| 10 Nov 2026
Uncertainty-aware XCO2 mapping over China through adaptive fusion of multi-source satellite observations
Abstract. Multi-source satellite observations of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) are limited by sparse orbital sampling, cross-platform footprint mismatch, and heterogeneous retrieval uncertainties, leading to substantial spatial discontinuities and uncertainty propagation errors in regional XCO2 mapping. To address these limitations, we developed an adaptive fusion framework integrating physics-based Gaussian oversampling and Bayesian maximum entropy (PGO-BME) for uncertainty-aware monthly XCO2 reconstruction over China. Monthly observations from DQ-1, OCO-2, OCO-3, and GOSAT collected between June 2022 and December 2024 were fused within the framework. In hold-out validation, the framework achieved an overall R2 of 0.90 and an RMSE of 0.63 ppm, substantially outperforming conventional pixel-center and area-weighting approaches. Independent validation against the Total Carbon Column Observing Network (TCCON) further showed R2 values of 0.93 at both the Hefei and Xianghe stations. The reconstructed dataset preserved both large-scale atmospheric transport patterns and localized anthropogenic enhancements, while posterior uncertainties were consistent with the corresponding spatial prediction errors. These results demonstrate that physically constrained, uncertainty-aware fusion can improve the spatial continuity and reliability of satellite XCO2 fields while retaining atmospheric CO2 variability across spatial scales.
Competing interests: The authors have the following competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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