Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa
An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here,...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doi.org/10.3390/rs13163183 https://doaj.org/article/60d973d4f66249e2a19b4774464914b5 |
id |
ftdoajarticles:oai:doaj.org/article:60d973d4f66249e2a19b4774464914b5 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:60d973d4f66249e2a19b4774464914b5 2023-05-15T15:00:32+02:00 Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa Renée Mie Fredensborg Hansen Eero Rinne Henriette Skourup 2021-08-01T00:00:00Z https://doi.org/10.3390/rs13163183 https://doaj.org/article/60d973d4f66249e2a19b4774464914b5 EN eng MDPI AG https://www.mdpi.com/2072-4292/13/16/3183 https://doaj.org/toc/2072-4292 doi:10.3390/rs13163183 2072-4292 https://doaj.org/article/60d973d4f66249e2a19b4774464914b5 Remote Sensing, Vol 13, Iss 3183, p 3183 (2021) SARAL/AltiKa radar echoes classification MYI FYI radar altimetry Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13163183 2022-12-31T16:18:45Z An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k -nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027. Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 13 16 3183 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
SARAL/AltiKa radar echoes classification MYI FYI radar altimetry Science Q |
spellingShingle |
SARAL/AltiKa radar echoes classification MYI FYI radar altimetry Science Q Renée Mie Fredensborg Hansen Eero Rinne Henriette Skourup Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa |
topic_facet |
SARAL/AltiKa radar echoes classification MYI FYI radar altimetry Science Q |
description |
An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k -nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027. |
format |
Article in Journal/Newspaper |
author |
Renée Mie Fredensborg Hansen Eero Rinne Henriette Skourup |
author_facet |
Renée Mie Fredensborg Hansen Eero Rinne Henriette Skourup |
author_sort |
Renée Mie Fredensborg Hansen |
title |
Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa |
title_short |
Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa |
title_full |
Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa |
title_fullStr |
Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa |
title_full_unstemmed |
Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa |
title_sort |
classification of sea ice types in the arctic by radar echoes from saral/altika |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13163183 https://doaj.org/article/60d973d4f66249e2a19b4774464914b5 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing, Vol 13, Iss 3183, p 3183 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/16/3183 https://doaj.org/toc/2072-4292 doi:10.3390/rs13163183 2072-4292 https://doaj.org/article/60d973d4f66249e2a19b4774464914b5 |
op_doi |
https://doi.org/10.3390/rs13163183 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
16 |
container_start_page |
3183 |
_version_ |
1766332630683353088 |