Carbon dioxide (CO₂) is the most significant anthropogenic greenhouse gas in the atmosphere. Accurately assessing CO₂ emissions is critical for developing effective and feasible reduction policies to mitigate global warming. Spaceborne platforms equipped with active and passive remote sensing instruments enable high-precision global column-averaged dry air mole fraction of CO₂ (XCO₂) observations, supporting the “top-down” approach to carbon emission estimation. Among these, spaceborne integrated path differential absorption (IPDA) lidar offers resilience to aerosol interference and, with its high pulse repetition frequency, can achieve global XCO₂ observations with high temporal and spatial resolution. However, due to single observation errors, the data often need to be processed using the sliding average algorithm, which diminishes the high temporal and spatial resolution advantages of spaceborne IPDA lidar. Therefore, we propose using the Kalman smoothing algorithm to reconstruct the high temporal and spatial resolution lidar XCO₂ observation from spaceborne IPDA data. Simulation experiments validate the algorithm’s filtering performance, and its application to point-source emission monitoring highlights its potential for high-resolution XCO₂ monitoring. These findings underscore the significance of the Kalman smoothing algorithm in enhancing global carbon emission quantification using spaceborne IPDA lidar data.

Based on the high temporal and spatial resolution advantage of spaceborne IPDA lidar XCO₂ data and its offline acquisition characteristics, we propose using the Kalman smoothing algorithm to reconstruct high temporal and spatial resolution XCO₂ observation results. First, a pseudo-true value sequence is constructed based on XCO₂ data simulated by weather research and forecasting model with greenhouse gases module (WRF-GHG). Various levels of observation errors are then superimposed on this sequence to create a pseudo-observation sequence. The filtering performance of the Kalman smoothing algorithm is tested with different state transfer matrices, and the optimal matrix is selected. Comparative experiments show that the Kalman smoothing algorithm outperforms the sliding average algorithm in terms of filtering performance. Finally, both the Kalman smoothing and sliding average algorithms are used to estimate the carbon emission rate of the same point source at the same time, confirming the Kalman smoothing algorithm’s applicability in high-resolution XCO₂ monitoring.

Simulation experiments first determine the state transfer matrix for the Kalman smoothing algorithm, followed by a comparison of its filtering performance with the sliding average algorithm, which uses a spatial resolution of 50 km. The results show that the Kalman smoothing algorithm not only retains the original observation’s temporal and spatial resolution (0.05 s, 337.5 m), but also improves the mean absolute error (MAE) by 9.46%, reduces the root mean square error (RMSE) by 13.39%, and increases the correlation coefficient by 6.46%, compared to the sliding average algorithm with a temporal and spatial resolution of 7.4 s and 50 km. The monitoring capabilities of the Kalman smoothing algorithm and the sliding average algorithm for the same point source emissions are further compared. The XCO₂ enhancement, obtained using the Kalman smoothing algorithm, estimates the point source emission rate at that moment to be 843.2 kg/s, with a correlation of 0.98 between the XCO₂ enhancement and the Gaussian point source model simulation results. In contrast, the sliding average algorithm estimates the point source emission rate at that moment to be 1876.8 kg/s, with a lower correlation of 0.81 between the XCO₂ enhancement and the Gaussian point source model simulation results. According to the emission inventory data for this point source, the annual average emission rate is 1100 kg/s. The instantaneous emission rate calculated by the Kalman smoothing algorithm is closer to this annual average, and the XCO₂ enhancement shows a higher correlation. Therefore, it can be concluded that the Kalman smoothing algorithm offers superior point source emission monitoring capabilities compared to the sliding average algorithm.

In response to the demand for high temporal and spatial resolution in the application of XCO₂ observation results from spaceborne IPDA lidar, we propose the use of the Kalman smoothing algorithm to process the original XCO₂ data. We discuss the selection of the state transfer matrix in the Kalman smoothing algorithm and compare its filtering performance with that of the commonly used sliding average algorithm. The MAE between the Kalman smoothing algorithm’s filtering result and the true value is reduced by 9.46% compared to the sliding average algorithm, which has a temporal and spatial resolution of 7.4 s and 50 km. In addition, the RMSE is reduced by 13.39%, and the correlation coefficient is increased by 6.46%. Therefore, it’s concluded that the Kalman smoothing algorithm provides better filtering performance than the sliding average algorithm, which has a theoretical temporal and spatial resolution of 7.4 s and 50 km while retaining the original high temporal and spatial resolution (0.05 s, 337.5 m). The application of the Kalman smoothing algorithm in point source emission monitoring is also tested. The instantaneous emission rate calculated by the Kalman smoothing algorithm is closer to the annual average, and the XCO₂ enhancement shows a higher correlation. Therefore, it’s shown that the Kalman smoothing algorithm can be effectively applied to high temporal and spatial resolution XCO₂ observation scenarios. High-resolution XCO₂ observations are crucial for assessing regional carbon sources and sinks, and the XCO₂ observations reconstructed using the Kalman smoothing algorithm can provide vital data support.