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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 5 | Copyright
The Cryosphere, 12, 1665-1679, 2018
https://doi.org/10.5194/tc-12-1665-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 May 2018

Research article | 18 May 2018

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Sanggyun Lee1, Hyun-cheol Kim2, and Jungho Im1 Sanggyun Lee et al.
  • 1School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
  • 2Unit of Arctic Sea-Ice prediction, Korea Polar Research Institute (KOPRI), Incheon, 21990, South Korea

Abstract. We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011–2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

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Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic atmosphere and ocean. In this study, we propose a novel lead detection approach based on waveform mixture analysis. The performance of the proposed approach in detecting leads was promising when compared to the existing methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters, as it directly uses L1B waveform data.
Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic...
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