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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Online Access: | http://hdl.handle.net/2117/357162 https://doi.org/10.1029/2020GL091285 |
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ftupcatalunyair:oai:upcommons.upc.edu:2117/357162 2023-05-15T14:27:09+02:00 Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data Herbert, Christoph Josef Camps Carmona, Adriano José Wellmann, Florian Vall-Llossera Ferran, Mercedes Magdalena Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció 2021-03-28 10 p. application/pdf http://hdl.handle.net/2117/357162 https://doi.org/10.1029/2020GL091285 eng eng https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091285 info:eu-repo/grantAgreement/MINECO/1PE/MDM-2016-0600 info:eu-repo/grantAgreement/MINECO/FEDER/ESP2017-89463-C3-2-R info:eu-repo/grantAgreement/MINECO/FEDER/RTI2018-099880-B-C21 Herbert, C. [et al.]. Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data. "Geophysical research letters", 28 Març 2021, vol. 48, núm. 6, p. 1-10. 0094-8276 http://hdl.handle.net/2117/357162 doi:10.1029/2020GL091285 Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ Open Access CC-BY Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Climatic changes Global warming Microwave radiometry Sea ice thickness Aprenentatge automàtic Canvis climàtics Escalfament global Article 2021 ftupcatalunyair https://doi.org/10.1029/2020GL091285 2022-08-02T23:05:25Z 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. 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. Peer Reviewed Postprint (published version) Article in Journal/Newspaper Arctic Arctic Global warming Sea ice Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge Arctic Geophysical Research Letters 48 6 |
institution |
Open Polar |
collection |
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
op_collection_id |
ftupcatalunyair |
language |
English |
topic |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Climatic changes Global warming Microwave radiometry Sea ice thickness Aprenentatge automàtic Canvis climàtics Escalfament global |
spellingShingle |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Climatic changes Global warming Microwave radiometry Sea ice thickness Aprenentatge automàtic Canvis climàtics Escalfament global Herbert, Christoph Josef Camps Carmona, Adriano José Wellmann, Florian Vall-Llossera Ferran, Mercedes Magdalena Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data |
topic_facet |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Climatic changes Global warming Microwave radiometry Sea ice thickness Aprenentatge automàtic Canvis climàtics Escalfament global |
description |
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. 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. Peer Reviewed Postprint (published version) |
author2 |
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció |
format |
Article in Journal/Newspaper |
author |
Herbert, Christoph Josef Camps Carmona, Adriano José Wellmann, Florian Vall-Llossera Ferran, Mercedes Magdalena |
author_facet |
Herbert, Christoph Josef Camps Carmona, Adriano José Wellmann, Florian Vall-Llossera Ferran, Mercedes Magdalena |
author_sort |
Herbert, Christoph Josef |
title |
Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data |
title_short |
Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data |
title_full |
Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data |
title_fullStr |
Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data |
title_full_unstemmed |
Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data |
title_sort |
bayesian unsupervised machine learning approach to segment arctic sea ice using smos data |
publishDate |
2021 |
url |
http://hdl.handle.net/2117/357162 https://doi.org/10.1029/2020GL091285 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Global warming Sea ice |
genre_facet |
Arctic Arctic Global warming Sea ice |
op_relation |
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091285 info:eu-repo/grantAgreement/MINECO/1PE/MDM-2016-0600 info:eu-repo/grantAgreement/MINECO/FEDER/ESP2017-89463-C3-2-R info:eu-repo/grantAgreement/MINECO/FEDER/RTI2018-099880-B-C21 Herbert, C. [et al.]. Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data. "Geophysical research letters", 28 Març 2021, vol. 48, núm. 6, p. 1-10. 0094-8276 http://hdl.handle.net/2117/357162 doi:10.1029/2020GL091285 |
op_rights |
Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ Open Access |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1029/2020GL091285 |
container_title |
Geophysical Research Letters |
container_volume |
48 |
container_issue |
6 |
_version_ |
1766300765334274048 |