Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5825-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5825-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
19 Oct 2023
Model evaluation paper |
| 19 Oct 2023
| 19 Oct 2023
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Qianqian Han
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, the Netherlands
Yijian Zeng
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, the Netherlands
Lijie Zhang
Institute of Bio and Geosciences: Agrosphere (IBG-3), Research Center Jülich, 52428 Jülich, Germany
Calimanut-Ionut Cira
Departamento de Ingeniería Topográfica y Cartográfica, E.T.S.I. en Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain
Egor Prikaziuk
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, the Netherlands
Ting Duan
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, the Netherlands
Chao Wang
Department of Earth, Marine and Environmental Sciences, University of North Carolina, Chapel Hill, NC 27514, USA
Brigitta Szabó
Institute for Soil Sciences, Centre for Agricultural Research, 1022 Budapest, Hungary
Salvatore Manfreda
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Ruodan Zhuang
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Bob Su
CORRESPONDING AUTHOR
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, the Netherlands
Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of the Ministry of Education, School of Water and Environment, Chang'an University, Xi'an 710054, China
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Cited
7 citations as recorded by crossref.
- Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors A. Hernandez et al. 10.1080/10106049.2025.2461523
- Near-surface soil hydrothermal response feedbacks landslide activity and mechanism X. Ye et al. 10.1016/j.enggeo.2024.107690
- A Transformer-based method to simulate multi-scale soil moisture Y. Liu et al. 10.1016/j.jhydrol.2025.132900
- Assessing Spatial Patterns of Surface Soil Moisture and Vegetation Cover in Batifa, Kurdistan Region-Iraq: Machine Learning Approach I. Khurshed et al. 10.1109/ACCESS.2023.3334635
- Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data M. Zambudio Martínez et al. 10.3390/ai6020041
- A Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools Applied at Different Scales E. Duarte & A. Hernandez 10.3390/app14177677
- Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale Q. Han et al. 10.5194/gmd-16-5825-2023
6 citations as recorded by crossref.
- Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors A. Hernandez et al. 10.1080/10106049.2025.2461523
- Near-surface soil hydrothermal response feedbacks landslide activity and mechanism X. Ye et al. 10.1016/j.enggeo.2024.107690
- A Transformer-based method to simulate multi-scale soil moisture Y. Liu et al. 10.1016/j.jhydrol.2025.132900
- Assessing Spatial Patterns of Surface Soil Moisture and Vegetation Cover in Batifa, Kurdistan Region-Iraq: Machine Learning Approach I. Khurshed et al. 10.1109/ACCESS.2023.3334635
- Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data M. Zambudio Martínez et al. 10.3390/ai6020041
- A Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools Applied at Different Scales E. Duarte & A. Hernandez 10.3390/app14177677
Latest update: 13 Mar 2025
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding...
