Abstract
This research is the first attempt to extensively assess the accuracy of commonly used surface and subsurface soil moisture (SM) datasets in reproducing temporal dynamics and trend changes SM observations for a long period on the Tibetan Plateau (TP). To this end, 12 satellite-based and 10 model-based SM products are evaluated using a 10-year upscaled SM dataset obtained from two in situ monitoring networks situated respectively in humid and arid climate zones over the TP. Moreover, the ability of using the outperforming satellite-based surface SM data in combination with cumulative distribution function (CDF) matching and exponential filter (ExpF) methods to improve model-based SM profile estimations is investigated for the first time. Among the 12 satellite-based products, SMAP_L3 provides the best surface SM retrievals, with the DCA and SCA-V products showing superior performance in the humid and arid areas, respectively. Consequently, the harmonized products produced based on the SMAP data (i.e., SMOSMAP and NNsm) also give relatively higher accuracy. It is surprised to find that the latest version of both CCI passive and active products presents degraded performance compared to its predecessor (i.e. v5.3). Among the 10 model-based products, SMAP_L4 with assimilation of L-band brightness temperature (TB) measurements provides relatively better estimations of surface and subsurface SM on the TP. Above findings highlight the superiority of utilizing the L-band TB measurements especially from the SMAP satellite to generate surface SM and improve the model-based SM estimations via data assimilation. Alternatively, it is further demonstrated that incorporating surface SM data from SMAP_L3, in conjunction with both CDF matching and ExpF methods, significantly enhances the precision of model-based SM estimations. This may open up a new possibility for improving the model-based SM profile data across the TP.
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