterogeneity of the surrounding environmental properties and cover the dynamics of both, surface SM (SSM) and root-zone SM (RZSM). This makes them a valuable ground reference for the validation of coarse-resolution SM products from remote sensing and modeling on the horizontal domain. On the vertical domain, the deeper vertical representation of COSMOS estimations is a challenge for the validation of SM estimations from remote sensing which capture SSM dynamics only. The newly released extensive COSMOS Europe data set contains hourly time series of in-situ SM at many locations. It allowed a comprehensive intercomparison and validation of the selected SM products over locations of different land cover types in Germany. We have selected SSM products from single remote sensors (AMSR2 L3, ASCAT L3 (H115/H116), Sentinel-1 L2, SMAP L3E, and SMOS L3), from dual sensors (Sentinel-1/ASCAT L3 and SMAP/Sentinel-1 L2), and from multiple sensors (ESA CCI and NOAA SMOPS). These SSM products have furthermore been vertically extrapolated using an exponential filter to additionally investigate their potential of resolving RZSM dynamics. In addition, we have selected products that already comprise both SSM and RZSM. These were obtained either through the assimilation of remote sensing SSM estimations into models (ASCAT L3 (H141/H142), SMAP L4, GLDAS-2 L4, and GLEAM), through exponential filtering of remote sensing SSM estimations (SMOS L4), or through reanalysis (ERA5-Land). We found that all selected products show a similar seasonal variability, but represent the sub-seasonal variability differently. For this we have analyzed bias and uncertainty estimations as static and dynamic measures, respectively. The match of SM dynamics of the selected SSM products with the SM dynamics obtained from COSMOS increases after applying an exponential filter. The same is true for the comparison of SM dynamics from COSMOS with those within lower layers of RZSM products. Nevertheless, the RZSM dynamics cannot be completely resolved by the selected products, neither by exponential filtering of given SSM data nor by published RZSM data. Our findings contribute to providing a systematic evaluation of state-of-the-art large-scale SM products and insights on how to improve SM estimation. Future work is needed to extend our study to a European scale to increase the complexity of environmental properties of the ground reference field sites.