Improved seamless mapping of surface O3 concentrations using an integrated deep learning framework

Satellite-derived ozone (O₃) data often contain spatial gaps due to factors such as cloud cover. To achieve seamless O₃ mapping, researchers typically either reconstructed the missing satellite input data before the O₃ inversion or reconstructed the missing O₃ data after inversion. Unlike previous step-by-step approaches, this study proposed a deep learning-based “inversion-reconstruction” integrated framework to estimate seamless surface O₃. By inputting gapped satellite data and other auxiliary information, the framework directly yielded gap-free O₃ data. The O₃ inversion and reconstruction results were jointly optimized in the framework, ensuring high consistency in the seamless mapping of O₃ concentrations. Holdout, spatial, and temporal validations demonstrated the effectiveness of our method for mapping seamless O₃ across China in 2019, with R² values of 0.809, 0.760, and 0.733, respectively. Daily seamless mapping revealed the spatiotemporal patterns of O₃, pollution episodes, and their potential transport routes. The satellite-inverted gapped O₃ data showed a 7.37 ± 4.18% difference from the gap-free merged O₃ data on a national daily scale.