Sea surface temperature (SST) is one of the physical parameters of the ocean, which plays an important role in meteorology, navigation, and fishing. SST retrieval from thermal infrared remote sensing has become a research focus because of its advantages of high efficiency and wide observation range. However, more accurate retrieval algorithms and more complete data processing procedures are needed for the satellite data with higher spatial–temporal resolution. High-precision SST retrieval models based on split-window algorithm were established using Gaofen-5 Visual and Infrared Multispectral Imager (VIMS) data, some of which introduced the quadratic term of brightness temperature difference into the models. The highest accuracy is better than 0.15 K. Moreover, we proposed a relatively complete cross-calibration approach to solve the problem of unstable calibration coefficients in bands 11 and 12 of VIMS. Therefore, the accuracy of the models was verified by satellite images. At last, the models were applied to the retrieval of SST in the vicinity of Fuqing Nuclear Power Plant. We expand the practical value of VIMS in SST retrieval and thermal discharge monitoring and provide technical support for the application of high-resolution remote sensing data.