A novel hybrid approach for mapping global surface solar radiation with DSCOVR/EPIC: Combining deep learning with physical algorithm

Surface solar radiation (SSR, also known as global radiation, R g ), is critical for Earth's energy, water, and carbon cycles, yet existing satellite-derived global R g products suffer from spatial inconsistencies due to multi-source data fusion. To address this, we propose a novel hybrid approach integrating deep learning with physical algorithms using observations from the Deep Space Climate Observatory (DSCOVR/EPIC), positioned at the Sun-Earth Lagrange-1 point and continuously observed the entire portion of the Earth with sunshine. Unlike traditional physical algorithms or machine learning algorithms, this method estimates cloud transmittance via a DenseNet-based convolutional neural network (CNN), calculates clear-sky R g using a physical parameterization scheme, and combines these to derive all-sky R g . Meanwhile, the direct and diffuse components (R dir and R dif ) are further separated from the estimated R g using a Light Gradient Boosting Machine (LightGBM) model. The method was trained with in-situ observations from the Baseline Surface Radiation Network (BSRN), and further independently evaluated against in-situ observations from three networks of the Solar Radiation (SOLRAD), China Meteorological Administration (CMA) radiation stations and Global Energy Balance Archive (GEBA). Independent evaluation demonstrates that our hybrid method exhibits excellent spatial scalability. Comparative validation against the product of Hao et al. (2020) derived from DSCOVR/EPIC observations demonstrates our method can generate more accurate global products of R g , R dir and R dif . The innovation of our method lies in integrating machine learning with physical algorithms to leverage their complementary strengths, while overcoming the limitations of high uncertainty associated with cloud optical property retrievals from DSCOVR/EPIC observations. This approach will contribute to the mapping of global spatially consistent radiation products, overcoming the limitations of geostationary and polar-orbiting satellites.