Articles | Volume 20, issue 11
https://doi.org/10.5194/tc-20-6331-2026
© Author(s) 2026. 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-20-6331-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Linking the evolution of floe sizes to the sea-ice deformation history using SAR imagery
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, United States
Cooperative Institute for Climate, Ocean and Ecosystem Studies, University of Washington, Seattle, WA, United States
Cecilia M. Bitz
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, United States
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Cited articles
Åström, J., Robertsen, F., Haapala, J., Polojärvi, A., Uiboupin, R., and Maljutenko, I.: A large-scale high-resolution numerical model for sea-ice fragmentation dynamics, The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, 2024. a
Bateson, A. W., Feltham, D. L., Schröder, D., Wang, Y., Hwang, B., Ridley, J. K., and Aksenov, Y.: Sea ice floe size: its impact on pan-Arctic and local ice mass and required model complexity, The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, 2022. a, b, c
Beitsch, A., Kaleschke, L., and Kern, S.: Investigating High-Resolution AMSR2 Sea Ice Concentrations during the February 2013 Fracture Event in the Beaufort Sea, Remote Sens., 6, 3841–3856, https://doi.org/10.3390/rs6053841, 2014. a
Bouchat, A. and Tremblay, B.: Reassessing the Quality of Sea-Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics, J. Geophys. Res.-Oceans, 125, https://doi.org/10.1029/2019jc015944, 2020. a, b
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea-Ice Deformation Fields, J. Geophys. Res.-Oceans, 127, https://doi.org/10.1029/2021jc017667, 2022. a
Clauset, A., Shalizi, C. R., and Newman, M. E. J.: Power-Law Distributions in Empirical Data, SIAM Rev., 51, 661–703, https://doi.org/10.1137/070710111, 2009. a
Denton, A. A. and Timmermans, M.-L.: Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery, The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, 2022. a
Dierking, W.: Sea Ice Monitoring by Synthetic Aperture Radar, Oceanography, https://doi.org/10.5670/oceanog.2013.33, 2013. a
Gherardi, M. and Lagomarsino, M. C.: Characterizing the size and shape of sea ice floes, Sci. Rep., 5, 10226, https://doi.org/10.1038/srep10226, 2015. a, b
Guo, W., Itkin, P., Lohse, J., Johansson, M., and Doulgeris, A. P.: Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity, The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, 2022. a
Hollands, T. and Dierking, W.: Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results, Ann. Glaciol., 52, 311–317, https://doi.org/10.3189/172756411795931462, 2011. a
Horvat, C. and Tziperman, E.: A prognostic model of the sea-ice floe size and thickness distribution, The Cryosphere, 9, 2119–2134, https://doi.org/10.5194/tc-9-2119-2015, 2015. a, b
Horvat, C. and Tziperman, E.: The evolution of scaling laws in the sea ice floe size distribution, J. Geophys. Res.-Oceans, 122, 7630–7650, https://doi.org/10.1002/2016jc012573, 2017. a
Horvat, C., Roach, L. A., Tilling, R., Bitz, C. M., Fox-Kemper, B., Guider, C., Hill, K., Ridout, A., and Shepherd, A.: Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison, The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, 2019. a, b, c, d, e, f, g, h
Hutter, N.: nhutter/floe_deform: Code to Linking the evolution of floe sizes to the sea-ice deformation history using SAR imagery (Version v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.21871700, 2026a. a, b, c
Hutter, N.: Supporting data to Linking the evolution of floe sizes to the sea-ice deformation history using SAR imagery, Zenodo [data set], https://doi.org/10.5281/zenodo.21869954, 2026b. a
Hutter, N., Losch, M., and Menemenlis, D.: Scaling Properties of Arctic Sea Ice Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in Satellite Observations, J. Geophys. Res.-Oceans, 123, 672–687, https://doi.org/10.1002/2017jc013119, 2018. a
