Reconstruction of Thermal Infrared Sea Surface Temperature Using a Diffusion Model

Sea surface temperature (SST) is a vital parameter in oceanography and climate science, influencing various fields. While remote sensing provides daily SST data, thermal infrared (TIR)-based sensors offer higher spatial resolution but struggle with cloud penetration, often resulting in data gaps and inaccuracies. This study proposes an SST reconstruction framework based on a diffusion model that integrates spatiotemporal information using Himawari TIR and OSTIA SST data, yielding a fully covered SST dataset with a spatial resolution of 0.02°. The model demonstrates good performance in reconstructing SST in the South China Sea (SCS), achieving a coefficient of determination ( $R^{2}$ ) of 0.92, a bias of 0.06 °C, a root-mean-square error (RMSE) of 0.39 °C, and a peak signal-to-noise ratio (PSNR) of 57.93. The transferability of the model is, furthermore, confirmed through accurate SST predictions in the Indian Ocean after training on SCS data, indicating its applicability across different regions with limited Himawari data and its potential for broader geographical applications. Compared to the original Himawari data, the reconstructed SST reduces the RMSE from 1.01 °C to 0.29 °C, increases the $R^{2}$ from 0.71 to 0.86, and adjusts the bias from -0.54 °C to 0.02 °C, thereby enhancing accuracy. By integrating temporal information, the proposed approach captures both spatial and temporal characteristics of SST, effectively representing seasonal variations, small-scale pulsations, rapid coastal changes, and stable offshore fluctuations. This study lays the groundwork for applying the proposed framework to other regions, highlighting its potential for broader applications in generating high-resolution, all-weather SST data.