Soil moisture is a vital parameter for a variety of applications including hydrological modelling and climate change studies, particularly in permafrost regions where freeze-thaw processes and complex terrain pose significant monitoring challenges. This study evaluates the accuracy of seven surface soil moisture (SSM) products (SMOS-IC, ESA CCI, AMSR2 LPRM, SMAP-L3, SMAP-L4, ERA5-Land, GLDAS-Noah) and three root-zone soil moisture (RZSM) products (SMAP-L4, ERA5-Land, GLDAS-Noah) using in situ observations from 19 stations in the permafrost region of the Heihe River Basin, China, from 2012 to 2020. Focusing on the thawing season (July-October), the analysis employs statistical metrics including Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), bias, and slope. Results indicate that SMAP-L3 and SMAP-L4 exhibit the highest SSM accuracy (R = 0.24 and 0.23, respectively) with low ubRMSE (0.037-0.038), while ERA5-Land shows the best RZSM correlation (R = 0.43) but may indicate the presence of systematic biases, nonlinear responses, or limitations in dynamic range, among other issues (slope = 0.01). Environmental factors such as precipitation, land surface temperature, and normalised difference vegetation index significantly influence accuracy. Spatial variability and scale mismatches highlight the need for improved land surface models and data assimilation. This study provides critical insights for selecting and refining soil moisture products to enhance hydrological and climate research in permafrost regions.

来源平台：INTERNATIONAL JOURNAL OF CLIMATOLOGY