Hutter, N., Bouchat, A., Dupont, F., Dukhovskoy, D., Koldunov, N., Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers, P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay, B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High-Resolution Sea Ice Simulations, J. Geophys. Res.-Oceans, 127, https://doi.org/10.1029/2021jc017666, 2022. a
Hwang, B., Ren, J., McCormack, S., Berry, C., Ayed, I. B., Graber, H. C., Aptoula, E., Deming, J. W., and Lee, C. M.: A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery, Elem. Sci. Anth., 5, https://doi.org/10.1525/elementa.154, 2017. a, b, c
Kortum, K., Singha, S., Spreen, G., Hutter, N., Jutila, A., and Haas, C.: SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition, The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, 2024. a, b, c
Krumpen, T., von Albedyll, L., Goessling, H. F., Hendricks, S., Juhls, B., Spreen, G., Willmes, S., Belter, H. J., Dethloff, K., Haas, C., Kaleschke, L., Katlein, C., Tian-Kunze, X., Ricker, R., Rostosky, P., Rückert, J., Singha, S., and Sokolova, J.: MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years, The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, 2021. a, b, c, d
Kwok, R., Petty, A. A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A., Wimert, J., Bagnardi, M., Kurtz, N., and the ICESat-2 Science Team: ATLAS/ICESat-2 L3A Sea Ice Freeboard (ATL10, Version 6), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL10.006, 2023. a, b
Lindsay, R. W. and Stern, H. L.: The RADARSAT Geophysical Processor System: Quality of Sea Ice Trajectory and Deformation Estimates, J. Atmos. Ocean. Tech., 20, 1333–1347, https://doi.org/10.1175/1520-0426(2003)020<1333:trgpsq>2.0.co;2, 2003. a
Lohse, J., Doulgeris, A. P., and Dierking, W.: Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle, Ann. Glaciol., 61, 260–270, https://doi.org/10.1017/aog.2020.45, 2020. a, b
Lopez-Acosta, R. and Wilhelmus, M. M.: Library of sea ice floe remote sensing observations in the Beaufort Sea Marginal Ice Zone, Zenodo [data set], https://doi.org/10.5281/zenodo.4796845, 2021. a, b
Lopez-Acosta, R., Schodlok, M., and Wilhelmus, M.: Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from moderate-resolution visual imagery, Remote Sens. Environ., 234, 111406, https://doi.org/10.1016/j.rse.2019.111406, 2019. a, b
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., Albedyll, L. v., Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H. J., Bessonov, V., Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B., Clemens-Sewall, D., Dadic, R., Damm, E., Boer, G. d., Demir, O., Dethloff, K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N., Gabarró, C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J., Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A., Heuzé, C., Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings, J. K., Hwang, B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke, L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T., Kurtz, N., Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F., Liston, G. E., Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., Maus, S., Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M., Oppelt, N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R., Polashenski, C., Rabe, B., Raphael, I. A., Regnery, J., Rex, M., Ricker, R., Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K., Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M. D., Smith, M. M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J., Thielke, L., Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner, D. N., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC expedition: Snow and sea ice, Elem. Sci. Anth., 10, https://doi.org/10.1525/elementa.2021.000046, 2022. a, b
Petty, A. A., Bagnardi, M., Kurtz, N., Tilling, R., Fons, S., Armitage, T., Horvat, C., and Kwok, R.: Assessment of ICESat-2 sea ice surface classification with Sentinel-2 imagery: implications for freeboard and new estimates of lead and floe geometry, Earth Space Sci., https://doi.org/10.1029/2020ea001491, 2021. a, b, c, d, e
