二氧化碳（CO2）是一种重要的温室气体，利用卫星遥感可实现全球大气CO2的持续大范围监测，对于制定减排策略应对全球气候变暖具有重要意义。大气中的气溶胶散射效应是制约卫星高精度CO2反演的主要因素。现有研究表明，在地表反照率较高的地区，例如沙漠地区，卫星反演的CO2大气柱平均干燥空气混合比(XCO2)比真实值普遍偏高（约0.5%）。然而目前学界对此还缺乏足够的理解和定量分析。围绕这一难点问题，本文应用精准的大气辐射传输模型和基于最优化估计的XCO2全物理反演算法，分析和量化沙漠地区沙尘气溶胶散射效应所导致的XCO2反演误差。本研究从气溶胶的三个重要特征变量出发，包括气溶胶光学厚度（AOD）、气溶胶层高（ALH）和单次散射反照率（SSA），解析沙尘气溶胶散射效应导致XCO2卫星反演结果偏差的物理机制。研究结果表明卫星反演算法中低估沙尘气溶胶的AOD、低估ALH或高估SSA是沙漠地区XCO2出现高估的可能原因。具体表现为：（1）在反演算法不考虑气溶胶的情况下，若实际AOD大于1.0，将导致XCO2反演结果高估超过1%；（2）当AOD低估0.3至0.5时，XCO2的反演结果将高估0.15%至1.28%；（3）当ALH低估超过0.6 km时，XCO2将高估超过1%；（4）在高估单次散射反照率（SSA）的情况下，XCO2反演结果偏高，但高估不超过0.15%。由本文的模拟实验可知，精准的气溶胶信息对于实现高精度的XCO2反演极为关键。本文还探究了沙漠地区可能发生的“临界反照率”效应对反演结果的影响，并指出此效应可能是高亮地表中CO2探测卫星无法同时量化气溶胶信息的根源。本文提出，为了解决这一难题，实际反演中需要综合气溶胶探测仪器的观测数据对气溶胶信息进行进一步约束以提高XCO2的反演精度。本文结论可为未来卫星反演算法的改进提供参考依据。

Carbon Dioxide (CO2) is an important greenhouse gas. Satellite remote sensing of atmospheric CO2 has the advantages of long-term and wide spatial range observation, which is of great significance for verifying emission reduction strategies to cope with global warming. Aerosol scattering in the atmosphere is considered to be a major obstacle for remote sensing retrieval of CO2 with high accuracy. Previous studies have shown that over areas with high surface albedo, such as desert regions, satellite retrievals of atmospheric column-average dry-air mole fraction of CO2 (XCO2) are systematically overestimated, and the bias can reach 50% of the allowable error to meet the practical application requirements. However, sufficient understandings and quantitative analysis of the systematic bias are still lacking. Focusing on this difficult problem, this thesis analyzes and quantifies the bias of XCO2 retrievals caused by the scattering effect of dust aerosol over desert regions using an accurate atmospheric radiative transfer model and a retrieval algorithm based on optimal estimation. This study starts from three important representative variables of aerosols, including aerosol Aerosol optical Optical depth Depth (AOD), aerosol Aerosol layer Layer height Height (ALH) and single Single scattering Scattering albedo Albedo (SSA), to illustrate the physical mechanism of dust aerosol scattering effects on XCO2 remote sensing retrievals. From the perspective of spectra radiance generated from forward radiative transfer model, increasing AOD will lead to decrease in the spectrum continuum level (defined as radiance of channels where gas absorption can be neglected) in the case of high surface albedo through its extinction effect. Increasing in ALH will cause smaller relative absorption depth (defined as the ratio of radiance difference between continuum level and absorption channels to continuum level) which is closely related to the XCO2 retrievals. From the perspective of retrieval model, this thesis carries out separate retrieval experiments using the O2 A Band and the WCO2 Band, respectively, and joint retrieval experiment using both bands. The results show that the underestimation of AOD or ALH of dust aerosols or the overestimation of SSA in satellite retrieval algorithms can be possible causes of the overestimation of XCO2 over deserts. Specifically, we show that: (1) in the case of not considering aerosol in the retrieval algorithm, XCO2 retrievals will be overestimated by more than 1% when the actual AOD is larger than 1.0; (2) when AOD is underestimated by a value between 0.3 and 0.5, XCO2 retrievals will be overestimated by 0.15% - 1.28%; (3) when ALH is underestimated by more than 0.6 km, XCO2 retrievals will be overestimated by more than 1%; (4) when SSA is overestimated, XCO2 retrievals will also be overestimated, but by no more than 0.15%. It can be shown from these simulation experiments that accurate aerosol information is of significant importance to achieving accurate atmospheric XCO2 retrievals. Additionally, this thesis also discusses the impact of potential “critical albedo” on retrievals and demonstrated that its effect is probably the cause of the bias in extracting useful aerosol information from CO2 monitoring satellites. This thesis proposes that to address this difficult problem, observations from aerosol observing instruments should be included in actual retrievals to further constrain the aerosol information to improve the accuracy of XCO2 retrievals.