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  • 1
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    Improvement and Sensitivity Analysis of Thermal Thin-Ice Thickness Retrievals (2012)
    IEEE
    In:  EPIC3Geoscience and Remote Sensing, IEEE Transactions, IEEE, 99, pp. 1-13, ISSN: 0196-2892
    Details
    Publication Date: 2019-07-17
    Description: Considering the sea ice decline in the Arctic during the last decades, polynyas are of high research interest since these features are core areas of new ice formation. The determination of ice formation requires accurate retrieval of polynya area and thin-ice thickness (TIT) distribution within the polynya. We use an established energy balance model to derive TITs with MODIS ice surface temperatures $(T_{s})$ and NCEP/DOE Reanalysis II in the Laptev Sea for two winter seasons. Improvements of the algorithm mainly concern the implementation of an iterative approach to calculate the atmospheric flux components taking the atmospheric stratification into account. Furthermore, a sensitivity study is performed to analyze the errors of the ice thickness. The results are the following: 1) 2-m air temperatures $(T_{a})$ and $T_{s}$ have the highest impact on the retrieved ice thickness; 2) an overestimation of $T_{a}$ yields smaller ice thickness errors as an underestimation of $T_{a}$; 3) NCEP $T_{a}$ shows often a warm bias; and 4) the mean absolute error for ice thicknesses up to 20 cm is $pm$4.7 cm. Based on these results, we conclude that, despite the shortcomings of the NCEP data (coarse spatial resolution and no polynyas), this data set is appropriate in combination with MODIS $T_{s}$ for the retrieval of TITs up to 20 cm in the Laptev Sea region. The TIT algorithm can be applied to other polynya regions and to past and future time periods. Our TIT product is a valuable data set for verification of other model and remote sensing ice thickness data.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 2
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    First tests on near real time ice type classification in Antarctica (2014)
    IEEE
    In:  EPIC3Energy and our changing planet, 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014) : Québec City, Québec, Canada, 13 -18 July 2014; [proceedings], Piscataway, NJ, IEEE, 5203 p., pp. 4876-4879, ISBN: 978-1-4799-5775-0, ISSN: 978-1-4799-5774-3
    Details
    Publication Date: 2014-11-27
    Description: In this paper, we explore the capabilities of an algorithm for ice type classification. Our main motivation and exemplary application was the recent incident of the research vessel Akademik Shokalskiy, which was trapped in pack ice for about two weeks. Strong winds had driven ice floes into a way, forming an area of pack ice, blocking the ship's advancement. High-resolution satellite images helped to assess the ice conditions at the location. To extract relevant information automatically from the images, we apply an algorithm that is aimed to generate an ice chart, outlining the different ice type zones such as pack ice, fast ice, open water. The algorithm is based on texture analysis. Textures are selected that allow recognition of different structures in ice. Subsequently, a neural network performs the classification. Since results are output in near real time, the algorithm offers new opportunities for ship routing in ice infested areas.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Inbook , peerRev
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