Articles | Volume 25, issue 20
https://doi.org/10.5194/acp-25-13687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-13687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Oct 2025
Research article |
| 24 Oct 2025
| 24 Oct 2025
Urban Area Observing System (UAOS) simulation experiment using DQ-1 total column concentration observations
Jinchun Yi
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Yiyang Huang
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Zhipeng Pei
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Ge Han
CORRESPONDING AUTHOR
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
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Cited articles
Abshire, J. B., Ramanathan, A. K., Riris, H., Allan, G. R., Sun, X., Hasselbrack, W. E., Mao, J., Wu, S., Chen, J., Numata, K., Kawa, S. R., Yang, M. Y. M., and DiGangi, J.: Airborne measurements of CO2 column concentrations made with a pulsed IPDA lidar using a multiple-wavelength-locked laser and HgCdTe APD detector, Atmos. Meas. Tech., 11, 2001–2025, https://doi.org/10.5194/amt-11-2001-2018, 2018.
Amediek, A., Fix, A., Wirth, M., and Ehret, G.: Development of an OPO system at 1.57 µm for integrated path DIAL measurement of atmospheric carbon dioxide, Applied Physics B, 92, 295–302, 2008.
Andres, R. J., Gregg, J. S., Losey, L., Marland, G., and Boden, T. A.: Monthly, global emissions of carbon dioxide from fossil fuel consumption, Tellus B, 63, 309–327, 2011.
Andres, R. J., Boden, T. A., Bréon, F.-M., Ciais, P., Davis, S., Erickson, D., Gregg, J. S., Jacobson, A., Marland, G., Miller, J., Oda, T., Olivier, J. G. J., Raupach, M. R., Rayner, P., and Treanton, K.: A synthesis of carbon dioxide emissions from fossil-fuel combustion, Biogeosciences, 9, 1845–1871, https://doi.org/10.5194/bg-9-1845-2012, 2012.
Bakwin, P., Davis, K., Yi, C., Wofsy, S., Munger, J., Haszpra, L., and Barcza, Z.: Regional carbon dioxide fluxes from mixing ratio data, Tellus B, 56, 301–311, 2004.
Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P., and White, J.: Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years, Nature, 488, 70–72, 2012.
Birol, F.: World energy outlook 2010, International Energy Agency, https://www.iea.org/reports/world-energy-outlook-2010 (last access: 20 October 2025), 2010.
Bousquet, P., Ciais, P., Peylin, P., Ramonet, M., and Monfray, P.: Inverse modeling of annual atmospheric CO2 sources and sinks: 1. Method and control inversion, Journal of Geophysical Research: Atmospheres, 104, 26161–26178, 1999.
Buchwitz, M., Reuter, M., Schneising, O., Hewson, W., Detmers, R. G., Boesch, H., Hasekamp, O. P., Aben, I., Bovensmann, H., and Burrows, J. P.: Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set, Remote Sensing of Environment, 203, 276–295, 2017.
Che, K., Cai, Z., Liu, Y., Wu, L., Yang, D., Chen, Y., Meng, X., Zhou, M., Wang, J., Yao, L., and Wang, P.: Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements, Environmental Research Letters, 17, 075001, https://doi.org/10.1088/1748-9326/ac7477, 2022.
Che, K., Lauvaux, T., Taquet, N., Stremme, W., Xu, Y., Alberti, C., Lopez, M., García-Reynoso, A., Ciais, P., Liu, Y., Ramonet, M., and Grutter, M.: CO2 Emissions Estimate From Mexico City Using Ground- and Space-Based Remote Sensing, Journal of Geophysical Research: Atmospheres, 129, e2024JD041297, https://doi.org/10.1029/2024JD041297, 2024.
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013, https://doi.org/10.5194/essd-10-1987-2018, 2018.
Dai, G., Wu, S., Long, W., Liu, J., Xie, Y., Sun, K., Meng, F., Song, X., Huang, Z., and Chen, W.: Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1, Atmos. Meas. Tech., 17, 1879–1890, https://doi.org/10.5194/amt-17-1879-2024, 2024.
Deng, A., Lauvaux, T., Davis, K. J., Gaudet, B. J., Miles, N., Richardson, S. J., Wu, K., Sarmiento, D. P., Hardesty, R. M., and Bonin, T. A.: Toward reduced transport errors in a high resolution urban CO2 inversion system, Elem. Sci. Anth., 5, 20, https://doi.org/10.1525/elementa.133, 2017.
