Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 20
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.
https://doi.org/10.5194/gmd-16-5825-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 20
Model evaluation paper
 | 
19 Oct 2023
Model evaluation paper |  | 19 Oct 2023

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su

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Latest update: 29 Jun 2024
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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.
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