Estimation of sea surface temperature in the Arctic based on Fengyun-3D/MERSI II data

Abstract Sea surface temperature (SST) is a critical parameter in understanding Arctic amplification of climate change. In this study, SST in the Arctic was estimated based on data from the Medium Resolution Spectral Imager (MERSI) II on board the Fengyun-3D (FY-3D) satellite and in-situ measurements. To improve the quality of the MERSI thermal data, an optimization model for stripe noise removal based on the alternating direction multiplier method was employed. Clear-sky SST was estimated based on the nonlinear SST (NLSST) algorithm and tripe NLSST algorithm. When compared with the SST product retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) in September 2019, the mean difference between VIIRS SST and MERSI II SST is −0.21℃ with a standard deviation of 0.29℃ in the daytime, while the mean difference is −0.15℃ with a standard deviation of 0.34℃ at nighttime. Results indicate that the accuracy of MERSI II SST meets the requirements for high-accuracy SST retrieval. Furthermore, these algorithms demonstrate the potential for long-term SST estimation in the Arctic using the FY-3D/MERSI II data.