Ehret, G., Kiemle, C., Wirth, M., Amediek, A., Fix, A., and Houweling, S.: Space-borne remote sensing of CO2, CH4, and N2O by integrated path differential absorption lidar: a sensitivity analysis, Applied Physics B, 90, 593–608, 2008.
Eldering, A., O'Dell, C. W., Wennberg, P. O., Crisp, D., Gunson, M. R., Viatte, C., Avis, C., Braverman, A., Castano, R., Chang, A., Chapsky, L., Cheng, C., Connor, B., Dang, L., Doran, G., Fisher, B., Frankenberg, C., Fu, D., Granat, R., Hobbs, J., Lee, R. A. M., Mandrake, L., McDuffie, J., Miller, C. E., Myers, V., Natraj, V., O'Brien, D., Osterman, G. B., Oyafuso, F., Payne, V. H., Pollock, H. R., Polonsky, I., Roehl, C. M., Rosenberg, R., Schwandner, F., Smyth, M., Tang, V., Taylor, T. E., To, C., Wunch, D., and Yoshimizu, J.: The Orbiting Carbon Observatory-2: first 18 months of science data products, Atmos. Meas. Tech., 10, 549–563, https://doi.org/10.5194/amt-10-549-2017, 2017a.
Eldering, A., Wennberg, P., Crisp, D., Schimel, D., Gunson, M., Chatterjee, A., Liu, J., Schwandner, F., Sun, Y., and O'Dell, C.: The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes, Science, 358, eaam5745, https://doi.org/10.1126/science.aam5745, 2017b.
Fasoli, B., Lin, J. C., Bowling, D. R., Mitchell, L., and Mendoza, D.: Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2), Geosci. Model Dev., 11, 2813–2824, https://doi.org/10.5194/gmd-11-2813-2018, 2018.
Fischer, M. L., Parazoo, N., Brophy, K., Cui, X., Jeong, S., Liu, J., Keeling, R., Taylor, T. E., Gurney, K., and Oda, T.: Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations, Journal of Geophysical Research: Atmospheres, 122, 3653–3671, 2017.
Gerbig, C., Körner, S., and Lin, J. C.: Vertical mixing in atmospheric tracer transport models: error characterization and propagation, Atmos. Chem. Phys., 8, 591–602, https://doi.org/10.5194/acp-8-591-2008, 2008.
Gerbig, C., Lin, J., Wofsy, S., Daube, B., Andrews, A., Stephens, B., Bakwin, P., and Grainger, C.: Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent: 1. Observed spatial variability from airborne platforms, Journal of Geophysical Research: Atmospheres, 108, https://doi.org/10.1029/2003JD003770, 2003.
Gilfillan, D. and Marland, G.: CDIAC-FF: global and national CO2 emissions from fossil fuel combustion and cement manufacture: 1751–2017, Earth Syst. Sci. Data, 13, 1667–1680, https://doi.org/10.5194/essd-13-1667-2021, 2021.
Gourdji, S. M., Karion, A., Lopez-Coto, I., Ghosh, S., Mueller, K. L., Zhou, Y., Williams, C. A., Baker, I. T., Haynes, K. D., and Whetstone, J. R.: A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models, Journal of Geophysical Research: Biogeosciences, 127, e2021JG006290, https://doi.org/10.1029/2021JG006290, 2022.
Gu, J., Zhang, Y., Yang, N., and Wang, R.: Diurnal variability of the planetary boundary layer height estimated from radiosonde data, Earth and Planetary Physics, 4, 479–492, https://doi.org/10.26464/epp2020042, 2020.
Gurney, K. R., Chen, Y. H., Maki, T., Kawa, S. R., Andrews, A., and Zhu, Z.: Sensitivity of atmospheric CO2 inversions to seasonal and interannual variations in fossil fuel emissions, Journal of Geophysical Research: Atmospheres, 110, https://doi.org/10.1029/2004JD005373, 2005.
Gurney, K. R., Razlivanov, I., Song, Y., Zhou, Y., Benes, B., and Abdul-Massih, M.: Quantification of fossil fuel CO2 emissions on the building/street scale for a large US city, Environmental science & technology, 46, 12194–12202, 2012.
