Blended Soil Moisture Across the Qinghai-Tibetan Plateau Using Triple Collocation Based on Reanalysis Datasets

Satellite remote sensing-based soil moisture (SM) retrieval quantifies the spatial and temporal distributions of SM to support Earth system modeling. However, existing SM products, including satellite remote sensing, model-simulated, and land data assimilation products, are plagued by large measurement errors. The Triple Collocation (TC) method can systematically quantify these errors and generate spatially and temporally continuous SM. In this study, we analyzed SM over the Qinghai-Tibetan Plateau (QTP) using three mainstream products: The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-interim) SM, the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) SM, and the China Meteorological Administration Land Data Assimilation System Version 1.0 (CLDAS-V1.0) SM. Results show that the ERA-interim contributes the largest weight to the TC-blended SM over the QTP, followed by CFSv2, while CLDAS-V1.0 makes the minimum contribution. The three products yield consistent results in the eastern and southern QTP but show significant discrepancies in the northwestern region. The TC-blended SM performs well across most land cover categories in the QTP, except Alpine swamp meadow areas. Our findings confirm that this SM blending technique effectively improves the accuracy of existing SM products, with wide application potential in future research.