人类排放的温室气体和气溶胶是造成全球气候变暖和大气环境恶化的主要因素，也是大气环境卫星遥感的核心探测目标。与传统的单一探测目标卫星相比，实现同平台的大气温室气体和气溶胶协同监测，对于提高温室气体卫星反演精度、改善“自上而下”碳源汇估算、提升温室气体和气溶胶的人为/自然源区分能力具有重要意义，也是各国航天机构积极发展的空间探测手段。本文对欧、日、中、美等具备温室气体和气溶胶协同监测能力的卫星进行系统的介绍，包括卫星平台、传感器、处理算法和质控验证。按照卫星监测任务和传感器用途，将其分为大气综合探测卫星和温室气体监测卫星两大类，并从碳中和行动和大气环境综合治理等需求出发，提出温室气体和气溶胶协同观测星座（GACOC）的概念及其发展方向，包括主被动卫星组网观测、温室气体和气溶胶高精度联合反演算法、人为排放源识别和定量监测等应用。

Climate change is the most critical issue related to human survival and economic development currently being faced by the whole world. Greenhouse gases (GHGs) and aerosol are the main factors contributing to global warming and atmospheric environmental degradation caused by anthropogenic emissions; thus, they are the core detection targets of satellite remote sensing platforms. Compared with traditional single-target satellites, the collaborative monitoring of GHGs and aerosol on the same airborne platform, “Greenhouse gases and Aerosol Collaborative Observation Constellation” (GACOC), could significantly improve the accuracy of CO₂ and CH₄ retrieval. This way could improve the ability to estimate the carbon source and sink via the “top-down” method, as well as the ability to distinguish anthropogenic/natural sources of CO₂, CH₄, and atmospheric particulate matters. The GACOC has become an important spatial detection approach actively developed by aerospace agencies of various countries.This study introduces the satellites launched by the European Union, Japan, China, and the United States that can monitor GHGs and aerosol in one space-borne platform. These satellites are further divided into two categories according to their missions. The first one is the comprehensive atmospheric sounding satellites that independently detect GHGs and aerosol. These satellites can provide the temporal and spatial distribution of columnar CO₂ or CH₄ concentration and aerosol properties in the global context. The representative satellites of this category include ENVISAT, Sentinel-5P, FY-3D, and GF-5, as well as GF-5(02), DQ-1, DQ-2, and MetOp-SG-A that are about to launch in 1-3 years. The second category is the GHG monitoring satellites. Synchronous aerosol and cloud observations on the same platform provide necessary information for high-precision inversion of GHGs. The typical GHG satellites include GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat, and the ESA-planned CO2M series.Focusing on the significant national demands such as assessment of carbon neutrality pathways and atmospheric environmental governance, this study also discusses the development tendencies of monitoring GHGs and aerosol within the framework of a collaborative observation constellation.(1) Identification and quantitative monitoring of large anthropogenic emission sources. The anthropogenic CO₂/CH₄ and aerosol particles (and other tracers such as NO₂) emitted from large-scale industrial areas or cities have some similarities in source, environment, and meteorological condition. Therefore, the high-resolution GHGs and aerosol observation by collaborative satellites can be employed to improve the ability to identify, track, and monitor large-scale, fixed, anthropogenic sources more efficiently.(2) High-precision joint inversion of atmospheric GHGs and aerosol. The scattering of aerosol and cloud greatly impact the inversion accuracy of CO₂/CH₄ satellite products. The advanced spaceborne technology that combines multi-angle, multi-band, and polarimetric measurements obtain high-precision aerosol optical and microphysical parameters. These parameters can be used to generate observation-based aerosol models when dealing with aerosol scattering during GHG’s inversion, and these models are more appropriate than the traditional models from modeling data.(3) Active–passive satellite networking. No single satellite can acquire a daily, global-coverage GHG or aerosol product due to the issues such as limited swath width, large number of cloudy pixels, and strict data quality criteria. Therefore, active–passive satellite networking is an essential approach to satisfy the demands of operationally observing the earth. The GACOC could fill in the data gap effectively and generate a spatially–temporally continuous global dataset of GHGs and aerosol. These data can provide a solid foundation for scientific research such as accurate assessments of climate change and dynamic monitoring of the atmospheric environment.