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 subweekly 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 Clima...
Published in: | The Cryosphere |
---|---|
Main Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10037/29837 https://doi.org/10.5194/tc-17-1279-2023 |
_version_ | 1829305618469486592 |
---|---|
author | Guo, Wenkai Itkin, Polona Singha, Suman Doulgeris, Anthony Paul Johansson, Malin Spreen, Gunnar |
author_facet | Guo, Wenkai Itkin, Polona Singha, Suman Doulgeris, Anthony Paul Johansson, Malin Spreen, Gunnar |
author_sort | Guo, Wenkai |
collection | University of Tromsø: Munin Open Research Archive |
container_issue | 3 |
container_start_page | 1279 |
container_title | The Cryosphere |
container_volume | 17 |
description | We provide sea ice classification maps of a subweekly 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 |
genre | Arctic Sea ice The Cryosphere |
genre_facet | Arctic Sea ice The Cryosphere |
geographic | Arctic |
geographic_facet | Arctic |
id | ftunivtroemsoe:oai:munin.uit.no:10037/29837 |
institution | Open Polar |
language | English |
op_collection_id | ftunivtroemsoe |
op_container_end_page | 1297 |
op_doi | https://doi.org/10.5194/tc-17-1279-2023 |
op_relation | The Cryosphere FRIDAID 2075799 https://hdl.handle.net/10037/29837 |
op_rights | Attribution 4.0 International (CC BY 4.0) openAccess Copyright 2023 The Author(s) https://creativecommons.org/licenses/by/4.0 |
publishDate | 2023 |
publisher | Copernicus Publications |
record_format | openpolar |
spelling | ftunivtroemsoe:oai:munin.uit.no:10037/29837 2025-04-13T14:15:08+00:00 Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture Guo, Wenkai Itkin, Polona Singha, Suman Doulgeris, Anthony Paul Johansson, Malin Spreen, Gunnar 2023-03-16 https://hdl.handle.net/10037/29837 https://doi.org/10.5194/tc-17-1279-2023 eng eng Copernicus Publications The Cryosphere FRIDAID 2075799 https://hdl.handle.net/10037/29837 Attribution 4.0 International (CC BY 4.0) openAccess Copyright 2023 The Author(s) https://creativecommons.org/licenses/by/4.0 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2023 ftunivtroemsoe https://doi.org/10.5194/tc-17-1279-2023 2025-03-14T05:17:57Z We provide sea ice classification maps of a subweekly 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 University of Tromsø: Munin Open Research Archive Arctic The Cryosphere 17 3 1279 1297 |
spellingShingle | Guo, Wenkai Itkin, Polona Singha, Suman Doulgeris, Anthony Paul Johansson, Malin Spreen, Gunnar Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture |
title | 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_short | 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 |
url | https://hdl.handle.net/10037/29837 https://doi.org/10.5194/tc-17-1279-2023 |