Evaluating Multi-Source Soil Moisture Products for Root-Zone Soil Moisture Representation in Yunnan, China

Root zone soil moisture (RZSM) is critical for understanding hydrological processes and monitoring agricultural drought, yet its accurate representation remains challenging in topographically complex regions. Using 40 cm in situ SM observations from 19 ground stations in Yunnan Province, China, during 2008–2012 as the reference, this study systematically evaluated the performance of five widely used multi-source soil moisture (SM) products and their different depth layers, including ERA5-Land, GLDAS Noah, GLEAM, ASCAT H141, and CCI SM. A CCI-derived RZSM proxy generated by exponential filtering, hereafter CCI RZSM, was also included. Product performance was assessed using original and deseasonalized time series, and the effects of land-use type, long-term wetness background, and short-term dry conditions on product performance were explicitly examined. The results showed that the intermediate and deeper layers of ERA5-Land and ASCAT H141, especially the 7–28 cm layers, exhibited better performance in capturing RZSM dynamics, achieving a favorable balance among temporal correlation (r > 0.6), random error and systematic bias. Surface-layer products showed limited direct representativeness, and effective RZSM representativeness differed substantially among nominal product layers. Deseasonalization showed that original-series correlations were partly supported by the shared seasonal wet–dry cycle, whereas most products had weaker skill in tracking non-seasonal RZSM anomalies. Environmental background substantially modulated error structures: stronger positive Bias generally occurred at drier stations, Grassland showed higher positive Bias, Cropland showed greater dispersion, and Forest displayed relatively balanced performance. Under dry conditions, temporal correlations declined for nearly all products, whereas increases in random error were mainly concentrated in surface layers. Exponential filtering improved the temporal consistency of CCI SM in representing RZSM, but the filtering with a fixed characteristic time parameter (T) performed worse than filtering with station-optimized T, indicating limited generalizability in ungauged regions. Overall, RZSM representativeness in Yunnan is jointly controlled by product structure, environmental background, and wet–dry conditions. ERA5-Land and ASCAT H141 intermediate-to-deep layers are therefore more suitable for RZSM anomaly and drought applications in Yunnan Province.