CO 2 is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO 2 of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO 2 distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014)(2015)(2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO 2 . Furthermore, the XCO 2 values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO 2 distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites' measurements reflect the growth trend of the global XCO 2 at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape.

Active remote sensing of atmospheric XCO 2 has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO 2 -IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO 2 globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system's retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO 2 -IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading.

Abstract:Since over 70% of carbon emissions are from urban areas, it is of great importance to develop an effective measurement technique that can accurately monitor atmospheric CO 2 in global urban areas. Remote sensing could be an effective way to achieve this goal. However, due to high aerosol loading in urban areas, there are large, inadequately resolved areas in the CO 2 products acquired by passive remote sensing. China is planning to launch the Atmospheric Environment Monitoring Satellite (AEMS) equipped with a CO 2 -light detecting and ranging (LIDAR) system. This work conducted a feasibility study on obtaining city-scale column CO 2 volume mixing ratios (XCO 2 ) using the LIDAR measurements. A performance framework consisting of a sensor model, sampling model, and environmental model was proposed to fulfill our demand. We found that both the coverage and the accuracy of the LIDAR-derived city-scale XCO 2 values were highly dependent on the orbit height. With an orbit height of 450 km, random errors of less than 0.3% are expected for all four metropolitan areas tested in this work. However, random errors of less than 0.3% were obtained in only two metropolitan areas with an orbit height of 705 km. Our simulations also showed that off-nadir sampling would improve the performance of a CO 2 -Integrated Path Differential Absorption (IPDA) LIDAR system operating in a 705 km orbit. These results indicate that an active remote sensing mission could help to effectively measure XCO 2 values in urban areas. More detailed studies are needed to reveal the potential of such equipment for improving the verification of carbon emissions and the estimation of urban carbon fluxes.