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 CO₂ 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 CO₂ products acquired by passive remote sensing. China is planning to launch the Atmospheric Environment Monitoring Satellite (AEMS) equipped with a CO₂-light detecting and ranging (LIDAR) system. This work conducted a feasibility study on obtaining city-scale column CO₂ volume mixing ratios (XCO₂) 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 XCO₂ 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 CO₂-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 XCO₂ 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.

In this study, a 1.57-µm airborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system was developed for CO₂ measurements. This airborne IPDA LIDAR is integrated with a real-time frequency monitoring system, an integrated sensor for temperature, pressure, and humidity, an inertial navigation system, and a global positioning system. The random errors of the LIDAR system, which are caused by the signal noise, background noise, and detector noise, among other factors, are analyzed for different target reflectivities at a flight altitude of 8 km. After parametric optimization, the signal is unsaturated at high target reflectivity. Further, it can be detected at low target reflectivity by adjusting the detector gain. After the averaging of 148 shots, the relative random error (RRE) was 0.057% for a typical target reflectivity of 0.1 sr⁻¹. Moreover, the systematic errors caused by the laser pulse energy, linewidth, spectral purity, and frequency drift, as well as the atmospheric parameters related to the flight experiments are also investigated. The relative system error (RSE) was 0.214% as determined based on an analysis of the systematic errors, which are primarily caused by the frequency drift. Two methods are proposed to reduce the RSE caused by the frequency drift. The first is the averaging of 148 shots, which can reduce the RSE to 0.096%. The other method involves calculating the integral weight function (IWF) using real-time frequency. However, this is a time-consuming and computationally intensive process. Hence, look-up tables for the absorption cross-section were created to overcome this issue, resulting in a decrease in the RSE to 0.096%. Using actual aircraft attitude angles, velocity, and position data from flight experiments, the relative errors (REs) in the IWF caused by the uncorrected integral path and Doppler shift were determined to be 0.273% and 0.479%, respectively. However, it was found that corrections to the integral path and Doppler shift based on accurate calculations of the IWF cause the airborne platform to turn in such a way that the REs are eliminated. Hence, this study confirms the validity of system parameters and provides a reference for other researchers who study similar IPDA LIDAR systems. Further, the sensitivity analysis of the airborne IPDA LIDAR system can provide a reference to future data inversions. Moreover, the proposed correction algorithms for the integral path and Doppler shift contribute to more accurate inversion results.

The demand for greenhouse gas measurement has increased dramatically due to global warming. A 1.57-μm airborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system for CO₂ concentration measurement was developed. The airborne field experiments of this IPDA LIDAR system were conducted at a flight altitude of approximately 7 km, and the weak echo signal of the ocean area was successfully received. The matched filter algorithm was applied to the retrieval of the weak signals, and the pulse integration method was used to improve the signal-to-noise ratio. The inversion results of the CO₂ column-averaged dry-air mixing ratio (XCO₂) by the scheme of averaging after log (AVD) and the scheme of averaging signals before log were compared. The AVD method was found more effective for the experiment. The long-term correlation between the changing trends of XCO₂ retrieved by the IPDA LIDAR system and CO₂ dry-air volume mixing ratio measured by the in-situ instrument reached 92%. In the steady stage of the open area (30 km away from the coast), which is almost unaffected by the residential areas, the mean value of XCO₂ retrieved by the IPDA LIDAR system was 414.69 ppm, with the standard deviation being 1.02 ppm. Compared with the CO₂ concentration measured by the in-situ instrument in the same period, bias was 1.30 ppm. The flight path passed across the ocean, residential, and mountainous areas, with the mean value of XCO₂ of the three areas being 419.35, 429.29, and 422.52 ppm, respectively. The gradient of the residential and ocean areas was 9.94 ppm, with that of the residential and mountainous areas being 6.77 ppm. Obvious gradients were found in different regions.

As one of the most influential greenhouse gases, carbon dioxide (CO₂) has a profound impact on the global climate. The spaceborne integrated path differential absorption (IPDA) lidar will be a great sensor to obtain the columnar concentration of CO₂ with high precision. This paper analyzes the performance of a spaceborne IPDA lidar, which is part of the Aerosol and Carbon Detection Lidar (ACDL) developed in China. The line-by-bine radiative transfer model was used to calculate the absorption spectra of CO₂ and H₂O. The laser transmission process was simulated and analyzed. The sources of random and systematic errors of IPDA lidar were quantitatively analyzed. The total systematic errors are 0.589 ppm. Monthly mean global distribution of relative random errors (RREs) was mapped based on the dataset in September 2016. Afterwards, the seasonal variations of the global distribution of RREs were studied. The global distribution of pseudo satellite measurements for a 16-day orbit repeat cycle showed relatively uniform distribution over the land of the northern hemisphere. The results demonstrated that 61.24% of the global RREs were smaller than 0.25%, or about 1 ppm, while 2.76% of the results were larger than 0.75%. The statistics reveal the future performance of the spaceborne IPDA lidar.

The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based lidar measurements. Under the horizontal smoothing number of 15, 12 and 9, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based lidar were both 0.72. Under the horizontal smoothing number of 6, 3 and 1, the correlation coefficients between the BLH derived by graphics distribution method (GDM) algorithm and ground-based lidar were 0.47, 0.14 and 0.12, respectively. When the horizontal smoothing number was large (15, 12 and 9), the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based lidar. The algorithm provided a reliable result when the horizontal smoothing number was greater than 9. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

The spaceborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system was found to be helpful in observing atmospheric CO₂ and understanding the carbon cycle. The airborne experiments of a scale prototype of China’s planned spaceborne IPDA LIDAR was implemented in 2019. A problem with data inversion caused by the detector module nonlinearity was found. Through many experiments, the amplifier circuit board (ACB) of the detector module was proved to be the main factor causing the nonlinearity. Through amplifier circuit optimization, the original bandwidth of the ACB was changed to 1 MHz by using a fifth-order active filter. Compared with the original version, the linearity of optimized ACB is improved from 42.6% to 0.0747%. The optimized ACB was produced and its linearity was verified by experiments. In addition, the output waveform of the optimized ACB changes significantly, which will affect the random error (RE) of the optimized IPDA LIDAR system. Through the performance simulation, the RE of more than 90% of the global area is less than 0.728 ppm. Finally, the transfer model of the detector module was given, which will be helpful for the further optimization of the CO₂ column-averaged dry-air mixing ratio (XCO₂) inversion algorithm.