Gurney, K. R., Mendoza, D. L., Zhou, Y., Fischer, M. L., Miller, C. C., Geethakumar, S., and de la Rue du Can, S.: High resolution fossil fuel combustion CO2 emission fluxes for the United States, Environmental Science & Technology, 43, 5535–5541, 2009.
Gurney, K. R., Liang, J., O'keeffe, D., Patarasuk, R., Hutchins, M., Huang, J., Rao, P., and Song, Y.: Comparison of global downscaled versus bottom-up fossil fuel CO2 emissions at the urban scale in four US urban areas, Journal of Geophysical Research: Atmospheres, 124, 2823–2840, 2019.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D., Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., and Fan, S.: Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models, Nature, 415, 626–630, 2002.
Hakkarainen, J., Ialongo, I., and Tamminen, J.: Direct space-based observations of anthropogenic CO2 emission areas from OCO-2, Geophysical Research Letters, 43, 11400–11406, 2016.
Han, G., Gong, W., Lin, H., Ma, X., and Xiang, Z.: Study on Influences of Atmospheric Factors on Vertical CO2 Profile Retrieving From Ground-Based DIAL at 1.6 µm, IEEE Transactions on Geoscience and Remote Sensing, 53, 3221–3234, 2014.
Han, G., Ma, X., Liang, A., Zhang, T., Zhao, Y., Zhang, M., and Gong, W.: Performance evaluation for China's planned CO2-IPDA, Remote Sensing, 9, 768, https://doi.org/10.3390/rs9080768, 2017.
Han, G., Huang, Y., Shi, T., Zhang, H., Li, S., Zhang, H., Chen, W., Liu, J., and Gong, W.: Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1, Remote Sensing of Environment, 313, 114368, https://doi.org/10.1016/j.rse.2024.114368, 2024.
Han, G., Zhang, H., Huang, Y., Chen, W., Mao, H., Zhang, X., Ma, X., Li, S., Zhang, H., and Liu, J.: First global XCO2 observations fromspaceborne lidar: methodology and initial result, Remote Sensing of Environment, 330, 114954, https://doi.org/10.1016/j.rse.2025.114954, 2025.
Hefner, M., Marland, G., and Oda, T.: The changing mix of fossil fuels used and the related evolution of CO2 emissions, Mitigation and Adaptation Strategies for Global Change, 29, 56, https://doi.org/10.1007/s11027-024-10149-x, 2024.
International Energy Agency: World energy outlook, Paris: OECD/IEA, https://www.iea.org/reports/world-energy-outlook-2009 (last access: 20 October 2025), 2009.
Kaminski, T., Scholze, M., Vossbeck, M., Knorr, W., Buchwitz, M., and Reuter, M.: Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide, Remote Sensing of Environment, 203, 109–124, https://doi.org/10.1016/j.rse.2017.08.017, 2017.
Kawa, S. R., Mao, J., Abshire, J. B., Collatz, G. J., Sun, X., and Weaver, C. J.: Simulation studies for a space-based CO2 lidar mission, Tellus B, 62, 759–769, https://doi.org/10.1111/j.1600-0889.2010.00486.x, 2010.
Kiemle, C., Quatrevalet, M., Ehret, G., Amediek, A., Fix, A., and Wirth, M.: Sensitivity studies for a space-based methane lidar mission, Atmos. Meas. Tech., 4, 2195–2211, https://doi.org/10.5194/amt-4-2195-2011, 2011.
Köhler, P., Guanter, L., Kobayashi, H., Walther, S., and Yang, W.: Assessing the potential of sun-induced fluorescence and the canopy scattering coefficient to track large-scale vegetation dynamics in Amazon forests, Remote Sensing of Environment, 204, 769–785, https://doi.org/10.1016/j.rse.2017.09.025, 2018.
Kort, E. A., Angevine, W. M., Duren, R., and Miller, C. E.: Surface observations for monitoring urban fossil fuel CO2 emissions: Minimum site location requirements for the Los Angeles megacity, Journal of Geophysical Research: Atmospheres, 118, 1577–1584, https://doi.org/10.1002/jgrd.50135, 2013.
Lauvaux, T. and Davis, K. J.: Planetary boundary layer errors in mesoscale inversions of column-integrated CO2 measurements, Journal of Geophysical Research: Atmospheres, 119, 490–508, https://doi.org/10.1002/2013JD020175, 2014.
Lauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambaliza, M. O., Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J., O'Keefe, D., Song, Y., Karion, A., Oda, T., Patarasuk, R., Razlivanov, I., Sarmiento, D., Shepson, P., Sweeney, C., Turnbull, J., and Wu, K.: High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX), Journal of Geophysical Research: Atmospheres, 121, 5213–5236, https://doi.org/10.1002/2015JD024473, 2016.
Li, H., Liu, B., Gong, W., Ma, Y., Jin, S., Wang, W., Fan, R., and Jiang, S.: Influence of clouds on planetary boundary layer height: A comparative study and factors analysis, Atmospheric Research, 314, 107784, https://doi.org/10.1016/j.atmosres.2024.107784, 2025.
Li, X., Xiao, J., and He, B.: Chlorophyll fluorescence observed by OCO-2 is strongly related to gross primary productivity estimated from flux towers in temperate forests, Remote Sensing of Environment, 204, 659–671, https://doi.org/10.1016/j.rse.2017.09.034, 2018.
Lin, J. C. and Gerbig, C.: Accounting for the effect of transport errors on tracer inversions, Geophysical Research Letters, 32, https://doi.org/10.1029/2004GL021127, 2005.
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., and Grainger, C. A.: A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model, Journal of Geophysical Research: Atmospheres, 108, https://doi.org/10.1029/2002JD003161, 2003.
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Grainger, C. A., Stephens, B. B., Bakwin, P. S., and Hollinger, D. Y.: Measuring fluxes of trace gases at regional scales by Lagrangian observations: Application to the CO2 Budget and Rectification Airborne (COBRA) study, Journal of Geophysical Research: Atmospheres, 109, https://doi.org/10.1029/2004JD004754, 2004.
Luo, B., Yang, J., Song, S., Shi, S., Gong, W., Wang, A., and Du, L.: Target Classification of Similar Spatial Characteristics in Complex Urban Areas by Using Multispectral LiDAR, Remote Sensing, 14, 238, https://doi.org/10.3390/rs14010238, 2022.
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X., Dunn, A. L., Lin, J. C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM), Global Biogeochemical Cycles, 22, https://doi.org/10.1029/2006GB002735, 2008.
Mao, J., Ramanathan, A., Abshire, J. B., Kawa, S. R., Riris, H., Allan, G. R., Rodriguez, M., Hasselbrack, W. E., Sun, X., Numata, K., Chen, J., Choi, Y., and Yang, M. Y. M.: Measurement of atmospheric CO2 column concentrations to cloud tops with a pulsed multi-wavelength airborne lidar, Atmos. Meas. Tech., 11, 127–140, https://doi.org/10.5194/amt-11-127-2018, 2018.
Miller, J. B., Tans, P. P., and Gloor, M.: Steps for success of OCO-2, Nature Geoscience, 7, 691–691, https://doi.org/10.1038/ngeo2255, 2014.
Moore III, B., Crowell, S. M. R., Rayner, P. J., Kumer, J., O'Dell, C. W., O'Brien, D., Utembe, S., Polonsky, I., Schimel, D., and Lemen, J.: The Potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to Provide Multi-scale Constraints on the Carbon Cycle in the Americas, Frontiers in Environmental Science, 6, 2018, https://doi.org/10.3389/fenvs.2018.00109, 2018.
Myneni, R. B., Dong, J., Tucker, C. J., Kaufmann, R. K., Kauppi, P. E., Liski, J., Zhou, L., Alexeyev, V., and Hughes, M. K.: A large carbon sink in the woody biomass of Northern forests, Proceedings of the National Academy of Sciences, 98, 14784–14789, https://doi.org/10.1073/pnas.261555198, 2001.
Nehrkorn, T., Eluszkiewicz, J., Wofsy, S. C., Lin, J. C., Gerbig, C., Longo, M., and Freitas, S.: Coupled weather research and forecasting–stochastic time-inverted lagrangian transport (WRF–STILT) model, Meteorology and Atmospheric Physics, 107, 51–64, https://doi.org/10.1007/s00703-010-0068-x, 2010.
Nehrkorn, T., Henderson, J., Leidner, M., Mountain, M., Eluszkiewicz, J., McKain, K., and Wofsy, S.: WRF Simulations of the Urban Circulation in the Salt Lake City Area for CO2 Modeling, Journal of Applied Meteorology and Climatology, 52, 323–340, https://doi.org/10.1175/JAMC-D-12-061.1, 2013.
OCO-2/OCO-3 Science Team, Payne, V., and Chatterjee, A.: OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V11.1r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/8E4VLCK16O6Q, 2022.
Oda, T. and Maksyutov, S.: A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights, Atmos. Chem. Phys., 11, 543–556, https://doi.org/10.5194/acp-11-543-2011, 2011.
Oda, T. and Maksyutov, S.: ODIAC Fossil Fuel CO2 Emissions Dataset, Center for Global Environmental Research, National Institute for Environmental Studies [data set], https://doi.org/10.17595/20170411.001, 2015.
Ott, L.: GEOS-Carb CASA-GFED 3-hourly Ecosystem Exchange Fluxes 0.5 degree x 0.625 degree V2, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/5MQJ64JTBQ40, 2020.
Patra, P. K., Hajima, T., Saito, R., Chandra, N., Yoshida, Y., Ichii, K., Kawamiya, M., Kondo, M., Ito, A., and Crisp, D.: Evaluation of earth system model and atmospheric inversion using total column CO2 observations from GOSAT and OCO-2, Progress in Earth and Planetary Science, 8, 25, https://doi.org/10.1186/s40645-021-00420-z, 2021.
Pei, Z., Han, G., Ma, X., Shi, T., and Gong, W.: A Method for Estimating the Background Column Concentration of CO2 Using the Lagrangian Approach, IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12, https://doi.org/10.1109/TGRS.2022.3176134, 2022.
Pillai, D., Gerbig, C., Kretschmer, R., Beck, V., Karstens, U., Neininger, B., and Heimann, M.: Comparing Lagrangian and Eulerian models for CO2 transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM, Atmos. Chem. Phys., 12, 8979–8991, https://doi.org/10.5194/acp-12-8979-2012, 2012.
Qiu, R., Han, G., Li, X., Xiao, J., Liu, J., Wang, S., Li, S., and Gong, W.: Contrasting responses of relationship between solar-induced fluorescence and gross primary production to drought across aridity gradients, Remote Sensing of Environment, 302, 113984, https://doi.org/10.1016/j.rse.2023.113984, 2024.
Rayner, P. J. and O'Brien, D. M.: The utility of remotely sensed CO2 concentration data in surface source inversions, Geophysical Research Letters, 28, 175–178, https://doi.org/10.1029/2000GL011912, 2001.
Refaat, T. F., Singh, U. N., Yu, J., Petros, M., Remus, R., and Ismail, S. J. A. O.: Double-pulse 2-µm integrated path differential absorption lidar airborne validation for atmospheric carbon dioxide measurement, Applied Optics, 55, 4232–4246, 2016.
Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Schneising, O., Pillai, D., Bovensmann, H., Burrows, J. P., Bösch, H., Parker, R., Butz, A., Hasekamp, O., O'Dell, C. W., Yoshida, Y., Gerbig, C., Nehrkorn, T., Deutscher, N. M., Warneke, T., Notholt, J., Hase, F., Kivi, R., Sussmann, R., Machida, T., Matsueda, H., and Sawa, Y.: Satellite-inferred European carbon sink larger than expected, Atmos. Chem. Phys., 14, 13739–13753, https://doi.org/10.5194/acp-14-13739-2014, 2014.
Roten, D., Lin, J. C., Kunik, L., Mallia, D., Wu, D., Oda, T., and Kort, E. A.: The Information Content of Dense Carbon Dioxide Measurements from Space: A High-Resolution Inversion Approach with Synthetic Data from the OCO-3 Instrument, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-315, 2022.
Schwandner, F. M., Gunson, M. R., Miller, C. E., Carn, S. A., Eldering, A., Krings, T., Verhulst, K. R., Schimel, D. S., Nguyen, H. M., Crisp, D., O'Dell, C. W., Osterman, G. B., Iraci, L. T., and Podolske, J. R.: Spaceborne detection of localized carbon dioxide sources, Science, 358, eaam5782, https://doi.org/10.1126/science.aam5782, 2017.
Shan, Y., Liu, J., Liu, Z., Xu, X., Shao, S., Wang, P., and Guan, D.: New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors, Applied Energy, 184, 742–750, 2016.
