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: 4,283 (including HTML, PDF, and XML)
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3,049
1,117
117
4,283
261
112
155
HTML: 3,049
PDF: 1,117
XML: 117
Total: 4,283
Supplement: 261
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EndNote: 155
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Total article views: 2,842 (including HTML, PDF, and XML)
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2,081
678
83
2,842
141
98
140
HTML: 2,081
PDF: 678
XML: 83
Total: 2,842
Supplement: 141
BibTeX: 98
EndNote: 140
Views and downloads (calculated since 19 Oct 2023)
Cumulative views and downloads
(calculated since 19 Oct 2023)
Total article views: 1,441 (including HTML, PDF, and XML)
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968
439
34
1,441
120
14
15
HTML: 968
PDF: 439
XML: 34
Total: 1,441
Supplement: 120
BibTeX: 14
EndNote: 15
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Viewed (geographical distribution)
Total article views: 4,283 (including HTML, PDF, and XML)
Thereof 4,246 with geography defined
and 37 with unknown origin.
Total article views: 2,842 (including HTML, PDF, and XML)
Thereof 2,839 with geography defined
and 3 with unknown origin.
Total article views: 1,441 (including HTML, PDF, and XML)
Thereof 1,407 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...
