Estimation of Solar Radiation and Its Direct and Diffuse Components from the FY-4A Based on the Transfer Learning Method

Accurate estimation of surface solar radiation and its global, direct, and diffuse components at high spatiotemporal resolution remains challenging, despite the availability of existing products, due to the combined effects of rapidly varying clouds, aerosols, and the strong dependence of many retrieval methods on auxiliary atmospheric datasets. In this study, we develop a transfer learning framework that leverages the Cloud, Atmospheric Radiation and Renewal Energy Application radiation product derived from Himawari-8 and extend it to China’s FY-4A geostationary satellite. Specifically, a deep neural network pretrained with Himawari-8 data was fine-tuned with FY-4A observations, while Bayesian optimization was employed to automatically determine key hyperparameters and enhance model generalization. The framework requires only satellite top-of-atmosphere reflectance and solar-satellite geometry as dynamic inputs, thereby avoiding reliance on auxiliary meteorological datasets that often restrict real-time and large-scale applicability. Validation against 33 sites from 3 networks during 2018 to 2020 demonstrates that the FY-4A retrievals achieve high accuracy at both instantaneous and daily scales. For representative Baseline Surface Radiation Network sites, the root mean square errors of global, direct, and diffuse radiation are 102.2, 117.5, and 83.1 W m −2 at the instantaneous scale, and 28.5, 30.1, and 22.6 W m −2 at the daily mean scale, respectively. Comparative analysis further indicates that FY-4A outperforms the Clouds and the Earth’s Radiant Energy System in estimating direct and diffuse radiation and consistently surpasses ECMWF Reanalysis v5 across all radiation components. This study establishes the high-precision solar radiation estimation algorithm specifically designed for Chinese geostationary satellites, providing a robust data foundation for solar power generation, energy forecasting, climate research, and broader applications in sustainable energy transition.