A Machine Learning Approach on SMOS Thin Sea Ice Thickness Retrieval
7 pages, 5 figures, 1 table.-- The data were collected and made available by the Beaufort Gyre Exploration Program based at the Woods Hole Oceanographic Institution (https://www2.whoi.edu/site/beaufortgyre/) in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/365691 |
| Acesso em linha: | http://hdl.handle.net/10261/365691 |
| Access Level: | acceso abierto |
| Palavra-chave: | Gradient Boosting (GB) Machine learning Random forest (RF) Sea ice thickness Soil Moisture and Ocean Salinity (SMOS) satellite |
| Resumo: | 7 pages, 5 figures, 1 table.-- The data were collected and made available by the Beaufort Gyre Exploration Program based at the Woods Hole Oceanographic Institution (https://www2.whoi.edu/site/beaufortgyre/) in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences |
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