Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Clim...
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Copernicus Publications
2023
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Online Access: | https://doi.org/10.5194/tc-17-1279-2023 https://doaj.org/article/e2a9adbf5fb548b196c3feca1c700044 |
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ftdoajarticles:oai:doaj.org/article:e2a9adbf5fb548b196c3feca1c700044 2023-05-15T15:14:18+02:00 Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture W. Guo P. Itkin S. Singha A. P. Doulgeris M. Johansson G. Spreen 2023-03-01T00:00:00Z https://doi.org/10.5194/tc-17-1279-2023 https://doaj.org/article/e2a9adbf5fb548b196c3feca1c700044 EN eng Copernicus Publications https://tc.copernicus.org/articles/17/1279/2023/tc-17-1279-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-1279-2023 1994-0416 1994-0424 https://doaj.org/article/e2a9adbf5fb548b196c3feca1c700044 The Cryosphere, Vol 17, Pp 1279-1297 (2023) Environmental sciences GE1-350 Geology QE1-996.5 article 2023 ftdoajarticles https://doi.org/10.5194/tc-17-1279-2023 2023-03-19T01:30:23Z We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar ... Article in Journal/Newspaper Arctic Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic The Cryosphere 17 3 1279 1297 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 W. Guo P. Itkin S. Singha A. P. Doulgeris M. Johansson G. Spreen Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar ... |
format |
Article in Journal/Newspaper |
author |
W. Guo P. Itkin S. Singha A. P. Doulgeris M. Johansson G. Spreen |
author_facet |
W. Guo P. Itkin S. Singha A. P. Doulgeris M. Johansson G. Spreen |
author_sort |
W. Guo |
title |
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
title_short |
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
title_full |
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
title_fullStr |
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
title_full_unstemmed |
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
title_sort |
sea ice classification of terrasar-x scansar images for the mosaic expedition incorporating per-class incidence angle dependency of image texture |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-1279-2023 https://doaj.org/article/e2a9adbf5fb548b196c3feca1c700044 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice The Cryosphere |
genre_facet |
Arctic Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 17, Pp 1279-1297 (2023) |
op_relation |
https://tc.copernicus.org/articles/17/1279/2023/tc-17-1279-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-1279-2023 1994-0416 1994-0424 https://doaj.org/article/e2a9adbf5fb548b196c3feca1c700044 |
op_doi |
https://doi.org/10.5194/tc-17-1279-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
3 |
container_start_page |
1279 |
op_container_end_page |
1297 |
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1766344769546485760 |