Super Resolution of Satellite-Derived Sea Surface Temperature Using a Transformer-Based Model

Abstract

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared.