Fig. 1. Optical path design drawing of DQ-1 satellite atmospheric detection lidar system^[52]

Fig. 2. Schematic diagram of working principle of spaceborne IPDA lidar system^[53]

Fig. 3. Shanhaiguan airborne experiment^[41]. (a) Changes of DAOD and XCO₂ calculated by IWF, PPM and PIM in marine areas; (b) comparison of XCO₂ inverted by IPDA lidar with XCO₂ from in-situ measurement instruments and corresponding flight routes

Fig. 4. Application results of pseudo-observed data, moving average algorithm and EPICSO algorithm^[58]

Fig. 5. Comparison of retrieval results of AVS, AVD and AVX with TCCON observation data^[59]

Fig. 6. Kalman smoothing algorithm and moving average algorithm to reconstruct XCO₂ observation sequence for point source emission monitoring^[60]

Fig. 7. Comparison between estimated annual CO₂ emissions derived from DQ-1 and reported values of three emission inventories^[66]

Fig. 8. Daytime and nighttime CO₂ emission rates of GRES power plant^[67]

Fig. 9. Contribution of XCO₂ enhancement and biosphere fluxes to local XCO₂ enhancement^[73]

Fig. 10. Distribution of echo intensity and signal-to-noise ratio at the top of layer clouds, sea surface and surface observed by DQ-1^[52]

Fig. 11. Comparison of IPDA lidar height measurement and SRTM elevation data^[52-53]

Fig. 12. Satellite footprint distribution and vertical observation comparison results of XCO₂^[53]

Fig. 13. Zonal average XCO₂ changes on globalfrom 2010 to 2013^[87]. (a) Land; (b) ocean