{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:32:37Z","timestamp":1775057557925,"version":"3.50.1"},"reference-count":202,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42075130"],"award-info":[{"award-number":["42075130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source\/sink-research methods. The internationally recognized \u201ctop-down\u201d approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO2. It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth\u2019s carbon-cycle processes.<\/jats:p>","DOI":"10.3390\/rs16183394","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T08:04:09Z","timestamp":1726128249000},"page":"3394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7181-9935","authenticated-orcid":false,"given":"Kai","family":"Hu","sequence":"first","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4584-7630","authenticated-orcid":false,"given":"Xinyan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2257-0405","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5001-7415","authenticated-orcid":false,"given":"Pengfei","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6856-9274","authenticated-orcid":false,"given":"Ziran","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3564-399X","authenticated-orcid":false,"given":"Yao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mathematical, Physical and Computational Sciences, University of Reading, Whiteknights, P.O. Box 217, Reading RG6 6AH, Berkshire, UK"}]},{"given":"Shiqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Economic and Technical Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China"}]},{"given":"Yuanyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Economic and Technical Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China"}]},{"given":"Han","family":"Wang","sequence":"additional","affiliation":[{"name":"Economic and Technical Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China"}]},{"given":"Li","family":"Di","sequence":"additional","affiliation":[{"name":"State Grid Henan Electric Power Company, Zhengzhou 450003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4681-9129","authenticated-orcid":false,"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1175\/1520-0477(1997)078<0197:EAGMEB>2.0.CO;2","article-title":"Earth\u2019s Annual Global Mean Energy Budget","volume":"78","author":"Kiehl","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.5194\/essd-12-3269-2020","article-title":"Global Carbon Budget 2020","volume":"12","author":"Friedlingstein","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5301","DOI":"10.5194\/essd-15-5301-2023","article-title":"Global Carbon Budget 2023","volume":"15","author":"Friedlingstein","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_4","first-page":"4","article-title":"Interpretation of \u201cIPCC 2006 National Greenhouse Gas Inventory Guidelines 2019 Revised Edition\u201d","volume":"37","author":"Cai","year":"2019","journal-title":"Environ. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8339","DOI":"10.1109\/JSTARS.2024.3355549","article-title":"A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data","volume":"17","author":"Hu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hu, K., Zhang, Q., Feng, X., Liu, Z., Shao, P., Xia, M., and Ye, X. (2024). An Interpolation and Prediction Algorithm for XCO2 based on Multi-source Time Series Data. Remote Sens., 16.","DOI":"10.3390\/rs16111907"},{"key":"ref_7","first-page":"1","article-title":"Overview of atmospheric CO2 remote sensing from space","volume":"19","author":"Chen","year":"2015","journal-title":"J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s11430-015-0239-7","article-title":"Space- and ground-based CO2 measurements: A review","volume":"59","author":"Yue","year":"2016","journal-title":"Sci. China-Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5635","DOI":"10.5194\/bg-15-5635-2018","article-title":"The impact of spatiotemporal variability in atmospheric CO2 concentration on global terrestrial carbon fluxes","volume":"15","author":"Lee","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"28968","DOI":"10.3402\/tellusb.v68.28968","article-title":"Decadal trends in the seasonal-cycle amplitude of terrestrial CO2 exchange resulting from the ensemble of terrestrial biosphere models","volume":"68","author":"Ito","year":"2016","journal-title":"Tellus Chem. Phys. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.5194\/bg-8-2027-2011","article-title":"Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction","volume":"8","author":"Poulter","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_12","first-page":"53","article-title":"Satellite remote sensing of greenhouse gases: Progress and trends","volume":"25","author":"Liu","year":"2021","journal-title":"Yaogan Xuebao\/J. Remote Sens."