Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5825-2023
https://doi.org/10.5194/gmd-16-5825-2023
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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Cited articles

Abowarda, A. S., Bai, L., Zhang, C., Long, D., Li, X., Huang, Q., and Sun, Z.: Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale, Remote Sens. Environ., 255, 112301, https://doi.org/10.1016/j.rse.2021.112301, 2021. 
Acharya, U., Daigh, A. L., and Oduor, P. G.: Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North, Soil Systems, 5, 57, https://doi.org/10.3390/soilsystems5040057, 2021. 
Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., and Ghalhari, G. A. F.: Machine learning to estimate surface soil moisture from remote sensing data, Water, 12, 3223, https://doi.org/10.3390/w12113223, 2020. 
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. 
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. 
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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.