Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods
In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, includ...
Published in: | Marine Geodesy |
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Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2022
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Subjects: | |
Online Access: | http://resolver.tudelft.nl/uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa https://doi.org/10.1080/01490419.2022.2089412 |
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author | Bij de Vaate, I. (author) Martin, Ericka (author) Slobbe, D.C. (author) Naeije, M.C. (author) Verlaan, M. (author) |
author_facet | Bij de Vaate, I. (author) Martin, Ericka (author) Slobbe, D.C. (author) Naeije, M.C. (author) Verlaan, M. (author) |
author_sort | Bij de Vaate, I. (author) |
collection | Delft University of Technology: Institutional Repository |
container_start_page | 1 |
container_title | Marine Geodesy |
description | In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean. Physical and Space Geodesy Astrodynamics & Space Missions Mathematical Physics |
format | Article in Journal/Newspaper |
genre | Arctic Arctic Ocean Sea ice |
genre_facet | Arctic Arctic Ocean Sea ice |
geographic | Arctic Arctic Ocean |
geographic_facet | Arctic Arctic Ocean |
id | fttudelft:oai:tudelft.nl:uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa |
institution | Open Polar |
language | English |
op_collection_id | fttudelft |
op_container_end_page | 34 |
op_doi | https://doi.org/10.1080/01490419.2022.2089412 |
op_relation | http://www.scopus.com/inward/record.url?scp=85133721554&partnerID=8YFLogxK Marine Geodesy: an international journal of ocean surveys, mapping and sensing--0149-0419--1ecf91d1-a328-4e46-b073-07cdd79948c0 http://resolver.tudelft.nl/uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa https://doi.org/10.1080/01490419.2022.2089412 |
op_rights | © 2022 I. Bij de Vaate, Ericka Martin, D.C. Slobbe, M.C. Naeije, M. Verlaan |
publishDate | 2022 |
record_format | openpolar |
spelling | fttudelft:oai:tudelft.nl:uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa 2025-01-16T20:23:57+00:00 Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods Bij de Vaate, I. (author) Martin, Ericka (author) Slobbe, D.C. (author) Naeije, M.C. (author) Verlaan, M. (author) 2022 http://resolver.tudelft.nl/uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa https://doi.org/10.1080/01490419.2022.2089412 en eng http://www.scopus.com/inward/record.url?scp=85133721554&partnerID=8YFLogxK Marine Geodesy: an international journal of ocean surveys, mapping and sensing--0149-0419--1ecf91d1-a328-4e46-b073-07cdd79948c0 http://resolver.tudelft.nl/uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa https://doi.org/10.1080/01490419.2022.2089412 © 2022 I. Bij de Vaate, Ericka Martin, D.C. Slobbe, M.C. Naeije, M. Verlaan Arctic Ocean classification lead detection machine learning Sentinel-3 synthetic aperture radar journal article 2022 fttudelft https://doi.org/10.1080/01490419.2022.2089412 2024-01-24T23:33:10Z In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean. Physical and Space Geodesy Astrodynamics & Space Missions Mathematical Physics Article in Journal/Newspaper Arctic Arctic Ocean Sea ice Delft University of Technology: Institutional Repository Arctic Arctic Ocean Marine Geodesy 1 34 |
spellingShingle | Arctic Ocean classification lead detection machine learning Sentinel-3 synthetic aperture radar Bij de Vaate, I. (author) Martin, Ericka (author) Slobbe, D.C. (author) Naeije, M.C. (author) Verlaan, M. (author) Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods |
title | Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods |
title_full | Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods |
title_fullStr | Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods |
title_full_unstemmed | Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods |
title_short | Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods |
title_sort | lead detection in the arctic ocean from sentinel-3 satellite data: a comprehensive assessment of thresholding and machine learning classification methods |
topic | Arctic Ocean classification lead detection machine learning Sentinel-3 synthetic aperture radar |
topic_facet | Arctic Ocean classification lead detection machine learning Sentinel-3 synthetic aperture radar |
url | http://resolver.tudelft.nl/uuid:0f1ff6db-a877-4466-9d1e-0f2ca3fbf0aa https://doi.org/10.1080/01490419.2022.2089412 |