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,...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Renée Mie Fredensborg Hansen, Eero Rinne, Henriette Skourup
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Subjects:
MYI
FYI
Online Access:https://doi.org/10.3390/rs13163183
id ftmdpi:oai:mdpi.com:/2072-4292/13/16/3183/
record_format openpolar
spelling 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
institution Open Polar
collection 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
_version_ 1774714808359190528