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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2021
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Online Access: | https://orbit.dtu.dk/en/publications/ae7dfe5f-0014-40f6-bee1-cd1828ca6875 https://doi.org/10.3390/rs13163183 https://backend.orbit.dtu.dk/ws/files/257670598/remotesensing_13_03183_v2.pdf |
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ftdtupubl:oai:pure.atira.dk:publications/ae7dfe5f-0014-40f6-bee1-cd1828ca6875 2024-06-23T07:48:27+00:00 Classification of sea ice types in the arctic by radar echoes from saral/altika Hansen, Renée Mie Fredensborg Rinne, Eero Skourup, Henriette 2021 application/pdf https://orbit.dtu.dk/en/publications/ae7dfe5f-0014-40f6-bee1-cd1828ca6875 https://doi.org/10.3390/rs13163183 https://backend.orbit.dtu.dk/ws/files/257670598/remotesensing_13_03183_v2.pdf eng eng https://orbit.dtu.dk/en/publications/ae7dfe5f-0014-40f6-bee1-cd1828ca6875 info:eu-repo/semantics/openAccess Hansen , R M F , Rinne , E & Skourup , H 2021 , ' Classification of sea ice types in the arctic by radar echoes from saral/altika ' , Remote Sensing , vol. 13 , no. 16 , 3183 . https://doi.org/10.3390/rs13163183 Classification FYI MYI Radar altimetry Radar echoes SARAL/AltiKa Sea ice types article 2021 ftdtupubl https://doi.org/10.3390/rs13163183 2024-06-11T15:05:08Z 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 Arctic Sea ice Technical University of Denmark: DTU Orbit Arctic Remote Sensing 13 16 3183 |
institution |
Open Polar |
collection |
Technical University of Denmark: DTU Orbit |
op_collection_id |
ftdtupubl |
language |
English |
topic |
Classification FYI MYI Radar altimetry Radar echoes SARAL/AltiKa Sea ice types |
spellingShingle |
Classification FYI MYI Radar altimetry Radar echoes SARAL/AltiKa Sea ice types Hansen, Renée Mie Fredensborg Rinne, Eero Skourup, Henriette Classification of sea ice types in the arctic by radar echoes from saral/altika |
topic_facet |
Classification FYI MYI Radar altimetry Radar echoes SARAL/AltiKa 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 |
Article in Journal/Newspaper |
author |
Hansen, Renée Mie Fredensborg Rinne, Eero Skourup, Henriette |
author_facet |
Hansen, Renée Mie Fredensborg Rinne, Eero Skourup, Henriette |
author_sort |
Hansen, Renée Mie Fredensborg |
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 |
publishDate |
2021 |
url |
https://orbit.dtu.dk/en/publications/ae7dfe5f-0014-40f6-bee1-cd1828ca6875 https://doi.org/10.3390/rs13163183 https://backend.orbit.dtu.dk/ws/files/257670598/remotesensing_13_03183_v2.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_source |
Hansen , R M F , Rinne , E & Skourup , H 2021 , ' Classification of sea ice types in the arctic by radar echoes from saral/altika ' , Remote Sensing , vol. 13 , no. 16 , 3183 . https://doi.org/10.3390/rs13163183 |
op_relation |
https://orbit.dtu.dk/en/publications/ae7dfe5f-0014-40f6-bee1-cd1828ca6875 |
op_rights |
info:eu-repo/semantics/openAccess |
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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1802638870337028096 |