},{"key":"ref_13","first-page":"581","article-title":"Advances in atmospheric observation techniques for greenhouse gases by satellite remote sensing","volume":"17","author":"Yang","year":"2022","journal-title":"J. Atmos. Environ. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"243","DOI":"10.11834\/jrs.20221806","article-title":"Satellite remote sensing for global stocktaking: Methods, progress and perspectives","volume":"26","author":"Liu","year":"2022","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.5194\/amt-12-1513-2019","article-title":"Building the COllaborative Carbon Column Observing Network (COCCON): Long-term stability and ensemble performance of the EM27\/SUN Fourier transform spectrometer","volume":"12","author":"Frey","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_16","first-page":"2087","article-title":"The Total Carbon Column Observing Network","volume":"369","author":"Wunch","year":"2011","journal-title":"Philos. Trans. R. Soc. Math. Phys. Eng. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.5194\/amt-9-2303-2016","article-title":"Addition of a channel for XCO2 observations to a portable FTIR spectrometer for greenhouse gas measurements","volume":"9","author":"Hase","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0273-1177(94)90343-3","article-title":"IMG, interferometric measurement of greenhouse gases from space","volume":"14","author":"Ogawa","year":"1994","journal-title":"Adv. Space Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/36.763283","article-title":"Retrieval of CO columns from IMG\/ADEOS spectra","volume":"37","author":"Clerbaux","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1029\/1999GL011059","article-title":"Simultaneous inversion for temperature and water vapor from IMG radiances","volume":"27","author":"Lubrano","year":"2000","journal-title":"Geophys. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/TGRS.2002.808356","article-title":"AIRS\/AMSU\/HSB on the Aqua mission: Design, science objectives, data products, and processing systems","volume":"41","author":"Aumann","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112032","DOI":"10.1016\/j.rse.2020.112032","article-title":"OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals","volume":"251","author":"Taylor","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/s00376-018-7312-6","article-title":"First Global Carbon Dioxide Maps Produced from TanSat Measurements","volume":"35","author":"Yang","year":"2018","journal-title":"Adv. Atmos. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.5194\/amt-14-1167-2021","article-title":"Anthropogenic CO2 monitoring satellite mission: The need for multi-angle polarimetric observations","volume":"14","author":"Rusli","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.5194\/amt-7-1723-2014","article-title":"The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparative validation of GHG-CCI SCIAMACHY\/ENVISAT and TANSO-FTS\/GOSAT CO2 and CH4 retrieval algorithm products with measurements from the TCCON","volume":"7","author":"Dils","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.rse.2013.04.024","article-title":"The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets","volume":"162","author":"Buchwitz","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"(2023, June 25). CO2_SCI_WFMD. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/e493802d83c846c8b76f817866fb74cc."},{"key":"ref_28","unstructured":"(2023, June 25). CO2_SCI_BESD. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/294b4075ddbc4464bb06742816813bdc."},{"key":"ref_29","unstructured":"(2023, June 25). CO2_GOS_OCFP. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/9255faeb392f41debf5402caa40dada8."},{"key":"ref_30","unstructured":"(2023, June 25). CO2_EMMA. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/9f002827ba7d48f59019fcfd3577a57e."},{"key":"ref_31","unstructured":"(2024, June 25). CO2_GO2_ACOS, Available online: https:\/\/daac.gsfc.nasa.gov\/datasets\/ACOS_L2_Lite_FP_9r."},{"key":"ref_32","unstructured":"(2023, June 25). CO2_GO2_SRFP. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/169c76a05fa247eebc5ee53f239871a7."},{"key":"ref_33","unstructured":"(2023, June 25). CO2_GO2_NIES, Available online: https:\/\/data2.gosat.nies.go.jp."},{"key":"ref_34","unstructured":"(2023, June 25). CO2_TAN_OCFP. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/2cc63301f1854239aa61c70e58c61207."},{"key":"ref_35","unstructured":"(2024, June 25). CO2_OC2_ACOS, Available online: https:\/\/daac.gsfc.nasa.gov\/datasets\/OCO2_L2_Lite_FP_11.1r."},{"key":"ref_36","unstructured":"(2023, June 25). CO2_OC2_FOCA. Available online: https:\/\/catalogue.ceda.ac.uk\/uuid\/070522ac6a5d4973a95c544beef714b4."