Assessment of Four Passive Microwave Sea Ice Concentrations by Using Automatic Modis Sea Ice Classification

This paper assessed the accuracy of four passive microwave (PM) sea ice concentration (SIC) products in polar regions by using twelve scenes MODIS images under clear-sky conditions. The SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2). An adapted optimal threshold method (i.e., the Otsu algorithm) was adopted to automatically classify the MODIS images into sea ice and water which were then aggregated to compare with the PM SIC. The results show that the averaged bias of PM SIC (PM SIC minus MODIS) is less than 6% in Arctic and ranges from -8% to 1 % in Antarctic, and the averaged root mean square error (RMSE) is less than 16% in the whole polar regions. Overall, the SSMIS/ASI product has better performance in Arctic, while the FY3/NT2 has lower bias and RMSE in Antarctic. Meanwhile, it is observed that the error metrics of PM SIC vary noticeably when compared to different MODIS images which may be caused by the diverse surface characteristics of different sea ice types.