Skip to main content

Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery

  • Wiebke Aldenhoff (a1), Céline Heuzé (a2) and Leif E.B. Eriksson (a1)

In this paper an algorithm for ice/water classification of C- and L-band dual polarization synthetic aperture radar data is presented. A comparison of the two different frequencies is made in order to investigate the potential to improve classification results with multi-frequency data. The algorithm is based on backscatter intensities in co- and cross-polarization and autocorrelation as a texture feature. The mapping between image features and ice/water classification is made with a neural network. Accurate ice/water maps for both frequencies are produced by the algorithm and the results of two frequencies generally agree very well. Differences are found in the marginal ice zone, where the time difference between acquisitions causes motion of the ice pack. C-band reliably reproduces the outline of the ice edge, while L-band has its strengths for thin ice/calm water areas within the icepack. The classification shows good agreement with ice/water maps derived from ice-charts and radiometer data from AMSR-2. Variations are found in the marginal ice zone where the generalization of the ice charts and lower accuracy of ice concentration from radiometer data introduce deviations. Usage of high-resolution dual frequency data could be beneficial for improving ice cover information for navigation and modelling.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery
      Available formats
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery
      Available formats
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery
      Available formats
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Hide All
Berg, A and Eriksson, LEB (2012) SAR algorithm for sea ice concentration—evaluation for the baltic sea. IEEE Geosci. Remote. Sens. Lett., 9(5), 938942 (doi: 10.1109/lgrs.2012.2186280)
Bishop, CM (2006) Pattern Recognition and Machine Learning. Springer-Verlag, New York
Carmack, E, 18 others (2015) Toward quantifying the increasing role of oceanic heat in sea ice loss in the new arctic. Bull. Am. Meteorol. Soc., 96(12), 20792105 (doi: 10.1175/bams-d-13-00177.1)
Carsey, FD ed. (1992) Microwave Remote Sensing of Sea Ice. American Geophysical Union, Washington, DC, USA (doi: 10.1029/gm068)
Casey, JA, Howell, SE, Tivy, A and Haas, C (2016) Separability of sea ice types from wide swath C- and L-band synthetic aperture radar imagery acquired during the melt season. Remote. Sens. Environ., 174, 314328 (doi: 10.1016/j.rse.2015.12.021)
Clausi, DA (2002) An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote. Sens., 28(1), 4562 (doi: 10.5589/m02-004)
Comiso, JC, Meier, WN and Gersten, R (2017) Variability and trends in the Arctic Sea ice cover: Results from different techniques. J. Geophys. Res.: Oceans, 122(8), 68836900 (doi: 10.1002/2017jc012768)
Dierking, W and Busche, T (2006) Sea ice monitoring by L-band SAR: an assessment based on literature and comparisons of JERS-1 and ERS-1 imagery. IEEE Trans. Geosci. Remote. Sens., 44(4), 957970 (doi: 10.1109/tgrs.2005.861745)
Eguíluz, VM, Fernández-Gracia, J, Irigoien, X and Duarte, CM (2016) A quantitative assessment of Arctic shipping in 2010–2014. Sci. Rep., 6(1), 30682 (doi: 10.1038/srep30682)
Eriksson, LE, 7 others (2010) Evaluation of new spaceborne SAR sensors for sea-ice monitoring in the Baltic Sea. Can. J. Remote. Sens., 36(S1), S56S73 (doi: 10.5589/m10-020)
Geldsetzer, T, 5 others (2015) All-season compact-polarimetry C-band SAR observations of sea ice. Can. J. Remote. Sens., 41(5), 485504 (doi:10.1080/07038992.2015.1120661)
Gonzalez, RC and Woods, RE (2007) Digital Image Processing. PRENTICE HALL, Upper Saddle River, NJ
Haralick, RM, Shanmugam, K and Dinstein, I (1973) Textural features for image classification. IEEE Trans. Syst. Man. Cybern., SMC-3(6), 610621 (doi: 10.1109/tsmc.1973.4309314)
Horstmann, J, 5 others (2015) Tropical cyclone winds retrieved from C-band cross-polarized synthetic aperture radar. IEEE Trans. Geosci. Remote. Sens., 53(5), 28872898 (doi: 10.1109/tgrs.2014.2366433)
Howell, SE, 9 others (2018) Comparing L- and C-band synthetic aperture radar estimates of sea ice motion over different ice regimes. Remote. Sens. Environ., 204, 380391 (doi: 10.1016/j.rse.2017.10.017)
Isoguchi, O and Shimada, M (2009) An L-band ocean geophysical model function derived from PALSAR. IEEE Trans. Geosci. Remote. Sens., 47(7), 19251936 (doi: 10.1109/tgrs.2008.2010864)
Jackson, CR, Apel, JR, others (2004) Synthetic aperture radar: marine user's manual, US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Serve, Office of Research and Applications.
