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Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

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Abstract

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness (PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index (DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

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Authors and Affiliations

  1. Marine Environmental Monitoring Center, State Oceanic Administration, Dalian, 116023, China

    Shuai Yuan & Xueqin Liu

  2. Key Laboratory of Spatial Active Opto-Electronic Techniques, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China

    Chengyu Liu

Authors
  1. Shuai Yuan
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  2. Chengyu Liu
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  3. Xueqin Liu
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Correspondence to Chengyu Liu.

Additional information

Foundation item: Under the auspices of the National Natural Science Foundation of China (No. 41306091), Public Science and Technology Research Funds Projects of Ocean (No. 201505019-2)

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Yuan, S., Liu, C. & Liu, X. Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data. Chin. Geogr. Sci. 28, 863–872 (2018). https://doi.org/10.1007/s11769-018-0986-y

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  • DOI: https://doi.org/10.1007/s11769-018-0986-y

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