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: 4,347 (including HTML, PDF, and XML)
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3,079
1,151
117
4,347
265
113
159
HTML: 3,079
PDF: 1,151
XML: 117
Total: 4,347
Supplement: 265
BibTeX: 113
EndNote: 159
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Total article views: 2,897 (including HTML, PDF, and XML)
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2,103
711
83
2,897
144
99
143
HTML: 2,103
PDF: 711
XML: 83
Total: 2,897
Supplement: 144
BibTeX: 99
EndNote: 143
Views and downloads (calculated since 19 Oct 2023)
Cumulative views and downloads
(calculated since 19 Oct 2023)
Total article views: 1,450 (including HTML, PDF, and XML)
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976
440
34
1,450
121
14
16
HTML: 976
PDF: 440
XML: 34
Total: 1,450
Supplement: 121
BibTeX: 14
EndNote: 16
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Viewed (geographical distribution)
Total article views: 4,347 (including HTML, PDF, and XML)
Thereof 4,286 with geography defined
and 61 with unknown origin.
Total article views: 2,897 (including HTML, PDF, and XML)
Thereof 2,875 with geography defined
and 22 with unknown origin.
Total article views: 1,450 (including HTML, PDF, and XML)
Thereof 1,411 with geography defined
and 39 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...
