Global-Scale Assessment of Multiple Recently Developed/Reprocessed Remotely Sensed Soil Moisture Datasets

The comprehensive and robust assessment of diverse global-scale satellite-based soil moisture (SM) products from various satellite data sources (e.g., different frequencies and incidence angles) and retrieval algorithms is essential for the refinements as well as applications of these products. To date, soil moisture retrieval algorithms and products are rapidly evolving and their updated iterations are ongoing. In support of the validation activities of recently developed/reprocessed satellite soil moisture products, the study first assessed eight commonly employed satellite soil moisture datasets comprising soil moisture active passive (SMAP) (DCA, IB, and MTDCA), soil moisture and ocean salinity (SMOS)-IC, Advanced Microwave Scanning Radiometer-2 (AMSR2) [Land Parameter Retrieval Model (LPRM) and Japan Aerospace Exploration Agency (JAXA)], FY-3C, and European Space Agency (ESA) CCI on a global scale using three different strategies, i.e., ERA5 reanalysis soil moisture dataset with similar spatial resolution to satellite products, in situ measurements from densely instrumented networks worldwide with mitigated spatial mismatch between ground site and satellite pixel and the extended triple collocation (ETC) method that can obtain error indicators relative to ground truth. The skills of these products under a broad range of vegetation density, land cover (LC) and climate types, and surface heterogeneity [heterogeneity in terrain, LC, soil texture (ST), and vegetation coverage] were also examined. The results indicate: 1) different soil moisture products show overall consistency in skill ranking under three different evaluation strategies, except for SMAP DCA, SMAP-IB, and SMAP MTDCA in terms of $R$ value; 2) ESA CCI, SMAP-IB, and SMAP DCA products generally perform better than the others under three strategies, and SMOS-IC and SMAP MTDCA also show satisfactory performance concerning unbiased root mean square difference (ubRMSD) and $R$ values; 3) vegetation density exerts visible influences on satellite soil moisture datasets. Specifically, the C-/X-band (AMSR2 and FY-3C) and L-band (SMAP and SMOS) products display the optimal skills under sparse and moderate vegetation coverage, respectively, and the impacts of vegetation density on C-/X-band products are evidently stronger than those on L-band datasets. The errors of satellite soil moisture data also increase as the increase of heterogeneity in terrain, LC, and vegetation coverage, while the effect of heterogeneity in ST on the skill of satellite soil moisture products is insignificant; and 4) the skills of L-band products are more stable than those of C-/X-band datasets under different ground conditions.