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: 4,067 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,932
1,021
114
4,067
247
109
146
HTML: 2,932
PDF: 1,021
XML: 114
Total: 4,067
Supplement: 247
BibTeX: 109
EndNote: 146
Views and downloads (calculated since 08 Jun 2023)
Cumulative views and downloads
(calculated since 08 Jun 2023)
Total article views: 2,671 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,986
605
80
2,671
136
95
133
HTML: 1,986
PDF: 605
XML: 80
Total: 2,671
Supplement: 136
BibTeX: 95
EndNote: 133
Views and downloads (calculated since 19 Oct 2023)
Cumulative views and downloads
(calculated since 19 Oct 2023)
Total article views: 1,396 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
946
416
34
1,396
111
14
13
HTML: 946
PDF: 416
XML: 34
Total: 1,396
Supplement: 111
BibTeX: 14
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: 4,067 (including HTML, PDF, and XML)
Thereof 4,022 with geography defined
and 45 with unknown origin.
Total article views: 2,671 (including HTML, PDF, and XML)
Thereof 2,659 with geography defined
and 12 with unknown origin.
Total article views: 1,396 (including HTML, PDF, and XML)
Thereof 1,363 with geography defined
and 33 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...
