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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ftdoajarticles:oai:doaj.org/article:e906bea4381b4551b82f3b7de29845a5 2023-05-15T14:54:29+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 Hyangsun Han Jungho Im Miae Kim Seongmun Sim Jinwoo Kim Duk-jin Kim Sung-Ho Kang 2016-01-01T00:00:00Z https://doi.org/10.3390/rs8010057 https://doaj.org/article/e906bea4381b4551b82f3b7de29845a5 EN eng MDPI AG http://www.mdpi.com/2072-4292/8/1/57 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8010057 https://doaj.org/article/e906bea4381b4551b82f3b7de29845a5 Remote Sensing, Vol 8, Iss 1, p 57 (2016) Arctic Chukchi Sea melt ponds multiyear sea ice melt pond fraction polarimetric SAR machine learning TerraSAR-X Science Q article 2016 ftdoajarticles https://doi.org/10.3390/rs8010057 2022-12-31T16:29:58Z 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 approaches—decision 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 × 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 Directory of Open Access Journals: DOAJ Articles Arctic Chukchi Sea Remote Sensing 8 1 57 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic Chukchi Sea melt ponds multiyear sea ice melt pond fraction polarimetric SAR machine learning TerraSAR-X Science Q |
spellingShingle |
Arctic Chukchi Sea melt ponds multiyear sea ice melt pond fraction polarimetric SAR machine learning TerraSAR-X Science Q Hyangsun Han Jungho Im Miae Kim Seongmun Sim Jinwoo Kim Duk-jin Kim Sung-Ho Kang 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 |
topic_facet |
Arctic Chukchi Sea melt ponds multiyear sea ice melt pond fraction polarimetric SAR machine learning TerraSAR-X Science Q |
description |
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 approaches—decision 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 × 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 |
Hyangsun Han Jungho Im Miae Kim Seongmun Sim Jinwoo Kim Duk-jin Kim Sung-Ho Kang |
author_facet |
Hyangsun Han Jungho Im Miae Kim Seongmun Sim Jinwoo Kim Duk-jin Kim Sung-Ho Kang |
author_sort |
Hyangsun Han |
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://doi.org/10.3390/rs8010057 https://doaj.org/article/e906bea4381b4551b82f3b7de29845a5 |
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_source |
Remote Sensing, Vol 8, Iss 1, p 57 (2016) |
op_relation |
http://www.mdpi.com/2072-4292/8/1/57 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8010057 https://doaj.org/article/e906bea4381b4551b82f3b7de29845a5 |
op_doi |
https://doi.org/10.3390/rs8010057 |
container_title |
Remote Sensing |
container_volume |
8 |
container_issue |
1 |
container_start_page |
57 |
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1766326206937956352 |