An Improved Method for Estimating Sea Surface Temperature Based on GF-5A Satellite Data in Bohai Bay - Jiren Sun; Daoming Wei; Dianjun Zhang; Zhiwei Sun (Remote Sensing)
Sea surface temperature (SST) is an important physical parameter that plays an important role in the study of various dynamic and thermodynamic processes in the ocean. Common SST retrieval methods are divided into single-channel methods (such as the single window algorithm) and multi-channel methods (such as the split window algorithm). To solve the problem of the low resolution of SST data used in coastal research, this study proposed a split window algorithm by adjusting the two important parameters, atmospheric transmittance and regression coefficients, to estimate SST using remotely sensed GF-5A images with a resolution of 100 m. The results were indirectly validated using MODIS temperature product and directly validated using measured data. The GF-5A image data obtained on 18 July 2024 were compared with MODIS data, giving R2 of 0.985 and RMSE of 0.139 K. For the GF-5A image data obtained on 31 December 2024, the indirectly verified R2 was 0.996 and the RMSE was 0.116 K. The R2 and RMSE values of the direct verification of the accuracy of data from the two GF-5A images and the measured data were 0.999 and 0.613 K, respectively, which are better than the SST retrieval results of Landsat 8 data obtained at the same resolution. This work provides data support for subsequent research on the ecological environment and plant resources in the Bohai Bay.