Jain, A, Mao, J and Mohiuddin, K (1996) Artificial neural networks: a tutorial. Computer, 29(3), 3144 (doi:10.1109/2.485891)
JAXA (2017) PALSAR-2 Level 1.1/2.1/1.5/3.1 CEOS SAR Product Format Description, Japan Aerospace Exploration Agency.
Johansson, AM, Brekke, C, Spreen, G and King, JA (2018) X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring. Remote. Sens. Environ., 204, 162180 (doi:10.1016/j.rse.2017.10.032)
Johansson, AM, 6 others (2017) 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(1), 669691 (doi:10.1002/2016jc012273)
Karvonen, J (2012) Baltic sea ice concentration estimation based on C-band HH-polarized SAR data. IEEE J. Selected Topics Appl. Earth Obs. Remote Sens., 5(6), 18741884 (doi:10.1109/jstars.2012.2209199)
Karvonen, J (2014) Baltic sea ice concentration estimation based on C-band dual-polarized SAR data. IEEE Trans. Geosci. Remote Sens., 52(9), 55585566 (doi: 10.1109/tgrs.2013.2290331)
Karvonen, J, Simila, M and Makynen, M (2005) Open water detection from baltic sea ice radarsat-1 SAR imagery. IEEE Geosci. Remote Sens. Lett., 2(3), 275279 (doi: 10.1109/lgrs.2005.847930)
Karvonen, J, Vainio, J, Marnela, M, Eriksson, P and Niskanen, T (2015) A comparison between high-resolution EO-based and ice analyst-assigned sea ice concentrations. IEEE J. Selected Topics Appl. Earth Obs. Remote Sens., 8(4), 17991807 (doi: 10.1109/jstars.2015.2426414)
Korosov, AA and Rampal, P (2017) A combination of feature tracking and pattern matching with optimal parametrization for sea ice drift retrieval from SAR data. Remote Sens. (Basel), 9(3), 258 (doi:10.3390/rs9030258)
Kwok, R, Spreen, G and Pang, S (2013) Arctic sea ice circulation and drift speed: decadal trends and ocean currents. J. Geophys. Res.: Oceans, 118(5), 24082425 (doi: 10.1002/jgrc.20191)
Lehtiranta, J, Siiriä, S and Karvonen, J (2015) Comparing C- and L-band SAR images for sea ice motion estimation. Cryosphere, 9(1), 357366 (doi: 10.5194/tc-9-357-2015)
Leigh, S, Wang, Z and Clausi, DA (2014) Automated ice–water classification using dual polarization SAR satellite imagery. IEEE Trans. Geosci. Remote. Sens., 52(9), 55295539 (doi: 10.1109/tgrs.2013.2290231)
Lindsay, R. and Schweiger, A (2015) Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations. Cryosphere, 9(1), 269283 (doi: 10.5194/tc-9-269-2015)
Marcq, S and Weiss, J (2012) Influence of sea ice lead-width distribution on turbulent heat transfer between the ocean and the atmosphere. Cryosphere, 6(1), 143156 (doi: 10.5194/tc-6-143-2012)
Meier, WN, Peng, G, Scott, DJ and Savoie, MH (2014) Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record. Polar. Res., 33(1), 21004 (doi: 10.3402/polar.v33.21004)
Miranda, N (2015) S-1 instrument and product performance status, Fringe Workshop 2015, Frascati.
