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The variations of atmospheric compositions (ACs) can change the atmospheric interactions with other parts of the ecosystem, and it also affects the energy budget of the Earth. Satellite remote sensing plays a crucial role in monitoring AC by providing a matchless global perspective with consistency over long periods. Although China started space exploration missions in the early 1970s, the situation of lacking operational AC satellite missions was turned around until the launch of FY-3A in 2008. In the following decade, more Chinese pathfinder AC satellite missions were putting into operation, and then lead to a boom in related researches on air quality, trace gases and greenhouse gases (GHG) measurements. In particular, moderate resolution imageries of polar mission FY-3 series are used to monitor air quality, and recent comparable imagery onboard geostationary mission FY-4A could provide similar air pollution observation with higher temporal resolution of up to five minutes. Nadir spectrographs onboard FY-3 series and the latest pathfinder mission GF-5 aims to monitor various trace gases. Moreover, hyper-spectrometers onboard GF-5 and another pathfinder mission TanSat are devoted to mapping the global distribution of GHG, as a supplement to operational mission FY-3D. Besides the progress in the space-borne part, the in-situ calibration and validation have achieved fruitfully since the late 1990s, gaining insight into the consistency between the satellite and in-situ observations over China. Applications and studies based on Chinese satellite missions are still growing since it has only been a few years after their launch. Furthermore, the newly planned missions for monitoring AC with active remote sensing instruments are also illustrated, such as lidar, which will be able to improve the accuracy of space-borne observations. • Space-borne programs of China aiming to detect changes of atmospheric composition were reported in this review. • All the Chinese remote sensing programs inculuding operational meteorological satellites series(FY-3/FY-4), pathfinder programs (TANSat, GF-5) and their payloads were briefly introduced. And the instruments were classified by their capability for monitoring aerosol, greenhouse gases and trace gases. • Chinese ground station networks for calibration and vilidation of remote sensed atmospheric composition products were also reported, as well as their typical applications. • China's future remote sensing satellite to enhance capability of the atmospheric composition detection were briefed. [ABSTRACT FROM AUTHOR]

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan's Greenhouse Gases Observing SATellite (GOSAT), the USA's Orbiting Carbon Observatory-2 (OCO-2), and China's Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China's State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS. [ABSTRACT FROM AUTHOR]

Accurate global monitoring of carbon dioxide (CO2) is essential for understanding climate change and informing policy decisions. This study compares column-averaged dry-air mole fractions of CO2 (XCO2) between ACOS_L2_Lite_FP V9r for Japan's Greenhouse Gases Observing Satellite (GOSAT), OCO-2_L2_Lite_FP V10r for the USA's Orbiting Carbon Observatory-2 (OCO-2), and IAPCAS V2.0 for China's Carbon Dioxide Observation Satellite (TANSAT) collectively referred to as GOT, with data from the Total Carbon Column Observing Network (TCCON). Our findings are as follows: (1) Significant data quantity differences exist between OCO-2 and the other satellites, with OCO-2 boasting a data volume 100 times greater. GOT shows the highest data volume between 30–45°N and 20–30°S, but data availability is notably lower near the equator. (2) XCO2 from GOT exhibits similar seasonal variations, with lower concentrations during June, July, and August (JJA) (402.72–403.74 ppm) and higher concentrations during December, January, and February (DJF) (405.74–407.14 ppm). XCO2 levels are higher in the Northern Hemisphere during March, April, and May (MAM) and DJF, while slightly lower during JJA and September, October, and November (SON). (3) The differences in XCO2 (ΔXCO2) reveal that ΔXCO2 between OCO-2 and TANSAT are minor (−0.47 ± 0.28 ppm), whereas the most significant difference is observed between GOSAT and TANSAT (−1.13 ± 0.15 ppm). Minimal differences are seen in SON (with the biggest difference between GOSAT and TANSAT: −0.84 ± 0.12 ppm), while notable differences occur in DJF (with the biggest difference between GOSAT and TANSAT: −1.43 ± 0.17 ppm). Regarding latitudinal variations, distinctions between OCO-2 and TANSAT are most pronounced in JJA and SON. (4) Compared to TCCON, XCO2 from GOT exhibits relatively high determination coefficients (R2 > 0.8), with GOSAT having the highest root mean square error (RMSE = 1.226 ppm, <1.5 ppm), indicating a strong relationship between ground-based observed and retrieved values. This research contributes significantly to our understanding of the spatial characteristics of global XCO2. Furthermore, it offers insights that can inform the analysis of differences in the inversion of carbon sources and sinks within assimilation systems when incorporating XCO2 data from satellite observations. [ABSTRACT FROM AUTHOR]

