Abstract Sea surface temperature (SST) is critical to global climate change analysis and research. This study the quality-controlled in situ sea surface temperature (SST) and Visible and Infrared Scanning Radiometer (VIRR) SST data from Fengyun-3C (FY-3C) satellite processed by bias correction were used, and the Kalman filtering methods with oriented elliptic correlation scales were applied to construct SST fields. Firstly, the model of oriented elliptic correlation scale was established for SST analysis, then the observation observation errors from each type of SST data source were estimated using the optimal matched datasets, and the background field errors were calculated by using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product using the Kalman filtering methods was obtained. Besides, in order to validate these SST results, the SST fields using the optimum interpolation (OI) method were chosen for comparions. The SST data quality analysis of 2016 revealed that the Kalman analysis has a better performance than those of the OI analysis with the root-mean-square errors (RMSEs) of 0.3911 and 0.3243 °C, respectively,which was more closer to the OISST product’s RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.