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Izvestiya, Atmospheric and Oceanic Physics

, Volume 53, Issue 9, pp 1123–1131 | Cite as

Automatic Calculation of Ice Drift Based on AMSR Data

Methods and Means of Processing and Interpretation of Space Information
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Abstract

The results of applying a new method for the automatic calculation of ice drift based on the sequence of Advanced Microwave Scanning Radiometer (AMSR) images are considered. The method is analogous to the cross-correlation method but uses other ways to track template similarity and a new vector rejection criterion. Ice-concentration maps constructed using the ARTIST Sea Ice (ASI) algorithm (University of Bremen) based on data from 89 GHz spectral channels are used as images. While highly accurate at calculating ice drift, the new method has made it possible to reduce the template size, which makes it possible to obtain more detailed drift maps. Even in the case of a template linear size of 50–70 km, the ice-drift calculation accuracy is within 5 cm/s and method informativeness (the proportion of constructed vectors and vectors after rejection) is 60–70%. The method shows higher efficiency if the brightness mismatch is used as a measure of similarity between two templates instead of the correlation coefficient.

Keywords

ice-drift automatic calculation AMSR 

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References

  1. Aleksanin, A.I., Aleksanina, M.G., and Karnatskii, A.Yu., Automatic calculation of ice field shift rates, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2011, vol. 8, no. 2, pp. 9–17.Google Scholar
  2. Aleksanin, A.I., Aleksanina, M.G., and Karnatskii, A.Yu., Automatic calculation of velocities of oceanic surface currents according to satellite image sequences, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2013, vol. 10, no. 2, pp. 131–142.Google Scholar
  3. Emery, W.J., Thomas, A.C., Collins, M.J., Crawford, W.R., and Mackas, D.L., An objective method for computing advective surface velocities from sequential infrared satellite images, J. Geophys. Res., 1986, vol. 91, no. C11, pp. 12865–12878.CrossRefGoogle Scholar
  4. Emery, W.J., Fowler, C.W., Hawkins, J., and Preller, R.H., Fram strait satellite image-derived ice motions, J. Geophys. Res., 1991, vol. 96, no. C3, pp. 4751–4768.CrossRefGoogle Scholar
  5. Hwang, B., Inter-comparison of satellite sea ice motion with drifting buoy data, Int. J. Remote Sens., 2013, vol. 34, no. 24, pp. 8741–8763.CrossRefGoogle Scholar
  6. Kamenkovich, V.M., Koshlyakov, M.N., and Monin, A.S., Sinopticheskie vikhri v okeane (Synoptic Eddies in the Ocean) Leningrad: Gidrometeoizdat, 1987.Google Scholar
  7. Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H., and Breivik, L.-A., Sea ice motion from low-resolution satellite sensors: An alternative method and its validation in the Arctic, J. Geophys. Res., 2010, vol. 115, C10032.CrossRefGoogle Scholar
  8. Maslanik, J., Agnew, T., Drinkwater, M., Emery, W., Fowler, C., Kwok, R., and Liu, A., Summary of icemotion mapping using passive microwave data, National Snow and Ice Data Center (NSIDC) Special Publication 8, November 1998. http://nsidc.org/pubs/special/nsidc_special_report_8.pdf.Google Scholar
  9. Sharkov, E.A., Radioteplovoe distantsionnoe zondirovanie Zemli: fizicheskie osnovy (Radiothermal Remote Sensing of the Earth: Physical Bases), vol. 1, Moscow: IKI RAN, 2014.Google Scholar
  10. Spreen, G., Kaleschke, L., and Heygster, G., Sea ice remote sensing using AMSR-E 89 GHz channels, J. Geophys. Res., 2008, vol. 113, C02S03.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • A. I. Alexanin
    • 1
    • 2
  • M. V. Stopkin
    • 2
  • V. A. Kachur
    • 1
    • 2
  1. 1.Institute of Automation and Control Processes, Far East BranchRussian Academy of ScienceVladivostokRussia
  2. 2.Far Eastern Federal UniversityVladivostokRussia

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