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
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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Total article views: 3,820 (including HTML, PDF, and XML)
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2,764
950
106
3,820
231
97
137
HTML: 2,764
PDF: 950
XML: 106
Total: 3,820
Supplement: 231
BibTeX: 97
EndNote: 137
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Total article views: 2,467 (including HTML, PDF, and XML)
HTML
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1,854
541
72
2,467
131
84
124
HTML: 1,854
PDF: 541
XML: 72
Total: 2,467
Supplement: 131
BibTeX: 84
EndNote: 124
Views and downloads (calculated since 19 Oct 2023)
Cumulative views and downloads
(calculated since 19 Oct 2023)
Total article views: 1,353 (including HTML, PDF, and XML)
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910
409
34
1,353
100
13
13
HTML: 910
PDF: 409
XML: 34
Total: 1,353
Supplement: 100
BibTeX: 13
EndNote: 13
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Viewed (geographical distribution)
Total article views: 3,820 (including HTML, PDF, and XML)
Thereof 3,783 with geography defined
and 37 with unknown origin.
Total article views: 2,467 (including HTML, PDF, and XML)
Thereof 2,459 with geography defined
and 8 with unknown origin.
Total article views: 1,353 (including HTML, PDF, and XML)
Thereof 1,324 with geography defined
and 29 with unknown origin.
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...
