Evaluation of ERA5 reanalysis and ECV satellite soil moisture products based on in situ observations over Jiangsu, China

Accurate and spatially continuous soil moisture data are essential for applications including numerical weather prediction, hydrological forecasting, and data assimilation. This study evaluates the global ERA5 reanalysis soil moisture (SM ERA5 ) and Essential Climate Variable (ECV) satellite-derived soil moisture (SM ECV ) against in situ measurements from 2013 to 2015 in Jiangsu Province, China. Five evaluation indices accuracy metrics and Triple collocation methods are used in this study. Taking SM in-situ as the reference, the SM ERA5 outperforms the SM ECV in terms of correlation coefficient (0.56 for SM ERA5 and 0.42 for SM ECV ) and Triple Collocation (TC) errors (0.01 m 3 m -3 for SM ERA5 and 0.025 m 3 m -3 for SM ECV ). However, the SM ECV can better characterize the soil moisture with smaller random differences (ubRMSD = 0.045 m 3 m -3 for SM ECV and 0.052 m 3 m -3 for SM ERA5 relative to the SM in-situ data. Both SM ECV and SM ERA5 exhibit consistent spatial patterns across seasons, although with notable magnitude differences. These two products effectively capture in situ soil moisture (SM in-situ ) temporal dynamics in the northern region, while larger discrepancies occur in the southern region. In addition, we evaluate these products from the perspective of soil moisture sensitivity to precipitation. Results show that SM ERA5 data more effectively capture soil moisture response to heavy precipitation events than SM ECV . Overall, SM ERA5 demonstrates superior performance in temporal correlation and precipitation sensitivity, whereas SM ECV excels in minimizing random errors. Both datasets exhibit uncertainties linked to sensor limitations and model parameterization, suggesting targeted improvements (e.g., multi-sensor fusion, bias correction) could enhance their reliability.