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: 3,468 (including HTML, PDF, and XML)
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2,549
822
97
3,468
206
89
118
HTML: 2,549
PDF: 822
XML: 97
Total: 3,468
Supplement: 206
BibTeX: 89
EndNote: 118
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Total article views: 2,212 (including HTML, PDF, and XML)
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1,692
457
63
2,212
121
77
105
HTML: 1,692
PDF: 457
XML: 63
Total: 2,212
Supplement: 121
BibTeX: 77
EndNote: 105
Views and downloads (calculated since 19 Oct 2023)
Cumulative views and downloads
(calculated since 19 Oct 2023)
Total article views: 1,256 (including HTML, PDF, and XML)
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857
365
34
1,256
85
12
13
HTML: 857
PDF: 365
XML: 34
Total: 1,256
Supplement: 85
BibTeX: 12
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: 3,468 (including HTML, PDF, and XML)
Thereof 3,442 with geography defined
and 26 with unknown origin.
Total article views: 2,212 (including HTML, PDF, and XML)
Thereof 2,212 with geography defined
and 0 with unknown origin.
Total article views: 1,256 (including HTML, PDF, and XML)
Thereof 1,230 with geography defined
and 26 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...
