Spectral energy model-driven inversion of XCO2 in IPDA lidar remote sensing

Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO 2 ) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO 2 monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO 2 in the coming years. In this work, based on the satellite's scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO 2 inversion. The 1.572 μm IPDA lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. And the experimental results show that the accuracy of the signal extracted by the 1.572 μm IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572 μm IPDA lidar column length. The optimized XCO 2 was evaluated (standard deviation as an evaluation metric) and its XCO 2 standard deviation reduced by 31%, 63% and 66% in the ocean, plains and mountains, respectively. Our algorithm can obtain the XCO 2 with a consistent trend by using XCO 2 from the OCO-2 satellite as a reference. The calculated XCO 2 is more accurate in areas dominated by anthropogenic factors (plains), owing to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO 2 inversion and has important reference significance for the IPDA lidar carried by China's satellites to be launched in this year.