Articles | Volume 17, issue 3
Research article
16 Mar 2023
Research article |  | 16 Mar 2023

Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture

Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen

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Cited articles

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Barber, D. G. and LeDrew, E. F.: SAR Sea Ice Discrimination Using Texture Statistics: A Multivariate Approach Photogrammetric Engineering and Remote Sensing, Photogramm. Eng. Remote Sens., 57, 385–395, 1991. a, b
Barber, D. G., Ehn, J. K., Pućko, M., Rysgaard, S., Deming, J. W., Bowman, J. S., Papakyriakou, T., Galley, R. J., and Søgaard, D. H.: Frost flowers on young Arctic sea ice: The climatic, chemical, and microbial significance of an emerging ice type, J. Geophys. Res.-Atmos., 119, 11593–11612,, 2014. a, b
Bliss, A., Hutchings, J., Anderson, P., Anhaus, P., and Jakob Belter, H.: Sea ice drift tracks from the Distributed Network of autonomous buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition 2019–2021, Arctic Data Center [data set],, 2021. a
Bogdanov, A. V., Sandven, S., Johannessen, O. M., Alexandrov, V. Y., and Bobylev, L. P.: Multi-sensor approach to automated classification of sea ice image data, Image Processing for Remote Sensing, 43, 293–324,, 2007. a
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.