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Journal of Oceanology and Limnology

, Volume 36, Issue 5, pp 1494–1508 | Cite as

An improved dual-polarized ratio algorithm for sea ice concentration retrieval from passive microwave satellite data and inter-comparison with ASI, ABA and NT2

  • Shugang Zhang (张树刚)Email author
  • Jinping Zhao (赵进平)
  • Min Li (李民)
  • Shixuan Liu (刘世萱)
  • Shuwei Zhang (张曙伟)
Article
  • 14 Downloads

Abstract

The dual-polarized ratio algorithm (DPR) for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data was improved using a contrast ratio (CR) parameter. In contrast to three other algorithms (Artist Sea Ice algorithm, ASI; NASA-Team 2 algorithm, NT2; and AMSR-E Bootstrap algorithm, ABA), this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents, such as open water and 100% sea ice. Instead, it is based on a ratio ( α ) of horizontally and vertically polarized sea ice emissivity at 36.5 GHz, which can be automatically determined by the CR. α exhibited a clear seasonal cycle: changing slowly during winter, rapidly at other times, and reaching a minimum during summer. The DPR was improved using a seasonal α. The systematic differences in the Arctic sea ice area over the complete AMSR-E period (2002–2011) were -0.8%±2.0% between the improved DPR and ASI; -1.3%±1.7% between the improved DPR and ABA; and -0.7%±1.9% between the improved DPR and NT2. The improved DPR and ASI (or ABA) had small seasonal differences. The seasonal differences between the improved DPR and NT2 decreased, except in summer. The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic (north of 83°N) around August 12, 2010, which was confirmed by the Chinese National Arctic Research Expedition. A series of high-resolution MODIS images (250 m×250 m) of the Beaufort Sea during summer were used to assess the four algorithms. According to mean bias and standard deviations, the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms. The improved DPR can provide reasonable sea ice concentration data, especially during summer.

Keyword

Arctic sea ice sea ice concentration algorithm time series Advanced Microwave Scanning Radiometer-EOS (AMSR-E) 

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Notes

Acknowledgement

We also acknowledge the AMSR-E data and the MODIS images provided by the National Snow and Ice Data Center, University of Bremen, and NASA. We thank the sea ice groups and all the crew on R/V Chinese National Arctic Research Expedition (CHINARE-2010) for their observations of sea ice conditions.