• Quantifying CO 2 emissions from power plants could be conducted by LiDAR data. • A CO 2 emission quantification model with high applicability was developed. • Quantify model has self-adjustment function in monitoring CO 2 emission. Accurate reporting of point source emissions of CO 2 is fundamental to addressing climate change. Currently, bottom-up verification methods based on inventory statistics face significant challenges in this area. Satellite remote sensing has emerged as a promising approach for cost-effective global-scale verification of point source emissions, with spaceborne LiDAR offering high spatial resolution ideal for this purpose. However, the inversion of CO 2 emissions from spaceborne LiDAR CO 2 concentration observations requires urgent attention, as existing methods heavily rely on prior information in diffusion models and the accuracy of meteorological data. In this work, a novel emission inversion method based on genetic algorithms and trust-region techniques is proposed to estimate CO 2 emissions from point sources using spaceborne LiDAR observations. A comparison between the CO 2 emission rates calculated from actual airborne LiDAR data (as a prototype of spaceborne LiDAR) and emission inventories for the Suizhong power plant showed a deviation of less than 7.0%. Observing system simulation experiment (OSSE) demonstrated that using DQ-1 (spaceborne LiDAR) observation data as input, the relative error of emission rates would be less than 0.6% when the distance between the emission source and the observation footprint is less than 10 km. Furthermore, the developed model mitigates the impact of uncertainties in meteorological data and IPDA (Integrated-Path Differential Absorption) LiDAR measurements on the final emission quantification. The proposed approach is expected to enable DQ-1 to provide affordable and accurate carbon verification services for over 20.0% of the world's strong point source emissions. [ABSTRACT FROM AUTHOR]

This study developed an inversion algorithm for the CO 2 -sounder instrument to be launched on a Chinese satellite. By using the L1B spectral data of the Greenhouse gases Observing Satellite (GOSAT) as input, the simulation results of the column averaged CO 2 dry air mole fraction (XCO 2 ) retrieved from the CO 2 -sounder algorithm were obtained and compared with the GOSAT L2 XCO 2 products for different seasons and geolocations. The simulation data showed a tendency to be less than the GOSAT L2 data, but the two data sets were generally in agreement with a global mean difference of −4.05 ± 6.06 ppm. For the matched data pairs, 51% had their relative differences less than 1% in absolute value, and 76% less than 2%. The seasonal and latitudinal variations of the XCO 2 differences were also investigated, and the largest discrepancy was found in the desert areas with the relative differences of about 3%–4%. These results suggested that better consistency between the two data sets could be achieved if more realistic observational parameters instead of model values were used in the simulation. These parameters included the a priori profiles of atmospheric temperature, concentrations of CO 2 , O 2 , and H 2 O, surface reflectance, aerosol and cloud indices, etc. [ABSTRACT FROM AUTHOR]

The global carbon dioxide hyperspectral remote sensing inversion system GF_VRTM is established for the greenhouse gas carbon dioxide remote sensing detection of GF-5 satellite. The validation and error analysis of global inversion is performed by using GF_VRTM with 21 June 2013 observation data of GOSAT-FTS in this study. The simulation results show that the CO 2 averaged column concentrations (XCO 2 ) overall trends are basically identical between GF_VRTM retrievals and GOSAT-FTS observations. There are 138 exposure points in whole observation points, and the relative error less than 2% is about 85%. The mean square error is 5.00 ppm, but the global average error is 1.09 ppm. If the assumption that GOSAT-FTS observation data is true, GF_VRTM satisfies the average precision requirements (less than 1%) of XCO 2 global inversion. For the results of the comparison, the minimum error scenario and maximum error scenario are selected for error analysis. Error sources are smooth error, measurement noise error, the forward model parameter error and forward model error, respectively. For the minimum error scenario and maximum error scenario, the relative error of XCO 2 inversion caused by smooth error and measurement noise error are 0.44% and −1.62%, respectively, and the relative error of XCO 2 inversion caused by the forward model parameter error are −0.43% and −1.53%, respectively. At the same time, the forward model error is ignored. [ABSTRACT FROM AUTHOR]

The atmospheric carbon dioxide (CO2) column concentrations observed by the Greenhouse Gases Observing Satellite (GOSAT) and ground stations at Mt Waliguan (36.29° N, 100.90° E) and Lulin (23.47° N, 120.87° E) in China are compared. The data covered time periods from June 2009 to November 2011 for GOSAT and from July 2009 to December 2010 for the ground stations. The GOSAT monthly mean data tend to be generally smaller than those of the ground measurements by 5–10 ppm. The spatial and temporal variations of the atmospheric XCO2(dry air, column averaged, molar fraction of CO2) concentrations, especially in the regions of China, are analysed by using the GOSAT monthly mean data. The variations are more significant in the northern hemisphere than in the southern hemisphere and show relatively high values and obvious fluctuations in the 15° N–45° N latitudinal band. These are generally consistent with the measurements of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Atmospheric Infrared Sounder (AIRS). The satellite data show significant seasonal variations, with maximum in April and May and minimum in September and October. This feature is in general agreement with that of the ground observations and previous reports. In the regions of China, the XCO2ranged from 355 ppm to 385 ppm with a mean of 374 ppm, which is in agreement with the global concentration. [ABSTRACT FROM AUTHOR]