<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate carbon emission estimates are essential for guiding climate action toward net zero emissions by 2050. The Bayesian inverse method, combined with atmospheric CO₂ measurements and the transport model, can serve as an independent verification approach to improve accuracy. In this study, we developed a Bayesian inverse modelling framework using ground- and space-based measurements and applied it to Seoul to test the framework and constrain its CO₂ emissions. By leveraging the high temporal resolution of ground-based in situ observations and the broad spatial coverage of satellite data, we improved the accuracy of emission estimates. Our results indicate a 4.43 % increase in posterior emissions compared to prior estimates, suggesting that the prior emissions were slightly underestimated. The spatiotemporal variability of posterior emissions increased significantly, enabling us to track CO₂ fluctuations and assess the impact of carbon reduction policies over time and space. Additionally, the mean absolute error was reduced, improving the agreement between simulated and observed CO₂ enhancements. We thoroughly investigated the performance of the inverse model through a sensitivity analysis that considered different observational network configurations. The most substantial reductions in uncertainties (19.2 %) were observed when all available observations were used. The extensive coverage of satellite observations enabled further corrections in areas not covered by ground observations. Overall, this study highlights the importance of combining multiple observational sources to better constrain urban CO₂ emissions. The framework also shows strong potential for application in other cities and can support the development of effective climate mitigation policies.