Møller, MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural. Netw., 6(4), 525533 (doi: 10.1016/s0893-6080(05)80056-5)
Ressel, R, Frost, A and Lehner, S (2015) A neural network-based classification for sea ice types on X-band SAR images. IEEE J. Selected Topics Appl. Earth Obs. Remote Sens., 8(7), 36723680 (doi: 10.1109/jstars.2015.2436993)
Richard, MD and Lippmann, RP (1991) Neural network classifiers estimate Bayesian a posteriori probabilities. Neural. Comput., 3(4), 461483 (doi: 10.1162/neco.1991.3.4.461)
Scheuchl, B, Flett, D, Caves, R and Cumming, I (2004) Potential of RADARSAT-2 data for operational sea ice monitoring. Can. J. Remote Sens., 30(3), 448461 (doi: 10.5589/m04-011)
Serreze, MC and Stroeve, J (2015) Arctic sea ice trends, variability and implications for seasonal ice forecasting. Philosophical Trans. R. Soc. A: Math., Phys. Eng. Sci. 373(2045), 20140159 (doi: 10.1098/rsta.2014.0159)
Shokr, ME (1991) Evaluation of second-order texture parameters for sea ice classification from radar images. J. Geophys. Res., 96(C6), 10625 (doi: 10.1029/91jc00693)
Shuchman, RA, 6 others (1987) Remote sensing of the fram strait marginal ice zone. Science, 236(4800), 429431
Smedsrud, LH, Halvorsen, MH, Stroeve, JC, Zhang, R and Kloster, K (2017) Fram strait sea ice export variability and September Arctic sea ice extent over the last 80 years. Cryosphere, 11(1), 6579 (doi: 10.5194/tc-11-65-2017)
Soh, L-K and Tsatsoulis, C (1999) Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote. Sens., 37(2), 780795 (doi: 10.1109/36.752194)
Spreen, G, Kaleschke, L and Heygster, G (2008) Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys. Res., 113(C2), C02S03 (doi: 10.1029/2005jc003384)
Stehman, SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote. Sens. Environ., 62(1), 7789 (doi: 10.1016/s0034-4257(97)00083-7)
Vihma, T (2014) Effects of Arctic sea ice decline on weather and climate: a review. Surv. Geophys., 35(5), 11751214 (doi: 10.1007/s10712-014-9284-0)
Wakabayashi, H, Mori, Y and Nakamura, K (2013) Sea ice detection in the sea of Okhotsk using PALSAR and MODIS data. IEEE J. Selected Topics Appl. Earth Obs. Remote Sens., 6(3), 15161523 (doi:10.1109/jstars.2013.2258327)
Wang, L, Scott, KA and Clausi, DA (2017) Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote. Sens. (Basel), 9(5), 408 (doi: 10.3390/rs9050408)
Wiebe, H, Heygster, G and Markus, T (2009) Comparison of the ASI ice concentration algorithm with Landsat-7 ETM+ and SAR imagery. IEEE Trans. Geosci. Remote. Sens., 47(9), 30083015 (doi:10.1109/tgrs.2009.2026367)
Zakhvatkina, N, Korosov, A, Muckenhuber, S, Sandven, S and Babiker, M (2017) Operational algorithm for ice water classification on dual-polarized RADARSAT-2 images. Cryosphere, 11(1), 3346 (doi: 10.5194/tc-11-33-2017)
Zakhvatkina, NY, Alexandrov, VY, Johannessen, OM, Sandven, S and Frolov, IY (2012) Classification of sea ice types in ENVISAT synthetic aperture radar images. IEEE Trans. Geosci. Remote. Sens., 51(5), 25872600 (doi: 10.1109/tgrs.2012.2212445)
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Annals of Glaciology
  • ISSN: 0260-3055
  • EISSN: 1727-5644
  • URL: /core/journals/annals-of-glaciology
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed