The first assessment of coarse-pixel soil moisture products within the multi-scale validation framework over Qinghai-Tibet Plateau

The validation of satellite soil moisture (SM) products is challenged by the large scale difference between in situ-satellite based measurements. In order to tackle the significant spatial scale mismatch, this study conducted a multi-scale validation of three typical SM products (i.e., SMOS-IC, SMAP L3, and AMSR2 LPRM) over Qinghai-Tibet Plateau. First, a 25-km better-performing SM dataset was produced by combining three SM products with the extended triple collocation (ETC) and arithmetic mean method. Second, a 500 m SM dataset was derived from the 25-km SM dataset with a random forest-based downscaling model and the high-resolution datasets of other variables. Third, the 500 m SM was evaluated using in situ SM measurements, which was then aggregated to a coarse pixel scale for the assessment of coarse-resolution satellite SM products. Finally, potential factors influencing the accuracy of satellite SM products were investigated. The results indicated that the 25-km merged SM product integrated the characteristics of these three SM products. During the downscaling process, terrain factors, NDVI, and day and night temperature difference were identified as the key variables to derive high-resolution SM, which agreed well with in situ measurements over most monitoring networks. The multi-scale validation results indicate that SMAP L3 and AMSR2 LPRM performs best regarding the median values of the correlation and deviation from the pixel scale reference in the spatial domain, respectively, and SMOS-IC is always the worst. However, when the pixel-based evaluation results were focused, AMSR2 LPRM performs best in most cases, followed by SMAP L3 and SMOS-IC. The accuracy of satellite SM products shows more dependence on slope than elevation, land cover types, and land surface temperature.