Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-237-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-237-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jan 2022
Research article |
| 24 Jan 2022
| 24 Jan 2022
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Department of Physics and Technology, UiT The Arctic University of
Norway, Tromsø, Norway
Polona Itkin
Department of Physics and Technology, UiT The Arctic University of
Norway, Tromsø, Norway
Johannes Lohse
Department of Physics and Technology, UiT The Arctic University of
Norway, Tromsø, Norway
Malin Johansson
Department of Physics and Technology, UiT The Arctic University of
Norway, Tromsø, Norway
Anthony Paul Doulgeris
Department of Physics and Technology, UiT The Arctic University of
Norway, Tromsø, Norway
Related authors
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
EGUsphere, https://doi.org/10.5194/egusphere-2025-1069, https://doi.org/10.5194/egusphere-2025-1069, 2025
Short summary
Short summary
In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
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
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.
Zuzanna M. Swirad, A. Malin Johansson, and Eirik Malnes
EGUsphere, https://doi.org/10.5194/egusphere-2025-3859, https://doi.org/10.5194/egusphere-2025-3859, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Drift, landfast and glacier ice are present in fjords and it is important to map them separately. We developed a method to split fjord ice into different types based on ice location, persistence in time and size. We used this method for Hornsund fjord, home to the Polish Polar Station, for an 11.5-year period. We observed that most of the ice is drift ice. The maps produced by this study can be used to look at water circulation, coastal erosion and habitat conditions.
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
EGUsphere, https://doi.org/10.5194/egusphere-2025-1069, https://doi.org/10.5194/egusphere-2025-1069, 2025
Short summary
Short summary
In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
Polona Itkin
The Cryosphere, 19, 1135–1151, https://doi.org/10.5194/tc-19-1135-2025, https://doi.org/10.5194/tc-19-1135-2025, 2025
Short summary
Short summary
Radar satellite images of sea ice were analyzed to understand how sea ice moves and deforms. These data are noisy, especially when looking at small details. A method was developed to filter out the noise. The filtered data were used to monitor how ice plates stretch and compress over time, revealing slow healing of ice fractures. Cohesive clusters of ice plates that move together were studied too. These methods provide climate-relevant insights into the dynamic nature of winter sea ice cover.
Polona Itkin and Glen E. Liston
EGUsphere, https://doi.org/10.5194/egusphere-2024-3402, https://doi.org/10.5194/egusphere-2024-3402, 2024
Short summary
Short summary
The MOSAiC project provided a year of observations of Arctic snow and sea ice, though some data were interrupted, especially during summer melt onset. We developed a data-model fusion system to produce continuous, high-resolution time series of snow and sea ice parameters. On all three analyzed three ice types snow redistribution correlated with sea ice deformation and level ice thickness was governed by the thinnest fraction of snow cover.
Zuzanna M. Swirad, A. Malin Johansson, and Eirik Malnes
The Cryosphere, 18, 895–910, https://doi.org/10.5194/tc-18-895-2024, https://doi.org/10.5194/tc-18-895-2024, 2024
Short summary
Short summary
We used satellite images to create sea ice maps of Hornsund fjord, Svalbard, for nine seasons and calculated the percentage of the fjord that was covered by ice. On average, sea ice was present in Hornsund for 158 d per year, but it varied from year to year. April was the "iciest'" month and 2019/2020, 2021/22 and 2014/15 were the "iciest'" seasons. Our data can be used to understand sea ice conditions compared with other fjords of Svalbard and in studies of wave modelling and coastal erosion.
Laust Færch, Wolfgang Dierking, Nick Hughes, and Anthony P. Doulgeris
The Cryosphere, 17, 5335–5355, https://doi.org/10.5194/tc-17-5335-2023, https://doi.org/10.5194/tc-17-5335-2023, 2023
Short summary
Short summary
Icebergs in open water are a risk to maritime traffic. We have compared six different constant false alarm rate (CFAR) detectors on overlapping C- and L-band synthetic aperture radar (SAR) images for the detection of icebergs in open water, with a Sentinel-2 image used for validation. The results revealed that L-band gives a slight advantage over C-band, depending on which detector is used. Additionally, the accuracy of all detectors decreased rapidly as the iceberg size decreased.
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
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.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
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
Cafarella, S. M., Scharien, R., Geldsetzer, T., Howell, S., Haas, C., Segal, R., and Nasonova, S.: Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar, Can. J. Remote Sens., 45, 457–475, https://doi.org/10.1080/07038992.2019.1647102, 2019. a
Casey, J. A., Beckers, J., Busche, T., and Haas, C.: Towards the retrieval of multi-year sea ice thickness and deformation state from polarimetric C- and X-band SAR observations, in: International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 1190–1193, https://doi.org/10.1109/IGARSS.2014.6946644, 2014. a, b, c
Cavalieri, D. J., Markus, T., Ivanoff, A., Liu, A. K., and Zhao, Y.:
AMSR-E/Aqua Daily L3 6.25 km Sea Ice Drift Polar Grids, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA,
https://doi.org/10.5067/AMSR-E/AE_SID.001, 2011. a
Chinchor, N.: MUC-4 Evaluation Metrics, in: Proceedings of the 4th Conference
on Message Understanding, Association for Computational Linguistics, USA, McLean Virginia, USA, 16–18 June 1992,
22–29, https://doi.org/10.3115/1072064.1072067, 1992. a
Cohen, L., Hudson, S. R., Walden, V. P., Graham, R. M., and Granskog, M. A.: Meteorological conditions in a thinner Arctic sea ice regime from winter to summer during the Norwegian Young sea ice expedition (N-ICE2015), J. Geophys. Res., 122, 7235–7259, https://doi.org/10.1002/2016JD026034, 2017. a, b
Cole, S. T., Toole, J. M., Lele, R., Timmermans, M. L., Gallaher, S. G., Stanton, T. P., Shaw, W. J., Hwang, B., Maksym, T., Wilkinson, J. P., Ortiz, M., Graber, H., Rainville, L., Petty, A. A., Farrell, S. L., Richter-Menge, J. A., and Haas, C.: Ice and ocean velocity in the Arctic marginal ice zone: Ice roughness and momentum transfer, Elementa, 5, 55, https://doi.org/10.1525/elementa.241, 2017. a
Comiso, J. C.: Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP
SSM/I-SSMIS, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/7Q8HCCWS4I0R, 2017. a
Dabboor, M., Montpetit, B., Howell, S., and Haas, C.: Improving sea ice characterization in dry ice winter conditions using polarimetric parameters from C- and L-Band SAR data, Remote Sens.-Basel, 9, 6859–6873, https://doi.org/10.3390/rs9121270, 2017. a
Dierking, W.: Mapping of Different Sea Ice Regimes Using Images From Sentinel-1 and ALOS Synthetic Aperture Radar, IEEE T. Geosci. Remote, 48, 1045–1058, https://doi.org/10.1109/TGRS.2009.2031806, 2010. a, b
Dierking, W. and Dall, J.: Sea-ice deformation state from synthetic aperture radar imagery – Part I: Comparison of C- and L-B and and different polarization, IEEE T. Geosci. Remote, 45, 3610–3621, https://doi.org/10.1109/TGRS.2007.903711, 2007. a, b, c
Dierking, W. and Dall, J.: Sea ice deformation state from synthetic aperture radar imagery – Part II: Effects of spatial resolution and noise level, IEEE T. Geosci. Remote, 46, 2197–2207, https://doi.org/10.1109/TGRS.2008.917267, 2008. a, b
Dybkjaer, G.: Medium Resolution Sea Ice Drift Product User Manual, Tech. rep., The Ocean & Sea Ice Satellite Application Facility (OSI SAF),
2018. a
European Space Agency: Copernicus Sentinel data, European Space Agency [data set], available at: https://scihub.copernicus.eu/ (last access: January 2021), 2015. a
European Space Agency: SNAP – ESA Sentinel Application Platform v7.0.4,
available at: http://step.esa.int (last access: January 2021), 2020. a
Fernández-Méndez, M., Olsen, L. M., Kauko, H. M., Meyer, A., Rösel, A., Merkouriadi, I., Mundy, C. J., Ehn, J. K., Johansson, A. M., Wagner, P. M., Ervik, Å., Sorrell, B. K., Duarte, P., Wold, A., Hop, H., and Assmy, P.: Algal hot spots in a changing Arctic Ocean: Sea-ice ridges and the snow-ice interface, Frontiers in Marine Science, 5, 75, https://doi.org/10.3389/fmars.2018.00075, 2018. a
Gatti, A. and Bertolini, A.: Sentinel-2 Products Specification Document,
S2-PDGS-TAS-DI-PSD, Thales Alenia Space, France, 13.1, 2015. a
Gegiuc, A., Similä, M., Karvonen, J., Lensu, M., Mäkynen, M., and Vainio, J.: Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data, The Cryosphere, 12, 343–364, https://doi.org/10.5194/tc-12-343-2018, 2018. a
Gill, J. P. S., Yackel, J. J., Geldsetzer, T., and Fuller, M. C.: Sensitivity of C-band synthetic aperture radar polarimetric parameters to snow thickness over landfast smooth first-year sea ice, Remote Sens. Environ., 166, 34–49, https://doi.org/10.1016/j.rse.2015.06.005, 2015. a, b
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
Gradinger, R., Bluhm, B., and Iken, K.: Arctic sea-ice ridges – Safe heavens for sea-ice fauna during periods of extreme ice melt?, Deep-Sea Res. Pt. II, 57, 86–95, https://doi.org/10.1016/j.dsr2.2009.08.008, 2010. a
Graham, R. M., Itkin, P., Meyer, A., Sundfjord, A., Spreen, G., Smedsrud, L. H., Liston, G. E., Cheng, B., Cohen, L., Divine, D., Fer, I., Fransson, A., Gerland, S., Haapala, J., Hudson, S. R., Johansson, A. M., King, J., Merkouriadi, I., Peterson, A. K., Provost, C., Randelhoff, A., Rinke, A., Rösel, A., Sennéchael, N., Walden, V. P., Duarte, P., Assmy, P., Steen, H., and Granskog, M. A.: Winter storms accelerate the demise of sea ice in the Atlantic sector of the Arctic Ocean, Sci. Rep.-UK, 9, 9222, https://doi.org/10.1038/s41598-019-45574-5, 2019. a, b, c, d, e
Granskog, M. A., Rösel, A., Dodd, P. A., Divine, D., Gerland, S., Martma, T., and Leng, M. J.: Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard, J. Geophys. Res.-Oceans, 122, 2539–2549, https://doi.org/10.1002/2016JC012398, 2017. a, b
Granskog, M. A., Fer, I., Rinke, A., and Steen, H.: Atmosphere-Ice-Ocean-Ecosystem Processes in a Thinner Arctic Sea Ice Regime: The Norwegian Young Sea ICE (N-ICE2015) Expedition, J. Geophys. Res.-Oceans, 123, 1586–1594, https://doi.org/10.1002/2017JC013328, 2018. a, b, c
Herzfeld, U. C., Hunke, E. C., McDonald, B. W., and Wallin, B. F.: Sea ice deformation in Fram Strait – Comparison of CICE simulations with analysis and classification of airborne remote-sensing data, Cold Reg. Sci. Technol., 117, 19–33, https://doi.org/10.1016/j.coldregions.2015.05.001, 2015. a
Howell, S. E., Komarov, A. S., Dabboor, M., Montpetit, B., Brady, M., Scharien, R. K., Mahmud, M. S., Nandan, V., Geldsetzer, T., and Yackel, J. J.: Comparing L- and C-band synthetic aperture radar estimates of sea ice motion over different ice regimes, Remote Sens. Environ., 204, 380–391, https://doi.org/10.1016/j.rse.2017.10.017, 2018. a
Hutchings, J. K., Roberts, A., Geiger, C. A., and Richter-Menge, J.: Spatial and temporal characterization of sea-ice deformation, Ann. Glaciol., 52, 360–368, https://doi.org/10.3189/172756411795931769, 2011. a
Hwang, B., Wilkinson, J., Maksym, T., Graber, H. C., Schweiger, A., Horvat, C., Perovich, D. K., Arntsen, A. E., Stanton, T. P., Ren, J., and Wadhams, P.: Winter-to-summer transition of Arctic sea ice breakup and floe size distribution in the Beaufort Sea, Elementa, 5, 40, https://doi.org/10.1525/elementa.232, 2017. a
Isleifson, D., Hwang, B., Barber, D. G., Scharien, R. K., and Shafai, L.: C-band polarimetric backscattering signatures of newly formed sea ice during fall freeze-up, IEEE T. Geosci. Remote, 48, 3256–3267, https://doi.org/10.1109/TGRS.2010.2043954, 2010. a
Isleifson, D., Galley, R. J., Firoozy, N., Landy, J. C., and Barber, D. G.: Investigations into frost flower physical characteristics and the C-band scattering response, Remote Sens.-Basel, 10, 1–16, https://doi.org/10.3390/rs10070991, 2018. a
Itkin, P., Spreen, G., Cheng, B., Doble, M., Girard-Ardhuin, F., Haapala, J., Hughes, N., Kaleschke, L., Nicolaus, M., and Wilkinson, J.: Thin ice and storms: Sea ice deformation from buoy arrays deployed during N-ICE2015, J. Geophys. Res.-Oceans, 122, 4661–4674, https://doi.org/10.1002/2016JC012403, 2017. a, b, c, d, e, f, g
Itkin, P., Spreen, G., Hvidegaard, S. M., Skourup, H., Wilkinson, J., Gerland, S., and Granskog, M. A.: Contribution of Deformation to Sea Ice Mass Balance: A Case Study From an N-ICE2015 Storm, Geophys. Res. Lett., 45, 789–796, https://doi.org/10.1002/2017GL076056, 2018. a
Johansson, A. M., King, J. A., Doulgeris, A. P., Gerland, S., Singha, S., Spreen, G., and Busche, T.: Combined observations of Arctic sea ice with near-coincident colocated X-band, C-band, and L-band SAR satellite remote sensing and helicopter-borne measurements, J. Geophys. Res.-Oceans, 122, 669–691, https://doi.org/10.1002/2016JC012273, 2017. a, b
Komarov, A. S. and Barber, D. G.: Sea ice motion tracking from sequential dual-polarization RADARSAT-2 images, IEEE T. Geosci. Remote, 52, 121–136, https://doi.org/10.1109/TGRS.2012.2236845, 2014. a
Korosov, A. A. and Rampal, P.: A combination of feature tracking and pattern matching with optimal parametrization for sea ice drift retrieval from SAR data, Remote Sens.-Basel, 9, 258, https://doi.org/10.3390/rs9030258, 2017. a, b
Kwok, R.: The RADARSAT Geophysical Processor System BT – Analysis of SAR Data of the Polar Oceans: Recent Advances, Springer, Berlin, Heidelberg, 235–257, https://doi.org/10.1007/978-3-642-60282-5_11, 1998. a
Landrum, L. and Holland, M. M.: Extremes become routine in an emerging new Arctic, Nat. Clim. Change, 10, 1108–1115, https://doi.org/10.1038/s41558-020-0892-z, 2020. a
Lavergne, T.: Low Resolution Sea Ice Drift Product User's Manual, Tech. rep., The Ocean & Sea Ice Satellite Application Facility (OSI SAF),
2016. a
Lehtiranta, J., Siiriä, S., and Karvonen, J.: Comparing C- and L-band SAR images for sea ice motion estimation, The Cryosphere, 9, 357–366, https://doi.org/10.5194/tc-9-357-2015, 2015. a
Liston, G. E., Polashenski, C., Rösel, A., Itkin, P., King, J., Merkouriadi, I., and Haapala, J.: A Distributed Snow-Evolution Model for Sea-Ice Applications (SnowModel), J. Geophys. Res.-Oceans, 123, 3786–3810, https://doi.org/10.1002/2017JC013706, 2018. a
Liu, H., Guo, H., and Zhang, L.: SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data, IEEE J. Sel. Top. Appl., 8, 1601–1613, https://doi.org/10.1109/JSTARS.2014.2365215, 2015. a
Lohse, J., Doulgeris, A. P., and Dierking, W.: An optimal decision-tree design strategy and its application to sea ice classification from SAR imagery, Remote Sens.-Basel, 11, 1574, https://doi.org/10.3390/rs11131574, 2019. a
Lohse, J., Doulgeris, A. P., and Dierking, W.: Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification, Remote Sens., 13, 552, https://doi.org/10.3390/rs13040552, 2021. a, b
MacDonald Dettwiler Assoc. Ltd. (MDA): Sentinel-1 Product Definition, S1-RS-MDA-52-7440, Issue/Revision: 2/3, 2016. a
MacDonald Dettwiler Assoc. Ltd. (MDA): RADARSAT-2 Product Description, RN-SP-52-1238, Issue 1/14, 2018.
Mahmud, M. S., Geldsetzer, T., Howell, S. E., Yackel, J. J., Nandan, V., and Scharien, R. K.: Incidence angle dependence of HH-polarized C- A nd L-band wintertime backscatter over arctic sea ice, IEEE T. Geosci. Remote, 56, 6686–6698, https://doi.org/10.1109/TGRS.2018.2841343, 2018. a, b
Mäkynen, M. and Juha, K.: Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery over the Kara Sea, IEEE T. Geosci. Remote, 55, 6170–6181, https://doi.org/10.1109/TGRS.2017.2721981, 2017. a, b
Mäkynen, M. P., Manninen, A. T., Similä, M. H., Karvonen, J. A., and Hallikainen, M. T.: Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice, IEEE T. Geosci. Remote, 40, 2593–2605, https://doi.org/10.1109/TGRS.2002.806991, 2002. a
Marcel, W., Clauss, K., Valgur, M., and Sølvsteen, J.: Sentinelsat Python
API, GNU General Public License v3.0+, available at:
https://github.com/sentinelsat/sentinelsat/tree/a551d071f9c5faae09603ec4a3ef9dc3dd3ef833 (last access: January 2021), 2021. a
Marsan, D., Stern, H., Lindsay, R., and Weiss, J.: Scale dependence and localization of the deformation of arctic sea ice, Phys. Rev. Lett., 93, 3–6, https://doi.org/10.1103/PhysRevLett.93.178501, 2004. a
Martin, S., Drucker, R. M., and Fort, M.: A laboratory study of frost flower growth on the surface of young sea ice, J. Geophys. Res., 100, 7027–7036, 1995. a
Martin, T., Tsamados, M., Schröder, D., and Feltham, D. L.: The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: A model study, J. Geophys. Res.-Oceans, 121, 1931–1952,
https://doi.org/10.1002/2015JC011186, 2016. a
Moen, M.-A. N., Doulgeris, A. P., Anfinsen, S. N., Renner, A. H. H., Hughes, N., Gerland, S., and Eltoft, T.: Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts, The Cryosphere, 7, 1693–1705, https://doi.org/10.5194/tc-7-1693-2013, 2013. a, b
Murashkin, D., Spreen, G., Huntemann, M., and Dierking, W.: Method for detection of leads from Sentinel-1 SAR images, Ann. Glaciol., 59, 124–136, https://doi.org/10.1017/aog.2018.6, 2018. a
Northrop, A.: IDEAS – LANDSAT Products Description Document,
IDEAS-VEG-SRV-REP-1320, Telespazio VEGA UK Ltd, 6.0, 2015. a
Onstott, R. G.: SAR and Scatterometer Signatures of Sea Ice, in: Microwave Remote Sensing of Sea Ice, The American Geophysical Union, 68, 73–104,
https://doi.org/10.1029/GM068p0073, 1992. a
Park, J., Won, J., Korosov, A. A., Babiker, M., and Miranda, N.: Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel Images, IEEE T. Geosci. Remote, 57, 4040–4049, https://doi.org/10.1109/TGRS.2018.2889381, 2019. a, b
Park, J.-W., Korosov, A. A., Babiker, M., Won, J.-S., Hansen, M. W., and Kim, H.-C.: Classification of sea ice types in Sentinel-1 synthetic aperture radar images, The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, 2020. a, b
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res.-Oceans, 114, 1–14, https://doi.org/10.1029/2008JC005066, 2009. a
Rampal, P., Weiss, J., Dubois, C., and Campin, J. M.: IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline, J. Geophys. Res.-Oceans, 116, 1–17, https://doi.org/10.1029/2011JC007110, 2011. a
Raney, R. K., Luscombe, A. P., Langham, E. J., and Ahmed, S.: RADARSAT (SAR imaging), P. IEEE,, 79, 839–849, https://doi.org/10.1109/5.90162, 1991. a
