dc.contributor.author | Guo, Wenkai | |
dc.contributor.author | Itkin, Polona | |
dc.contributor.author | Singha, Suman | |
dc.contributor.author | Doulgeris, Anthony Paul | |
dc.contributor.author | Johansson, Malin | |
dc.contributor.author | Spreen, Gunnar | |
dc.date.accessioned | 2023-08-10T09:05:20Z | |
dc.date.available | 2023-08-10T09:05:20Z | |
dc.date.issued | 2023-03-16 | |
dc.description.abstract | 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 (SAR) and optical data in
other MOSAiC studies. | en_US |
dc.identifier.citation | Guo, Itkin, Singha, Doulgeris, Johansson, Spreen. Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture. The Cryosphere. 2023;17(3):1279-1297 | en_US |
dc.identifier.cristinID | FRIDAID 2075799 | |
dc.identifier.doi | 10.5194/tc-17-1279-2023 | |
dc.identifier.issn | 1994-0416 | |
dc.identifier.issn | 1994-0424 | |
dc.identifier.uri | https://hdl.handle.net/10037/29837 | |
dc.language.iso | eng | en_US |
dc.publisher | Copernicus Publications | en_US |
dc.relation.journal | The Cryosphere | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | Attribution 4.0 International (CC BY 4.0) | en_US |
dc.title | Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |