Sea surface temperature (SST) is a key climate variable, which affects the behavior of the Earthâ\euro™s atmosphere. In this paper, a method coupled with microwave sea surface emissivities (SSEs) is developed to retrieve SST from the intercalibrated measurements acquired by the Microwave Radiation Imager (MWRI) on Fengyun 3D (FY-3D) satellite. First, the spatiotemporal matching samples over sea surfaces in 0.25˚×0.25Ëš between FY-3D MWRI measurements and the fifth generation of European Center for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data in January, April, July and October 2020 are collected and used to determine the unknown coefficients of the SST retrieval algorithm. To improve the accuracy, besides grouping the samples by sea surface wind speed and SST, pseudo-SSEs are introduced into the SST retrieval algorithm. The root mean square errors (RMSEs) of the SST retrieval algorithm in the three steps are 1.18 K, 0.73 K and 0.68 K, respectively. With the same training samples, the SST retrieval algorithm in this work is superior to the Wentz and Meissnerâ\euro™s (W&Mâ\euro™s) algorithm, whose RMSEs are 1.08 K and 1.05 K in the first step and second step, respectively. Then, the SSTs in 2020 between 60ºS and 60ºN are retrieved from the FY-3D MWRI measurements without precipitation and heavy clouds. Finally, the SSTs retrieved in this work are validated with the GMI SST and the iQuam in situ data. The errors of the retrieved SSTs in the three steps are 0.19±1.23 K, 0.17±1.15 K and 0.01±1.15 K against the GMI SST, respectively, while they are 0.48±1.24 K, 0.37±1.13 K and 0.12±1.10 K against the iQuam in situ data, respectively. The errors of both the retrieval algorithm and the derived SSTs in this work are obviously reduced after introducing the pseudo-SSEs into the algorithm, which proves that the SST retrieval algorithm developed in this work is valid and accurate. The error comparisons of SSTs retrieved from the FY-3D MWRI measurements show that the algorithm in this work also outperforms the W&Mâ\euro™s algorithm.