Ressel, R., Singha, S., Lehner, S., Rösel, A., and Spreen, G.: Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar, IEEE J. Sel. Top. Appl., 9, 3131–3143, https://doi.org/10.1109/JSTARS.2016.2539501, 2016. a
Segal, R. A., Scharien, R. K., Cafarella, S., and Tedstone, A.: Characterizing
winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago
using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging
SpectroRadiometer, Ann. Glaciol., 61, 284–298, https://doi.org/10.1017/aog.2020.48,
2020. a
Singha, S., Johansson, A. M., and Doulgeris, A. P.: Robustness of SAR Sea Ice Type Classification Across Incidence Angles and Seasons at L-Band, IEEE T. Geosci. Remote, 59, 9941–9952, https://doi.org/10.1109/tgrs.2020.3035029, 2020. a
Spreen, G., Kwok, R., and Menemenlis, D.: Trends in Arctic sea ice drift and role of wind forcing: 1992–2009, Geophys. Res. Lett., 38, 1–6, https://doi.org/10.1029/2011GL048970, 2011.
a
Steer, A. D., Worby, A. P., and Heil, P.: Observed changes in sea-ice floe size distribution during early summer in the western Weddell Sea, Deep-Sea Res. Pt. II, 55, 933–942, 2008. a
Sturm, M., Perovich, D. K., and Holmgren, J.: Thermal conductivity and heat transfer through the snow on the ice of the Beaufort Sea, J. Geophys. Res.-Oceans, 107, C21, https://doi.org/10.1029/2000jc000409, 2002. a
The Mathworks Inc.: MATLAB R2020a, available at:
http://www.mathworks.com/ (last access: May 2021), 2020. a
Theodoridis, S. and Koutroumbas, K.: Pattern Recognition, 4th edn., Academic Press, Inc., USA, ISBN 9781597492720, 2008. a
Toyota, T., Takatsuji, S., and Nakayama, M.: Characteristics of sea ice floe size distribution in the seasonal ice zone, Geophys. Res. Lett., 33, 2–5, https://doi.org/10.1029/2005GL024556, 2006. a
Toyota, T., Ishiyama, J., and Kimura, N.: Measuring Deformed Sea Ice in Seasonal Ice Zones Using L-Band SAR Images, IEEE T. Geosci. Remote, 59, 9361–9381, https://doi.org/10.1109/TGRS.2020.3043335, 2020. a, b
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC), The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020. a
U.S. Geological Survey: Landsat-8 images, U.S. Geological Survey [data set], available at: https://earthexplorer.usgs.gov/TS3 (last access: January 2021), 2015. a
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace, Scotts Valley, CA, United States, 2009. a
Van Wychen, W., Vachon, P. W., Wolfe, J., and Biron, K.: Synergistic RADARSAT-2 and Sentinel-1 SAR Images for Ocean Feature Analysis, Can. J. Remote Sens., 45, 591–602, https://doi.org/10.1080/07038992.2019.1662284, 2019. a
Zakhvatkina, N., Smirnov, V., and Bychkova, I.: Satellite SAR Data-based Sea Ice Classification: An Overview, Geosciences, 9, 152, https://doi.org/10.3390/geosciences9040152, 2019. a, b, c, d
Zakhvatkina, N. Y., Alexandrov, V. Y., Johannessen, O. M., Sandven, S., and Frolov, I. Y.: Classification of sea ice types in ENVISAT synthetic aperture radar images, IEEE T. Geosci. Remote, 51, 2587–2600, https://doi.org/10.1109/TGRS.2012.2212445, 2013. a
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.
This study uses radar satellite data categorized into different sea ice types to detect ice...
