Spatiotemporal Trend and Influencing Factors of Surface Soil Moisture in Eurasian Drylands over the Past Four Decades

Eurasian drylands are vital for the global climate and ecological balance. Quantifying spatiotemporal variations in surface soil moisture (SSM) is essential for monitoring water, energy, and carbon cycles. The suitability of recent global-scale surface soil moisture datasets for Eurasian arid and semi-arid regions has not been comprehensively evaluated. This study investigates spatiotemporal trends of five SSM products—MERRA-2, ESACCI, GLEAM, GLDAS, and ERA5—from 1980 to 2023. The performance of these products was evaluated using in situ station data and the three-cornered hat (TCH) method, followed by partial correlation analysis to assess the influence of environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET), vapor pressure deficit (VPD), and leaf area index (LAI), on SSM from 1981 to 2018. The results showed consistent SSM patterns: higher values in India, the North China Plain, and Russia, and lower values in the Arabian Peninsula, the Iranian Plateau, and Central Asia. Regionally, MAT, PET, VPD, and LAI increased significantly (0.04 °C yr−1, 1.66 mm yr−1, 0.004 kPa yr−1, and 0.003 m2 m−2 yr−1, respectively; p < 0.05), while MAP rose non-significantly (0.29 mm yr−1). ERA5 exhibited the strongest correlation with in situ station data (R2 = 0.42), followed by GLEAM (0.37), ESACCI (0.28), MERRA2 (0.19), and GLDAS (0.17). Additionally, ERA5 showed the highest correlation (correlation = 0.72), while GLEAM had the lowest bias (0.03 m3 m−3) and ESACCI exhibited the lowest ubRMSE (0.03 m3 m−3). The three-cornered hat method identified ERA5 and GLDAS as having the lowest uncertainties (<0.03 m3 m−3), with ESACCI exceeding 0.05 m3 m−3 in northern regions. Across land cover types, cropland had the lowest uncertainty among the five SSM products, while forest had the highest. Partial correlation and dominant factor analysis identified MAP as the primary driver of SSM. This study comprehensively evaluated SSM products, highlighting their strengths and limitations. It underscored MAP’s crucial role in SSM dynamics and provided insights for improving SSM datasets and water resource management in drylands, with broader implications for understanding the hydrological impacts of climate change.