Assessment of Machine Learning-Driven Retrievals of Arctic Sea Ice Thickness from L-Band Radiometry Remote Sensing
Special Issue Machine Learning and Statistical Learning with Applications 2025.-- 18 pages, 5 figures, 5 tables.-- The data supporting the conclusions of this article will be made available by the authors on request. These data were derived from the following resources available in the public domain...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/396401 |
| Acceso en línea: | http://hdl.handle.net/10261/396401 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Remote sensing Sea ice Cryosphere |
| Sumario: | Special Issue Machine Learning and Statistical Learning with Applications 2025.-- 18 pages, 5 figures, 5 tables.-- The data supporting the conclusions of this article will be made available by the authors on request. These data were derived from the following resources available in the public domain: The production of SMOS sea ice thickness data was funded by the ESA project SMOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service, and data from 15 October 2010 to 15 April 2021 were obtained from AWI. The BGEP data used in the validation were collected and made available by the Beaufort Gyre Exploration Program based at the Woods Hole Oceanographic Institution (https://www2.whoi.edu/site/beaufortgyre, accessed on 24 July 2025) in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences. Remote sensing data processing was executed at the Barcelona Expert Center on Remote Sensing (BEC-RS, https://bec.icm.csic.es, accessed on 24 July 2025) of the Institut de Ciències del Mar ICM-CSIC. The source codes are available for downloading at the following link: https://github.com/ferranhema/smos-mlsit (accessed on 24 July 2025) |
|---|