Abstract
This Arctic daily sea ice concentration dataset (SIC) at 12.5 km resolution generated from the brightness temperature (TB) of the Microwave Radiation Imager (MWRI) sensor aboard on the FY-3D satellite, were produced by Chinese Antarctic Center of Surveying and Mapping, Wuhan University. FY-3D MWRI Level 1 swath data were acquired from the Chinese National Satellite Meteorological Center (http://data.nsmc.org.cn). After geolocation and calibration, all swaths TB data of one calendar day were resampled and combined into the 12.5 km polar stereographic grid. The ASI dynamic tie-points algorithm was adopted to generate daily sea ice concentration data from January 2018 to June 2020. The qualities of this dataset have been assessed by the cross validation from Moderate Resolution Imaging Spectroradiometer (MODIS) images with higher spatial resolution (250m). The absolute biases between the daily MWRI SIC and MODIS SIC ranged from 0.16% to 13.42%. Inter-comparison were also made to MWRI SIC product based on the NT2 algorithm published by the Chinese National Satellite Meteorological Center. Results suggest that this dataset has better capability of capturing large leads features, identifying the small opening and delineating ice boundary shape. This dataset would provide valuable sea ice concentration record for global climate change studies, or basic information for sea ice thickness estimation, snow cover thickness retrieval and navigation route design. Detailed descriptions and validation of this dataset will be soon available on Earth System Science Data. The dataset consists of following two parts: the result of dynamic tie points and the Arctic MWRI SIC product. The dynamic tie points are stored in XLSX format, called ‘dynamic tie points_2018.01-2020.06.xlsx’. The SIC dataset from January 2018 to June 2020 are achieved in TIFF format with the date in the file name. Pixel values in TIFF files have specific meanings: ‘1-100’ for the percentage of SIC, ‘0’ for open water, ‘-1’ for land, ‘-2’ for the Pole Hole and ‘NoData’ for missing data.
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