Shan, Y., Guan, D., Zheng, H., Ou, J., Li, Y., Meng, J., Mi, Z., Liu, Z., and Zhang, Q.: China CO2 emission accounts 1997–2015, Scientific Data, 5, 170201, https://doi.org/10.1038/sdata.2017.201, 2018.
Shekhar, A., Chen, J., Paetzold, J. C., Dietrich, F., Zhao, X., Bhattacharjee, S., Ruisinger, V., and Wofsy, S. C.: Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite, Environmental Research Letters, 15, 095010, https://doi.org/10.1088/1748-9326/ab9cfe, 2020.
Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W., Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S., Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M., Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and Denning, A. S.: Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO2, Science, 316, 1732–1735, https://doi.org/10.1126/science.1137004, 2007.
Sun, Y., Frankenberg, C., Jung, M., Joiner, J., Guanter, L., Köhler, P., and Magney, T.: Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP, Remote Sensing of Environment, 209, 808–823, https://doi.org/10.1016/j.rse.2018.02.016, 2018.
Toon, G., Blavier, J.-F., Washenfelder, R., Wunch, D., Keppel-Aleks, G., Wennberg, P., Connor, B., Sherlock, V., Griffith, D., Deutscher, N., and Notholt, J.: Total Column Carbon Observing Network (TCCON), Advances in Imaging, Vancouver, 2009/04/26, JMA3, https://opg.optica.org/abstract.cfm?uri=hisense-2009-JMA3 (last access: 20 October 2025), 2009.
Vermote, E. and Wolfe, R.: MYD09GA MODIS/Aqua Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid V006, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD09GA.006, 2015a.
Vermote, E. and Wolfe, R.: MOD09GQ MODIS/Terra Surface Reflectance Daily L2G Global 250 m SIN Grid V006, https://doi.org/10.5067/MODIS/MYD09GA.006, 2015b.
Vogel, F. R., Thiruchittampalam, B., Theloke, J., Kretschmer, R., Gerbig, C., Hammer, S., and Levin, I.: Can we evaluate a fine-grained emission model using high-resolution atmospheric transport modelling and regional fossil fuel CO2 observations?, Tellus B, 65, 18681, https://doi.org/10.3402/tellusb.v65i0.18681, 2013.
Wang, H., Jiang, F., Wang, J., Ju, W., and Chen, J. M.: Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO2 retrievals, Atmos. Chem. Phys., 19, 12067–12082, https://doi.org/10.5194/acp-19-12067-2019, 2019.
Wang, J. S., Kawa, S. R., Eluszkiewicz, J., Baker, D. F., Mountain, M., Henderson, J., Nehrkorn, T., and Zaccheo, T. S.: A regional CO2 observing system simulation experiment for the ASCENDS satellite mission, Atmos. Chem. Phys., 14, 12897–12914, https://doi.org/10.5194/acp-14-12897-2014, 2014.
Wang, Q., Mustafa, F., Bu, L., Zhu, S., Liu, J., and Chen, W.: Atmospheric carbon dioxide measurement from aircraft and comparison with OCO-2 and CarbonTracker model data, Atmos. Meas. Tech., 14, 6601–6617, https://doi.org/10.5194/amt-14-6601-2021, 2021.
Wang, W., Li, B., and Chen, B.: Improved surface NO2 Retrieval: Double-layer machine learning model construction and spatio-temporal characterization analysis in China (2018–2023), Journal of Environmental Management, 384, 125439, https://doi.org/10.1016/j.jenvman.2025.125439, 2025.
Watson, A. J., Schuster, U., Bakker, D. C. E., Bates, N. R., Corbière, A., González-Dávila, M., Friedrich, T., Hauck, J., Heinze, C., Johannessen, T., Körtzinger, A., Metzl, N., Olafsson, J., Olsen, A., Oschlies, A., Padin, X. A., Pfeil, B., Santana-Casiano, J. M., Steinhoff, T., Telszewski, M., Rios, A. F., Wallace, D. W. R., and Wanninkhof, R.: Tracking the Variable North Atlantic Sink for Atmospheric CO2, Science, 326, 1391–1393, https://doi.org/10.1126/science.1177394, 2009.
