A Multigranularity Spatiotemporal Attention Model Based on Multisource Satellite Data for Monthly XCO ₂ Reconstruction Over China

Accurate and timely monitoring of atmospheric CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentrations is a crucial prerequisite for achieving carbon peaking and carbon neutrality goals. However, existing satellite observations are often limited by cloud cover, sensor constraints, and orbital characteristics, resulting in data gaps that hinder the direct analysis of CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> spatiotemporal dynamics. To address this, we develop an operational processing pipeline that leverages an integrated deep learning framework to reconstruct monthly, full-coverage XCO₂ over China at 0.05° spatial resolution for the period 2019–2024. This pipeline integrates multi-source satellite data (GOSAT, OCO-2, GF-5B) with auxiliary variables including vegetation indices, nighttime light remote sensing, NO₂ column concentrations, and ERA5 meteorological parameters. By eliminating reliance on assimilated model products, the pipeline generates monthly XCO₂ maps with a processing latency of 1–2 months after satellite observation.Validation against TCCON ground-based observations demonstrates strong reconstruction performance (R² = 0.88, RMSE = 1.50 ppm). When compared to CarbonTracker and CAMS datasets, the reconstructed dataset shows mean biases of −0.28 ppm and −0.46 ppm, and standard deviations of 0.53 ppm and 0.72 ppm, respectively, indicating that it accurately captures the spatial distribution and interannual trends of CO₂ concentrations. Comparative experiments further reveal that incorporating GF-5B data improves the overall model performance. Regional analyses show that the model effectively identifies high-emission hotspot areas. Interannual analysis indicates that the average XCO₂ in China increased from 410.30 ppm in 2019 to 422.73 ppm in 2024, corresponding to an annual growth rate between 0.4% and 0.9%.