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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/16/3183 |
| _version_ | 1797522148970987520 |
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| 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 |
| collection | DOAJ |
| 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 <i>k</i>-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. |
| first_indexed | 2024-03-10T08:25:22Z |
| format | Article |
| id | doaj.art-60d973d4f66249e2a19b4774464914b5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:25:22Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-60d973d4f66249e2a19b4774464914b52023-11-22T09:33:24ZengMDPI AGRemote Sensing2072-42922021-08-011316318310.3390/rs13163183Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKaRenée Mie Fredensborg Hansen0Eero Rinne1Henriette Skourup2Marine Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, FinlandMarine Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, FinlandGeodesy and Earth Observation, DTU Space, Elektrovej, Building 327, 2800 Kgs. Lyngby, DenmarkAn 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 <i>k</i>-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.https://www.mdpi.com/2072-4292/13/16/3183SARAL/AltiKaradar echoesclassificationMYIFYIradar altimetry |
| spellingShingle | Renée Mie Fredensborg Hansen Eero Rinne Henriette Skourup Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa Remote Sensing SARAL/AltiKa radar echoes classification MYI FYI radar altimetry |
| title | 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_short | 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 |
| topic | SARAL/AltiKa radar echoes classification MYI FYI radar altimetry |
| url | https://www.mdpi.com/2072-4292/13/16/3183 |
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