ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
This article is part of the Research TopicUnravelling Soil Moisture Dynamics and Their Roles in Climate Model SensitivityView all articles
Validation and Ensemble-Based Layer-wise Correction of Soil Moisture Observations from Automatic Stations
Provisionally accepted
- 1Xilinhot National Climate Observatory, Xilinhot, China
- 2Inner Mongolia Eco-And Agro-Meteorological Centre, Hohhot, China
- 3Nanjing University of Information Science and Technology, Nanjing, China
- 4Chinese Academy of Sciences Aerospace Information Research Institute, Beijing, China
- 5Xilinhot National Meteorological Observatory, Xilinhot, China
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Soil moisture is one of the fundamental variables in land–atmosphere interactions, hydrological processes and vegetation dynamics. Accurate soil moisture information is of great significance for environmental and climate studies. In recent years, automatic monitoring stations have been increasingly deployed owing to their advantages of high-frequency and standardized measurements. However, their measurements suffer from nonnegligible biases, limiting the reliability of automatic soil moisture datasets. To address these challenges, this study conducts a comprehensive validation and machine-learning-based correction of three automatic soil moisture systems: the automatic soil moisture station, the CRS-2000C regional soil moisture measurement system, and the soil temperature–moisture monitoring system at the Xilinhot National Climate Observatory, China. Using manual measurements as reference, the observations from the three automatic stations were validated. All the automatic soil moisture measurements revealed substantial biases, especially in deeper soil layers. To reduce the biases, an ensemble correction framework that employed generalized additive model (GAM) to integrate Cubist, Random Forest, XGBoost, and CatBoost models was developed for layer-wise soil moisture correction. Five-fold cross-validation was applied to evaluate correction performance. After correction, the accuracy of all stations improved significantly, with R2 increasing by 0.075–0.289, MAE decreasing by 0.012–0.057 m³/m³, and RMSE decreasing by 0.013–0.075 m³/m³, with particularly pronounced improvements in deeper layers. This study highlights the necessity of correcting automatic soil moisture observations and provides an effective framework for the correction.
Keywords: automatic soil moisture monitoring station, correction, CRS-2000C regional soil moisture measurement system, the soiltemperature–moisture monitoring system, Validation
Received: 24 Oct 2025; Accepted: 04 Jan 2026.
Copyright: © 2026 Li, Li, Ji, Xu, Xu, Wang, Yan, Chen and Zhang. 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: Meng Ji
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