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,...
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ftmdpi:oai:mdpi.com:/2072-4292/13/16/3183/ 2023-08-20T04:04:26+02:00 Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa Renée Mie Fredensborg Hansen Eero Rinne Henriette Skourup agris 2021-08-11 application/pdf https://doi.org/10.3390/rs13163183 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13163183 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 16; Pages: 3183 SARAL/AltiKa radar echoes classification MYI FYI radar altimetry sea ice types Text 2021 ftmdpi https://doi.org/10.3390/rs13163183 2023-08-01T02:24:54Z 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. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 13 16 3183 |
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Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
SARAL/AltiKa radar echoes classification MYI FYI radar altimetry sea ice types |
spellingShingle |
SARAL/AltiKa radar echoes classification MYI FYI radar altimetry sea ice types 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 sea ice types |
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 |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13163183 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 13; Issue 16; Pages: 3183 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13163183 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13163183 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
16 |
container_start_page |
3183 |
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1774714808359190528 |