Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data
Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate i...
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ftuisanist:oai:scholarworks.unist.ac.kr:201301/18831 2023-05-15T14:58:09+02:00 Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data Han, Hyangsun Kim, Miae Sim, Seongmun Kim, Jinwoo Kim, Duk-jin Kang, Sung-Ho Im, Jungho 2016-01 https://scholarworks.unist.ac.kr/handle/201301/18831 https://doi.org/10.3390/rs8010057 http://www.mdpi.com/2072-4292/8/1/57 ?????? unknown MDPI AG REMOTE SENSING, v.8, no.1, pp.1 - 23 2072-4292 https://scholarworks.unist.ac.kr/handle/201301/18831 1204 25612 2-s2.0-84957873084 000369494500036 doi:10.3390/rs8010057 http://www.mdpi.com/2072-4292/8/1/57 ARTICLE ART 2016 ftuisanist https://doi.org/10.3390/rs8010057 2022-05-15T05:26:16Z Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the ... Article in Journal/Newspaper Arctic Chukchi Chukchi Sea Climate change Sea ice ScholarWorks@UNIST (Ulsan National Institute of Science and Technology) Arctic Chukchi Sea Remote Sensing 8 1 57 |
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Open Polar |
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ScholarWorks@UNIST (Ulsan National Institute of Science and Technology) |
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Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the ... |
format |
Article in Journal/Newspaper |
author |
Han, Hyangsun Kim, Miae Sim, Seongmun Kim, Jinwoo Kim, Duk-jin Kang, Sung-Ho Im, Jungho |
spellingShingle |
Han, Hyangsun Kim, Miae Sim, Seongmun Kim, Jinwoo Kim, Duk-jin Kang, Sung-Ho Im, Jungho Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data |
author_facet |
Han, Hyangsun Kim, Miae Sim, Seongmun Kim, Jinwoo Kim, Duk-jin Kang, Sung-Ho Im, Jungho |
author_sort |
Han, Hyangsun |
title |
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data |
title_short |
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data |
title_full |
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data |
title_fullStr |
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data |
title_full_unstemmed |
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data |
title_sort |
retrieval of melt ponds on arctic multiyear sea ice in summer from terrasar-x dual-polarization data using machine learning approaches: a case study in the chukchi sea with mid-incidence angle data |
publisher |
MDPI AG |
publishDate |
2016 |
url |
https://scholarworks.unist.ac.kr/handle/201301/18831 https://doi.org/10.3390/rs8010057 http://www.mdpi.com/2072-4292/8/1/57 |
geographic |
Arctic Chukchi Sea |
geographic_facet |
Arctic Chukchi Sea |
genre |
Arctic Chukchi Chukchi Sea Climate change Sea ice |
genre_facet |
Arctic Chukchi Chukchi Sea Climate change Sea ice |
op_relation |
REMOTE SENSING, v.8, no.1, pp.1 - 23 2072-4292 https://scholarworks.unist.ac.kr/handle/201301/18831 1204 25612 2-s2.0-84957873084 000369494500036 doi:10.3390/rs8010057 http://www.mdpi.com/2072-4292/8/1/57 |
op_doi |
https://doi.org/10.3390/rs8010057 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
1 |
container_start_page |
57 |
_version_ |
1766330250574168064 |