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
Viewed
Total article views: 4,494 (including HTML, PDF, and XML)
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3,190
1,183
121
4,494
276
115
170
HTML: 3,190
PDF: 1,183
XML: 121
Total: 4,494
Supplement: 276
BibTeX: 115
EndNote: 170
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Total article views: 3,007 (including HTML, PDF, and XML)
HTML
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Total
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EndNote
2,185
735
87
3,007
151
101
149
HTML: 2,185
PDF: 735
XML: 87
Total: 3,007
Supplement: 151
BibTeX: 101
EndNote: 149
Views and downloads (calculated since 19 Oct 2023)
Cumulative views and downloads
(calculated since 19 Oct 2023)
Total article views: 1,487 (including HTML, PDF, and XML)
HTML
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1,005
448
34
1,487
125
14
21
HTML: 1,005
PDF: 448
XML: 34
Total: 1,487
Supplement: 125
BibTeX: 14
EndNote: 21
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Viewed (geographical distribution)
Total article views: 4,494 (including HTML, PDF, and XML)
Thereof 4,457 with geography defined
and 37 with unknown origin.
Total article views: 3,007 (including HTML, PDF, and XML)
Thereof 3,004 with geography defined
and 3 with unknown origin.
Total article views: 1,487 (including HTML, PDF, and XML)
Thereof 1,453 with geography defined
and 34 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...