Ressel, R., Frost, A., and Lehner, S.: A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images, IEEE J. Sel. Top. Appl., 8, 3672–3680, https://doi.org/10.1109/jstars.2015.2436993, 2015. a
Ringeisen, D., Hutter, N., and von Albedyll, L.: Deformation lines in Arctic sea ice: intersection angle distribution and mechanical properties, The Cryosphere, 17, 4047–4061, https://doi.org/10.5194/tc-17-4047-2023, 2023. a, b
Roach, L. A., Horvat, C., Dean, S. M., and Bitz, C. M.: An Emergent Sea Ice Floe Size Distribution in a Global Coupled Ocean-Sea Ice Model, J. Geophys. Res.-Oceans, 123, 4322–4337, https://doi.org/10.1029/2017jc013692, 2018. a, b, c
Roach, L. A., Bitz, C. M., Horvat, C., and Dean, S. M.: Advances in Modeling Interactions Between Sea Ice and Ocean Surface Waves, J. Adv. Model. Earth Sy., 11, 4167–4181, https://doi.org/10.1029/2019ms001836, 2019. a, b, c
Scheuchl, B., Flett, D., Caves, R., and Cumming, I.: Potential of RADARSAT-2 data for operational sea ice monitoring, Can. J. Remote Sens., 30, 448–461, https://doi.org/10.5589/m04-011, 2004. a
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, https://doi.org/10.1029/2005jc003384, 2008. a
Stern, H. L., Schweiger, A. J., Stark, M., Zhang, J., Steele, M., and Hwang, B.: Seasonal evolution of the sea-ice floe size distribution in the Beaufort and Chukchi seas, Elem. Sci. Anth., 6, 48, https://doi.org/10.1525/elementa.305, 2018a. a, b, c, d
Thomas, M., Geiger, C., and Kambhamettu, C.: High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery, Cold Reg. Sci. Tech., 52, 207–223, https://doi.org/10.1016/j.coldregions.2007.06.006, 2008. a
Thomas, M., Kambhamettu, C., and Geiger, C. A.: Motion Tracking of Discontinuous Sea Ice, IEEE T. Geosci. Remote, 49, 5064–5079, https://doi.org/10.1109/tgrs.2011.2158005, 2011. a
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/jc080i033p04501, 1975. a
Toyota, T., Haas, C., and Tamura, T.: Size distribution and shape properties of relatively small sea-ice floes in the Antarctic marginal ice zone in late winter, Deep-Sea Res. Pt. II, 58, 1182–1193, https://doi.org/10.1016/j.dsr2.2010.10.034, 2011. a, b, c
Uusinoka, M., Haapala, J., and Polojärvi, A.: Deep Learning-Based Optical Flow in Fine-Scale Deformation Mapping of Sea Ice Dynamics, Geophys. Res. Lett., 52, e2024GL112000, https://doi.org/10.1029/2024GL112000, 2025. a, b
von Albedyll, L. and Hutter, N.: High-resolution sea ice drift and deformation from sequential SAR images in the Transpolar Drift during MOSAiC 2019/2020, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.958449, 2023. a, b, c, d
von Albedyll, L., Hendricks, S., Hutter, N., Murashkin, D., Kaleschke, L., Willmes, S., Thielke, L., Tian-Kunze, X., Spreen, G., and Haas, C.: Lead fractions from SAR-derived sea ice divergence during MOSAiC, The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, 2024. a, b
Weiss, J.: Scaling of Fracture and Faulting of Ice on Earth, Surv. Geophys., 24, 185–227, https://doi.org/10.1023/a:1023293117309, 2003. a, b
Weiss, J.: Drift, Deformation, and Fracture of Sea Ice, A Perspective Across Scales, SpringerBriefs in Earth Sciences, 73–83, https://doi.org/10.1007/978-94-007-6202-2_5, 2013. 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
Zhang, J., Stern, H., Hwang, B., Schweiger, A., Steele, M., Stark, M., and Graber, H. C.: Modeling the seasonal evolution of the Arctic sea ice floe size distribution, Elementa, 4, 000126, https://doi.org/10.12952/journal.elementa.000126, 2016. a
Short summary
Polar oceans are covered by many pieces of floating ice, called floes, that vary in size, shape and thickness. This study focuses on how fracturing caused by stresses from wind and ocean currents changes floe sizes. Using satellite images, we track ice motion and floe sizes, and study floe shapes and how deformation alters the distribution of floe sizes. We find that stronger deformation creates more small floes and present a formula to include this effect in sea-ice models.
Polar oceans are covered by many pieces of floating ice, called floes, that vary in size, shape...
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