A new transfer learning framework enables China's Fengyun-4A (FY-4A) geostationary satellite to estimate surface solar radiation (SSR) and its global, direct, and diffuse components with high accuracy, according to a study published in the Journal of Remote Sensing on April 29, 2026. The research addresses a critical need for accurate sunlight data to support the clean-energy transition, particularly for solar power forecasting, climate research, and sustainable energy planning.
Surface solar radiation controls Earth's energy balance, hydrological cycles, and the performance of solar photovoltaic and concentrating solar power systems. Ground-based radiometric networks offer reliable observations but are sparse and unevenly distributed, especially across oceans and developing regions. Reanalysis products provide broad coverage but may lose accuracy due to coarse resolution and simplified cloud-aerosol interactions. Satellite observations can fill this gap, yet many existing algorithms are sensor-specific and focus mainly on global radiation rather than separately estimating direct and diffuse components.
Researchers from the Aerospace Information Research Institute, Chinese Academy of Sciences; Sichuan University of Science and Engineering; and the Institute of Atmospheric Physics, Chinese Academy of Sciences, reported (DOI: 10.34133/remotesensing.1044) a new satellite-based method for retrieving global, direct, and diffuse solar radiation from FY-4A. The key advance is a transfer learning strategy that carries radiative knowledge from the Himawari-8 satellite to FY-4A. The team first developed a deep neural network model using Himawari-8 observations and the Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) radiation product, then fine-tuned the pretrained model with FY-4A data. The model uses top-of-atmosphere reflectance and solar-satellite geometry as dynamic inputs, while Bayesian optimization automatically selects key hyperparameters.
Validation was performed using 33 ground stations from the Baseline Surface Radiation Network, Bureau of Meteorology, and Global Tropical Moored Buoy Array during 2018–2020. At representative sites, FY-4A achieved instantaneous root mean square errors of 102.2 W m⁻² for global radiation, 117.5 W m⁻² for direct radiation, and 83.1 W m⁻² for diffuse radiation. At the daily mean scale, RMSEs dropped to 28.5, 30.1, and 22.6 W m⁻², respectively, showing strong performance across different temporal scales.
The authors said the study demonstrates how knowledge from a mature satellite product can be transferred to another platform to build new operational capability. The framework allows FY-4A to estimate not only total sunlight but also the direct and diffuse components that determine how solar energy systems perform under clear, cloudy, and hazy conditions. Reducing reliance on auxiliary meteorological data makes the method more practical for near-real-time monitoring. In their view, the approach turns China's geostationary satellite observations into a more powerful resource for energy and climate applications.
The new FY-4A radiation product could help improve photovoltaic site assessment, power forecasting, grid management, climate modeling, and land-surface simulations. Direct radiation is especially important for concentrating solar power, while diffuse radiation affects PV output under cloudy or aerosol-rich skies. By resolving these components separately, the framework offers more actionable information than global radiation alone. The study also demonstrates that transfer learning can help overcome sensor differences and limited ground training data. Looking ahead, the same strategy could be extended to other Chinese geostationary satellites, including Fengyun-4B, supporting more reliable solar-energy monitoring across East Asia and beyond.