},{"key":"ref_37","unstructured":"(2024, June 25). CO2_OC3_ACOS, Available online: https:\/\/daac.gsfc.nasa.gov\/datasets\/OCO3_L2_Lite_FP_10.4r."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"543","DOI":"10.5194\/acp-11-543-2011","article-title":"A very high-resolution (1 km \u00d7 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights","volume":"11","author":"Oda","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5213","DOI":"10.5194\/essd-13-5213-2021","article-title":"A comprehensive and synthetic dataset for global, regional, and national greenhouse gas emissions by sector 1970\u20132018 with an extension to 2019","volume":"13","author":"Minx","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1093\/nsr\/nwx150","article-title":"Anthropogenic emission inventories in China: A review","volume":"4","author":"Li","year":"2017","journal-title":"Natl. Sci. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1002\/asi.22968","article-title":"Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis","volume":"65","author":"Chen","year":"2014","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1517\/14712598.2014.920813","article-title":"Emerging trends and new developments in regenerative medicine: A scientometric update (2000\u20132014)","volume":"14","author":"Chen","year":"2014","journal-title":"Expert Opin. Biol. Ther."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rodgers, C.D. (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific.","DOI":"10.1142\/9789812813718"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.asr.2003.08.062","article-title":"The Orbiting Carbon Observatory (OCO) mission","volume":"34","author":"Crisp","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"D05305","DOI":"10.1029\/2006JD008336","article-title":"Orbiting Carbon Observatory: Inverse method and prospective error analysis","volume":"113","author":"Connor","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.jqsrt.2007.12.015","article-title":"The GEISA spectroscopic database: Current and future archive for Earth and planetary atmosphere studies","volume":"109","author":"Scott","year":"2008","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6762","DOI":"10.1364\/AO.45.006762","article-title":"Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance","volume":"45","author":"Kotchenova","year":"2006","journal-title":"Appl. Opt."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1175\/1520-0450(1981)020<0802:AFLBLM>2.0.CO;2","article-title":"A Fast Line-by-Line Method for Atmospheric Absorption Computations: The Automatized Atmospheric Absorption Atlas","volume":"20","author":"Scott","year":"1981","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_49","unstructured":"Kneizys, F.X. (2024, June 25). Users Guide to LOWTRAN 7. Air Force Geophysics Lab. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/1988ugls.rept.....K."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0034-4257(98)00045-5","article-title":"MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS","volume":"65","author":"Berk","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.asr.2005.03.012","article-title":"SCIATRAN 2.0\u2014A new radiative transfer model for geophysical applications in the 175\u20132400 nm spectral region","volume":"36","author":"Rozanov","year":"2005","journal-title":"Adv. Space Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7397","DOI":"10.5194\/acp-9-7397-2009","article-title":"Performance of the line-by-line radiative transfer model (LBLRTM) for temperature and species retrievals: IASI case studies from JAIVEx","volume":"9","author":"Shephard","year":"2009","journal-title":"Atmos. Chem. Phys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/S0022-4073(99)00115-6","article-title":"Angular distribution of the Stokes vector in a plane-parallel, vertically inhomogeneous medium in the vector discrete ordinate radiative transfer (VDISORT) model","volume":"65","author":"Schulz","year":"2000","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1364\/AO.27.002502","article-title":"Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media","volume":"27","author":"Stamnes","year":"1988","journal-title":"Appl. Opt."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.jqsrt.2014.08.019","article-title":"LINTRAN v2.0: A linearised vector radiative transfer model for efficient simulation of satellite-born nadir-viewing reflection measurements of cloudy atmospheres","volume":"149","author":"Schepers","year":"2014","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.5194\/gmd-11-2717-2018","article-title":"An update on the RTTOV fast radiative transfer model (currently at version 12)","volume":"11","author":"Saunders","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1029\/JC084iC10p06329","article-title":"Simultaneous measurement of atmospheric CH2O, O3, and NO2 by differential optical absorption","volume":"84","author":"Platt","year":"1979","journal-title":"J. Geophys. Res. Ocean"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Buchwitz, M., Beek, R., Noel, S., Burrows, J., and Bovensmann, H. (2006). Carbon Monoxide, Methane and Carbon Dioxide over China Retrieved from SCIAMACHY\/ENVISAT by WFM-DOAS, European Space Agency. Special Publication, ESA SP.","DOI":"10.5194\/acpd-5-1943-2005"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.5194\/acp-5-3313-2005","article-title":"Carbon monoxide, methane and carbon dioxide columns retrieved from SCIAMACHY by WFM-DOAS: Year 2003 initial data set","volume":"5","author":"Buchwitz","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"15231","DOI":"10.1029\/2000JD900191","article-title":"A near-infrared optimized DOAS method for the fast global retrieval of atmospheric CH4, CO, CO2, H2O, and N2O total column amounts from SCIAMACHY Envisat-1 nadir radiances","volume":"105","author":"Buchwitz","year":"2000","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"130703","DOI":"10.7498\/aps.62.130703","article-title":"Measurement of atmospheric CO2 vertical column density using weighting function modified differential optical absorption spectroscopy","volume":"62","author":"Sun","year":"2013","journal-title":"Acta Phys. Sin."},{"key":"ref_62","unstructured":"Huo, Y. (2015). Ground-Based Observation and CO2 Retrieval of Ultra-Fine Solar Spectra in the Near-Infrared Band. [Ph.D. Thesis, Lanzhou University]."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/acp-5-9-2005","article-title":"Iterative maximum a posteriori (IMAP)-DOAS for retrieval of strongly absorbing trace gases: Model studies for CH4 and CO2 retrieval from near infrared spectra of SCIAMACHY onboard ENVISAT","volume":"5","author":"Frankenberg","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.5194\/acp-6-3517-2006","article-title":"Measuring atmospheric CO2 from space using Full Spectral Initiation (FSI) WFM-DOAS","volume":"6","author":"Barkley","year":"2006","journal-title":"Atmos. Chem. Phys."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.5194\/acp-11-2863-2011","article-title":"Long-term analysis of carbon dioxide and methane column-averaged mole fractions retrieved from SCIAMACHY","volume":"11","author":"Schneising","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"D19207","DOI":"10.1029\/2009JD012116","article-title":"An improved photon path length probability density function\u2013based radiative transfer model for space-based observation of greenhouse gases","volume":"114","author":"Oshchepkov","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.5194\/amt-5-2375-2012","article-title":"SCIAMACHY WFM-DOAS XCO2: Reduction of scattering related errors","volume":"5","author":"Heymann","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2961","DOI":"10.5194\/amt-8-2961-2015","article-title":"Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm","volume":"8","author":"Heymann","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"717","DOI":"10.5194\/amt-4-717-2011","article-title":"Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite","volume":"4","author":"Yoshida","year":"2011","journal-title":"Atmos. Meas. Tech."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"D07206","DOI":"10.1029\/2008JD010710","article-title":"Development of an unbiased cloud detection algorithm for a spaceborne multispectral imager","volume":"114","author":"Ishida","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1002\/jgrd.50146","article-title":"Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space. Part 2: Algorithm intercomparison in the GOSAT data processing for CO2 retrievals over TCCON sites","volume":"118","author":"Oshchepkov","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.5194\/amt-16-1477-2023","article-title":"Update on the GOSAT TANSO\u2013FTS SWIR Level 2 retrieval algorithm","volume":"16","author":"Someya","year":"2023","journal-title":"Atmos. Meas. Tech."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"99","DOI":"10.5194\/amt-5-99-2012","article-title":"The ACOS CO2 retrieval algorithm\u2014Part 1: Description and validation against synthetic observations","volume":"5","author":"Connor","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"687","DOI":"10.5194\/amt-5-687-2012","article-title":"The ACOS CO2 retrieval algorithm\u2014Part II: Global XCO2 data characterization","volume":"5","author":"Crisp","year":"2012","journal-title":"Atmos. Meas. Tech."