Abstract
Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long-time-series sea ice concentration is an important parameter for climate research. Sea ice concentration (SIC) products over the Antarctic and Arctic in 2016-2019 were supplied based on the brightness temperature (Tb) data of the FY-3C Microwave Radiation Imager (MWRI). With the Tb data of SSMIS as a reference, monthly calibration models were established based on time-space matching and linear regression. After calibration, the correlation coefficients between the BT of F17/SSMIS and FY-3C/MWRI at different channels were effectively improved. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect.
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