Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-237-2022
https://doi.org/10.5194/tc-16-237-2022
Research article
 | 
24 Jan 2022
Research article |  | 24 Jan 2022

Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity

Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris

Related authors

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
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023,https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary

Related subject area

Discipline: Sea ice | Subject: Remote Sensing
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
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023,https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Aerial observations of sea ice breakup by ship waves
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023,https://doi.org/10.5194/tc-17-827-2023, 2023
Short summary
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023,https://doi.org/10.5194/tc-17-809-2023, 2023
Short summary
The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023,https://doi.org/10.5194/tc-17-737-2023, 2023
Short summary
Inter-comparison and evaluation of Arctic sea ice type products
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023,https://doi.org/10.5194/tc-17-279-2023, 2023
Short summary

Cited articles

Arntsen, A. E., Song, A. J., Perovich, D. K., and Richter-Menge, J. A.: Observations of the summer breakup of an Arctic sea ice cover, Geophys. Res. Lett., 42, 8057–8063, https://doi.org/10.1002/2015GL065224, 2015. a
Asplin, M. G., Galley, R., Barber, D. G., and Prinsenberg, S.: Fracture of summer perennial sea ice by ocean swell as a result of Arctic storms, J. Geophys. Res.-Oceans, 117, 1–12, https://doi.org/10.1029/2011JC007221, 2012. a
Assmy, P., Fernández-Méndez, M., Duarte, P., Meyer, A., Randelhoff, A., Mundy, C. J., Olsen, L. M., Kauko, H. M., Bailey, A., Chierici, M., Cohen, L., Doulgeris, A. P., Ehn, J. K., Fransson, A., Gerland, S., Hop, H., Hudson, S. R., Hughes, N., Itkin, P., Johnsen, G., King, J. A., Koch, B. P., Koenig, Z., Kwasniewski, S., Laney, S. R., Nicolaus, M., Pavlov, A. K., Polashenski, C. M., Provost, C., Rösel, A., Sandbu, M., Spreen, G., Smedsrud, L. H., Sundfjord, A., Taskjelle, T., Tatarek, A., Wiktor, J., Wagner, P. M., Wold, A., Steen, H., and Granskog, M. A.: Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice, Sci. Rep.-UK, 7, 1–9, https://doi.org/10.1038/srep40850, 2017. a
Barber, D. G., Hanesiak, J. M., and Yackel, J. J.: Sea ice, radarsat-1 and arctic climate processes: A review and update, Can. J. Remote Sens., 27, 51–61, https://doi.org/10.1080/07038992.2001.10854919, 2001. a
Bouillon, S. and Rampal, P.: On producing sea ice deformation data sets from SAR-derived sea ice motion, The Cryosphere, 9, 663–673, https://doi.org/10.5194/tc-9-663-2015, 2015. a, b
Download
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.