We describe the approach to estimating the atmospheric carbon dioxide (CO2) for the Aerosol and Carbon Detection Lidar (ACDL) onboard the Atmospheric Environment Monitoring Satellite (AEMS). The method estimates the optimal state vector by maximizing the measurement posterior probability under a given prior state vector probability distribution. A priori constraint considering the spatial correlation is used as regularization to solve the ill-posed problem. We ran a series of observing system simulation experiments to demonstrate the critical outcome and character percentage uncertainty reduction. The results show that the state vector uncertainty can be reduced by ∼ 10% near the surface for the single sounding. The CO2 column-averaged dry air mole fraction (XCO2) derived by this algorithm is more stable than that obtained by the conventional algorithm and enables the monitoring of concentration changes for the multiple soundings. Similar to the Total Carbon Column Observing Network (TCCON), the averaging kernel is also provided for the subsequent flux inversion. Our simulation experiments demonstrate that the structure of the prior error covariance plays an important role in revealing vertical information from observations. In addition, we applied this algorithm to an airborne ACDL experiment for the retrieval of atmospheric CO2 over Bohai Bay on March 14, 2019. AEMS’s observations with a small footprint will yield important information on the carbon cycle, especially for small but strong emission sources.