Wei, D., Reinmann, A., Schiferl, L. D., and Commane, R.: High resolution modeling of vegetation reveals large summertime biogenic CO2 fluxes in New York City, Environmental Research Letters, 17, 124031, https://doi.org/10.1088/1748-9326/aca68f, 2022.
Winbourne, J. B., Smith, I. A., Stoynova, H., Kohler, C., Gately, C. K., Logan, B. A., Reblin, J., Reinmann, A., Allen, D. W., and Hutyra, L. R.: Quantification of Urban Forest and Grassland Carbon Fluxes Using Field Measurements and a Satellite-Based Model in Washington DC/Baltimore Area, Journal of Geophysical Research: Biogeosciences, 127, e2021JG006568, https://doi.org/10.1029/2021JG006568, 2022.
Wu, D., Lin, J. C., Fasoli, B., Oda, T., Ye, X., Lauvaux, T., Yang, E. G., and Kort, E. A.: A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”), Geosci. Model Dev., 11, 4843–4871, https://doi.org/10.5194/gmd-11-4843-2018, 2018.
Wu, D., Lin, J. C., Duarte, H. F., Yadav, V., Parazoo, N. C., Oda, T., and Kort, E. A.: A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1), Geosci. Model Dev., 14, 3633–3661, https://doi.org/10.5194/gmd-14-3633-2021, 2021.
Xiang, C., Ma, X., Zhang, X., Han, G., Zhang, W., Chen, B., Liang, A., and Gong, W.: Design of Inversion Procedure for the Airborne CO2-IPDA LIDAR: A Preliminary Study, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 11840–11852, https://doi.org/10.1109/JSTARS.2021.3127564, 2021.
Yang, J., Gan, R., Luo, B., Wang, A., Shi, S., and Du, L.: An improved method for individual tree segmentation in complex urban scenes based on using multispectral LiDAR by deep learning, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 6561–6576, 2024.
Ye, X., Lauvaux, T., Kort, E. A., Oda, T., Feng, S., Lin, J. C., Yang, E. G., and Wu, D.: Constraining Fossil Fuel CO2 Emissions From Urban Area Using OCO-2 Observations of Total Column CO2, Journal of Geophysical Research: Atmospheres, 125, e2019JD030528, https://doi.org/10.1029/2019JD030528, 2020.
Zeng, J.: A Data-driven Upscale Product of Global Gross Primary Production, Net Ecosystem Exchange and Ecosystem Respiration, ver.2020.2, Center for Global Environmental Research NIES [data set], https://doi.org/10.17595/20200227.001, 2020.
Zhang, H., Han, G., Chen, W., Pei, Z., Liu, B., Liu, J., Zhang, T., Li, S., and Gong, W.: Validation Method for Spaceborne IPDA LIDAR XCO2 Products via TCCON, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 16984–16992, https://doi.org/10.1109/JSTARS.2024.3418028, 2024.
Zhou, M., Wang, P., Kumps, N., Hermans, C., and Nan, W.: TCCON data from Xianghe, China, Release GGG2020.R0 (Version R0), CaltechDATA [data set], https://doi.org/10.14291/tccon.ggg2020.xianghe01.R0, 2022.
Zhu, Y., Liu, J., Chen, X., Zhu, X., Bi, D., and Chen, W.: Sensitivity analysis and correction algorithms for atmospheric CO2 measurements with 1.57-µm airborne double-pulse IPDA LIDAR, Opt. Express, 27, 32679–32699, https://doi.org/10.1364/OE.27.032679, 2019.
Zhu, Y., Yang, J., Chen, X., Zhu, X., Zhang, J., Li, S., Sun, Y., Hou, X., Bi, D., Bu, L., Zhang, Y., Liu, J., and Chen, W.: Airborne Validation Experiment of 1.57-µm Double-Pulse IPDA LIDAR for Atmospheric Carbon Dioxide Measurement, Remote Sensing, 12, 1999, https://doi.org/10.3390/rs12121999, 2020.
Short summary
The inventory overestimated emissions in Beijing and Riyadh by 10–20 % and underestimated emissions in Cairo by 10–30 %. The biosphere flux has a 20–40 % impact on emissions in Beijing during certain periods. Moreover, using night-time and day-time biosphere flux data separately can improve the simulation accuracy of the same orbit by 20–70 % compared to using daily average data.
The inventory overestimated emissions in Beijing and Riyadh by 10–20 % and underestimated...
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