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• W2945014426 abstract "We have developed an algorithm for thin ice detection under winter conditions using the Advanced Microwave Scanning Radiometer 2 (AMSR2) radiometer high-frequency brightness temperature (36 and 89 GHz) L1R swath data, and a method to combine thin ice swath charts to a more reliable daily thin ice chart. Moderate Resolution Imaging Spectroradiometer (MODIS) ice thickness swath charts were used as reference data for the algorithm development. The algorithm is based on the classification of 36-GHz polarization ratio (PR36) and H-polarization 89-36-GHz gradient ratio (GR3689H) signatures with linear discriminant analysis. We applied an atmospheric correction to the AMSR2 L1R data following established sea ice concentration (SIC) retrieval algorithms. The PR36 and GR8936H signatures were adjusted to a constant air temperature (T <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>a</sub> ) before the thin ice detection using an empirical relationship between them and Ta. The maximum thickness of detected thin ice was estimated to be 20 cm. The thin ice detection with the L1R data is conducted only when SIC ≥ 70% and Ta ≤ -5 °C to limit conditions where thick ice may be erroneously detected as thin ice. The thin ice detection algorithm was developed for the Barents and Kara Seas, but it should also be applicable for other Arctic marginal ice zones (MIZs). The daily thin ice chart was validated using an independent set of MODIS daily ice thickness charts. The average probability for misclassification of thick ice as thin ice was 10% and 32% for vice versa. We demonstrate the use of the daily thin ice chart for monitoring the thin ice fraction in the Barents and Kara Seas." @default.
• W2945014426 created "2019-05-29" @default.
• W2945014426 creator A5067418571 @default.
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• W2945014426 date "2019-10-01" @default.
• W2945014426 modified "2023-10-15" @default.
• W2945014426 title "Thin Ice Detection in the Barents and Kara Seas Using AMSR2 High-Frequency Radiometer Data" @default.
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• W2945014426 doi "https://doi.org/10.1109/tgrs.2019.2913283" @default.
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