ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1672787
This article is part of the Research TopicUnravelling Soil Moisture Dynamics and Their Roles in Climate Model SensitivityView all articles
Evaluation of ERA5 reanalysis and ECV satellite soil moisture products based on in-situ observations over Jiangsu, China
Provisionally accepted
- 1Meteorological Services Center, Jiangsu Meteorological Bureau, Nanjing, China
- 2Public Meteorological Service Center, China Meteorological Administration, Beijing, China
- 3Rabdan Academy Abu Dhabi, Abu Dhabi, United Arab Emirates
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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 (SMERA5) and Essential Climate Variable (ECV) satellite-derived soil moisture (SMECV) against in-situ measurements from 2013–2015 in Jiangsu Province, China. Taking SMin-situ as the reference, the SMERA5 outperforms the SMECV in terms of correlation coefficient (0.56 for SMERA5 and 0.42 for SMECV) and Triple Collocation (TC) errors (0.01m3·m-3 for SMERA5 and 0.025m3·m-3 for SMECV). However, the SMECV can better characterize the soil moisture with smaller random differences (ubRMSD=0.045 m3·m-3 for SMECV and 0.052 m3·m-3 for SMERA5) relative to the SMin-situ data. Both SMECV and SMERA5 exhibit consistent spatial patterns across seasons, although with notable magnitude differences. These two products effectively capture in-situ soil moisture (SMin-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 SMERA5 data more effectively capture soil moisture response to heavy precipitation events than SMECV. Overall, SMERA5 demonstrates superior performance in temporal correlation and precipitation sensitivity, whereas SMECV 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.
Keywords: Spatial pattern, temporal dynamics, Correlation coefficients, triple collocation, differences, Uncertainties
Received: 28 Jul 2025; Accepted: 12 Sep 2025.
Copyright: © 2025 Shi, Yang and Ullah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiao Shi, Meteorological Services Center, Jiangsu Meteorological Bureau, Nanjing, China
Ruyi Yang, Public Meteorological Service Center, China Meteorological Administration, Beijing, China
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