Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data
Microwave radiometry at L-band is sensitive to sea ice thickness (SIT) up to ∼ 60 cm. Current methods to infer SIT depend on ice-physical properties and data provided by the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. However, retrieval accuracy is limited due to seasonally and regionally...
| Published in: | Geophysical Research Letters |
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| Main Authors: | , , , |
| Other Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
American Geophysical Union
2021
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10261/252549 https://doi.org/10.1029/2020GL091285 https://doi.org/10.13039/501100000780 |
| Summary: | Microwave radiometry at L-band is sensitive to sea ice thickness (SIT) up to ∼ 60 cm. Current methods to infer SIT depend on ice-physical properties and data provided by the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. However, retrieval accuracy is limited due to seasonally and regionally variable surface conditions during the formation and melting of sea ice. In this work, Arctic sea ice is segmented using a Bayesian unsupervised learning algorithm aiming to recognize spatial patterns by harnessing multi-incidence angle brightness temperature observations. The approach considers both statistical characteristics and spatial correlations of the observations. The temporal stability and separability of classes are analyzed to distinguish ambiguous from well-determined regions. Model uncertainty is quantified from class membership probabilities using information entropy. The presented approach opens up a new scope to improve current SIT retrieval algorithms, and can be particularly beneficial to investigate merged satellite products. There are no perceived conicts of inter-est for the lead author or coauthors. The lead author received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660050. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. It was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033 |
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