References

  1. Andersen S, Tonboe R, Kaleschke L, Heygster G, Pedersen L T. 2007. Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice. J. Geophys. Res., 112(C8): C08004,  https://doi.org/10.1029/2006JC003543.Google Scholar
  2. Comiso J C, Cavalieri D J, Markus T. 2003. Sea ice concentration, ice temperature, and snow depth using AMSR-E Data. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 243–252,  https://doi.org/10.1109/TGRS.2002.808317.CrossRefGoogle Scholar
  3. Comiso J C, Zwally H J. 1997. Temperature corrected Bootstrap algorithm. In: 199. IEEE International Geoscience and Remote Sensing, 1997. IGARSS ‘97. Remote Sensing—A Scientific Vision for Sustainable Development. IEEE, Singapore. p.857–861,  https://doi.org/10.1109/IGARSS.1997.615279.CrossRefGoogle Scholar
  4. Comiso J C. 1995. SSM/I concentrations using the Bootstrap algorithm. NASA Reference Publication 1380. National Aeronautics and Space Administration, Washington, DC. 49p.Google Scholar
  5. Gloersen P, Cavalieri D J. 1986. Reduction of weather effects in the calculation of sea ice concentration from microwave radiances. J. Geophys. Res., 91(C3): 3 913–3 919,  https://doi.org/10.1029/JC091iC03p03913.Google Scholar
  6. Hebert D A, Allard R A, Metzger E J, Posey P G, Preller R H, Wallcraft A J, Phelps M W, Smedstad O M. 2015. Shortterm sea ice forecasting: an assessment of ice concentration and ice drift forecasts using the U.S. Navy’s Arctic Cap Nowcast/Forecast System. J. Geophys. Res., 120(12): 8 327–8 345,  https://doi.org/10.1002/2015JC011283.Google Scholar
  7. Huang W, Lu P, Lei R, Xie H, Li Z. 2016. Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations. Annals of Glaciology, 57(73): 105–118,  https://doi.org/10.1017/aog.2016.30.CrossRefGoogle Scholar
  8. Liu J P, Curry J A, Martinson D G. 2004. Interpretation of recent Antarctic sea ice variability. Geophys. Res. Lett., 31(2): L02205,  https://doi.org/10.1029/2003GL018732.Google Scholar
  9. Maa N, Kaleschke L. 2010. Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea. Tellus A, 62(4): 393–410,  https://doi.org/10.1111/j.1600-0870.2010.00452.x.CrossRefGoogle Scholar
  10. Markus T, Cavalieri D J. 2000. An enhancement of the NASA team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1 387–1 398,  https://doi.org/10.1109/36.843033.CrossRefGoogle Scholar
  11. Mathew N. 2007. Retrieval of surface emissivity of sea ice and temperature profiles over sea ice from passive microwave radiometers. Ph.D. dissertation, Univ. Bremen, Bremen, Germany.Google Scholar
  12. Nakamura T, Yamazaki K, Iwamoto K, Honda M, Miyoshi Y, Ogawa Y, Tomikawa Y, Ukita J. 2016. The stratospheric pathway for Arctic impacts on midlatitude climate. Geophys. Res. Lett., 43(7): 3 494–3 501,  https://doi.org/10.1002/2016GL068330.CrossRefGoogle Scholar
  13. Serreze M C, Stroeve J. 2015. Arctic sea ice trends, variability and implications for seasonal ice forecasting. Philos ophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373: 20140159,  https://doi.org/10.1098/rsta.2014.0159.CrossRefGoogle Scholar
  14. Spreen G, Kaleschke L, Heygster G. 2008. Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys. Res., 113: C02S03,  https://doi.org/10.1029/2005JC003384.Google Scholar
  15. Stocker T F, Qin D, Plattner G K, Tignor M M B, Allen S K, Boschung J, Nauels A, Xia Y, Bex V, Midgley P M. 2013. Climate Change 2013: The Physical Science Basis. Working Group 1 Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge.Google Scholar
  16. Stroeve J C, Crawford A D, Stammerjohn S. 2016. Using timing of ice retreat to predict timing of fall freeze-up in the Arctic. Geophys. Res. Lett., 43(12): 6 332–6 340,  https://doi.org/10.1002/2016GL069314.CrossRefGoogle Scholar
  17. Stroeve J C, Serreze M C, Fetterer F, Arbetter T, Meier W, Maslanik J, Knowles K. 2005. Tracking the arctic’s shrinking ice cover: another extreme September minimum in 2004. Geophys. Res. Lett., 32(4): L04501,  https://doi.org/10.1029/2004GL021810.Google Scholar
  18. Sumata H, Gerdes R, Kauker F, Karcher M. 2015. Empirical error functions for monthly mean Arctic sea-ice drift. J. Geophys. Res., 120(11): 7 450–7 475,  https://doi.org/10.1002/2015JC011151.CrossRefGoogle Scholar
  19. Svendsen E, Kloster K, Farrelly B, Johannessen O M, Johannessen J A, Campbell W J, Gloersen P, Cavalier D J, Mätzler C. 1983. Norwegian remote sensing experiment: evaluation of the nimbus 7 Scanning multichannel microwave radiometer for sea ice research. J. Geophys. Res., 88(C5): 2 781–2 791,  https://doi.org/10.1029/JC088iC05p02781.Google Scholar
  20. Webster M A, Rigor I G, Perovich D K, Rechter-Menge J A, Polashenski C M, Light B. 2015. Seasonal evolution of melt ponds on Arctic sea ice. J. Geophys. Res., 120(9): 5 968–5 982,  https://doi.org/10.1002/2015JC011030.CrossRefGoogle Scholar
  21. Xie H, Lei R, Ke C, Wang H, Li Z, Zhao J, Ackley S F. 2013. Summer sea ice characteristics and morphology in the Pacific Arctic Sector as observed during the CHINARE 2010 cruise. The Cryosphere, 7(4): 1 057–1 072,  https://doi.org/10.5194/tc-7-1057-2013.CrossRefGoogle Scholar
  22. Zhang S G, Zhao J P, Frey K, Su J. 2013. Dual-polarized ratio algorithm for retrieving Arctic sea ice concentration from passive microwave brightness temperature. Journal of Oceanography, 69(2): 215–227,  https://doi.org/10.1007/s10872-012-0167-z.CrossRefGoogle Scholar
  23. Zhao J P, Ren J P. 2000. Study on the method to analyze parameters of Arctic sea ice from airborne digital imagery. Journal of Remote Sensing, 4(4): 271–278,  https://doi.org/10.11834/jrs.20000406.Google Scholar
  24. Zwally H J, Comiso J C, Parkinson C L, Cavalieri D J, Gloersen P. 2002. Variability of Antarctic sea ice 1979–1998. J. Geophys. Res., 107(C5): 9-1-9-19,  https://doi.org/10.1029/2000JC000733.

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shugang Zhang (张树刚)
    • 1
    • 2
    Email author
  • Jinping Zhao (赵进平)
    • 2
  • Min Li (李民)
    • 1
  • Shixuan Liu (刘世萱)
    • 1
  • Shuwei Zhang (张曙伟)
    • 1
  1. 1.Institute of Oceanographic InstrumentationShandong Academy of SciencesQingdaoChina
  2. 2.College of Physical and Environmental OceanographyOcean University of ChinaQingdaoChina

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