| Sea surface temperature (SST) is an important factor affecting global climate, and constitutes a key position in oceanographic research. In this study, the SST datasets, derived from MODIS, AMSR-2 and HY-2A radiometer, are merged by using the optimal interpolation (OI) and Bayesian maximum entropy (BME) method, respectively. The merged SSTs over the whole year in 2015 are validated by using the iQuam and Argo SST data. The average daily coverage of MODIS, AMSR-2 and HY-2A radiometer is 15%, 21.6% and 22%, respectively, while the average daily coverage of the merged SST based on OI and BME is 98.6% and 99.4%, respectively. Compared with single sensor SST, the spatial coverage of the two merged SSTs is significantly improved. In contrast with iQuam SST, the bias of the merged SSTs based on optimal interpolation and Bayesian maximum entropy is 0.07℃ and 0.04℃, respectively, while the RMSE are both 0.78℃, of which from March to July the merged SSTs based on OI show better accuracy than those based on BME, with the other months on the contrary. Compared with the Argo buoy SST data, the bias of the two merged SSTs is 0.06℃ and 0.01℃, respectively, while the RMSE are 0.77 and 0.75. Overall, the accuracy of the merged SSTs based on BME is slightly better than that based on OI, but meanwhile the computational cost of the BME method is higher. |