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.5194\/amt-16-3173-2023","article-title":"Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm","volume":"16","author":"Taylor","year":"2023","journal-title":"Atmos. Meas. Tech."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"270","DOI":"10.3390\/rs3020270","article-title":"Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission","volume":"3","author":"Boesch","year":"2011","journal-title":"Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"D21301","DOI":"10.1029\/2012JD018087","article-title":"Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite (GOSAT): Comparison with ground-based TCCON observations and GEOS-Chem model calculations","volume":"117","author":"Cogan","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1364\/AO.48.003322","article-title":"Retrievals of atmospheric CO2 from simulated space-borne measurements of backscattered near-infrared sunlight: Accounting for aerosol effects","volume":"48","author":"Butz","year":"2009","journal-title":"Appl. Opt."},{"key":"ref_79","first-page":"541","article-title":"Study on the Spatiotemporal Distribution of Carbon Dioxide Concentration in China Based on GOSAT Inversion","volume":"40","author":"Yang","year":"2016","journal-title":"J. Atmos. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"D10307","DOI":"10.1029\/2012JD017549","article-title":"Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: Performance comparison of proxy and physics retrieval algorithms","volume":"117","author":"Schepers","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Lu, S., Landgraf, J., Fu, G., van Diedenhoven, B., Wu, L., Rusli, S.P., and Hasekamp, O.P. (2022). Simultaneous Retrieval of Trace Gases, Aerosols, and Cirrus Using RemoTAP\u2014The Global Orbit Ensemble Study for the CO2M Mission. Front. Remote Sens., 3.","DOI":"10.3389\/frsen.2022.914378"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1007\/s11434-015-0953-2","article-title":"An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations","volume":"60","author":"Yang","year":"2015","journal-title":"Sci. Bull."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s00376-020-0297-y","article-title":"A New TanSat XCO2 Global Product towards Climate Studies","volume":"38","author":"Yang","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1016\/j.jqsrt.2012.05.021","article-title":"Atmospheric validation of high accuracy CO2 absorption coefficients for the OCO-2 mission","volume":"113","author":"Thompson","year":"2012","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1016\/j.scib.2018.08.004","article-title":"The TanSat mission: Preliminary global observations","volume":"63","author":"Liu","year":"2018","journal-title":"Sci. Bull."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"D23210","DOI":"10.1029\/2008JD010061","article-title":"PPDF-based method to account for atmospheric light scattering in observations of carbon dioxide from space","volume":"113","author":"Oshchepkov","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_87","first-page":"26","article-title":"A Method for Carbon Dioxide Retrieval Based on Statistics and Path Length Distribution","volume":"37","author":"Duan","year":"2017","journal-title":"Acta Opt. Sin."},{"key":"ref_88","first-page":"202","article-title":"CO2 Statistical Inversion Method Based on Principal Component Analysis","volume":"12","author":"Sang","year":"2017","journal-title":"J. Atmos. Environ. Opt."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3837","DOI":"10.5194\/amt-14-3837-2021","article-title":"XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm","volume":"14","author":"Reuter","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"107724","DOI":"10.1016\/j.jqsrt.2021.107724","article-title":"Efficient two-dimensional scalar fields reconstruction of laminar flames from infrared hyperspectral measurements with a machine learning approach","volume":"271","author":"Ren","year":"2021","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"16368","DOI":"10.3390\/s121216368","article-title":"Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data","volume":"12","author":"Guo","year":"2012","journal-title":"Sensors"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/72.165599","article-title":"Feedback stabilization using two-hidden-layer nets","volume":"3","author":"Sontag","year":"1992","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_93","first-page":"4581","article-title":"First global measurement of midtropospheric CO2 from NOAA polar satellites: Tropical zone","volume":"108","author":"Serrar","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"L17106","DOI":"10.1029\/2004GL020141","article-title":"Midtropospheric CO2 concentration retrieval from AIRS observations in the tropics","volume":"31","author":"Crevoisier","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"He, Z., Lei, L., Zhang, Y., Sheng, M., Wu, C., Li, L., Zeng, Z.C., and Welp, L.R. (2020). Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method. Remote Sens., 12.","DOI":"10.3390\/rs12030576"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"D21301","DOI":"10.1029\/2004JD004821","article-title":"Operational trace gas retrieval algorithm for the Infrared Atmospheric Sounding Interferometer","volume":"109","author":"Turquety","year":"2004","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"6337","DOI":"10.5194\/acp-9-6337-2009","article-title":"Tropospheric methane in the tropics\u2014First year from IASI hyperspectral infrared observations","volume":"9","author":"Crevoisier","year":"2009","journal-title":"Atmos. Chem. Phys."},{"key":"ref_98","first-page":"99","article-title":"Rapid Algorithm for Hyperspectral Thermal Infrared Radiation Transmission Model Based on Neural Networks","volume":"33","author":"Wu","year":"2010","journal-title":"J. Arid. Land Geogr."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1175\/JTECH-D-13-00137.1","article-title":"A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network","volume":"31","author":"Zeng","year":"2014","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jenvman.2019.05.049","article-title":"Spatial distribution of XCO2 using OCO-2 data in growing seasons","volume":"244","author":"Siabi","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.jqsrt.2016.12.005","article-title":"EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations","volume":"189","author":"Bril","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_102","first-page":"1223","article-title":"Algorithm for Atmospheric CO2 Inversion of Beijing Urban Underlying Surface","volume":"23","author":"Wu","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"L05407","DOI":"10.1029\/2007GL032568","article-title":"Quality assessment of BRDF\/albedo retrievals in MODIS operational system","volume":"35","author":"Shuai","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.5194\/amt-15-5219-2022","article-title":"On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements","volume":"15","author":"David","year":"2022","journal-title":"Atmos. Meas. Tech."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"117","DOI":"10.5194\/amt-14-117-2021","article-title":"XCO2 estimates from the OCO-2 measurements using a neural network approach","volume":"14","author":"David","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"108006","DOI":"10.1016\/j.jqsrt.2021.108006","article-title":"Atmospheric CO2 retrieval from satellite spectral measurements by a two-step machine learning approach","volume":"278","author":"Zhao","year":"2022","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.jqsrt.2013.07.002","article-title":"The HITRAN2012 molecular spectroscopic database","volume":"130","author":"Rothman","year":"2013","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jqsrt.2019.04.031","article-title":"The impact of various HITRAN molecular spectroscopic databases on infrared radiative transfer simulation","volume":"234","author":"Zhu","year":"2019","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"108441","DOI":"10.1016\/j.jqsrt.2022.108441","article-title":"A machine learning based line-by-line absorption coefficient model for the application of atmospheric carbon dioxide remote sensing","volume":"296","author":"Xie","year":"2023","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_110","first-page":"20","article-title":"CO2 satellite inversion methocl based on machine learning","volume":"43","author":"Miao","year":"2023","journal-title":"China Environ. Sci."},{"key":"ref_111","first-page":"42","article-title":"Implementation of Embedded CO2 Concentration Inversion Algorithm Based on Deep Learning","volume":"44","author":"Wang","year":"2023","journal-title":"Laser J."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Jin, Z., Tian, X., Duan, M., and Han, R. (2021). An Efficient Algorithm for Retrieving CO2 in the Atmosphere From Hyperspectral Measurements of Satellites: Application of NLS-4DVar Data Assimilation Method. Front. Earth Sci., 9.","DOI":"10.3389\/feart.2021.688542"},{"key":"ref_113","unstructured":"Zhao, L. (2018). Remote Sensing Inversion of Atmospheric CO2 and CH4 Based on GOSAT Satellite. [Ph.D. Thesis, Jilin University]."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1600-0889.2006.00235.x","article-title":"Global monthly CO2 flux inversion with a focus over North America","volume":"59","author":"Deng","year":"2007","journal-title":"Tellus B"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"18925","DOI":"10.1073\/pnas.0708986104","article-title":"An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker","volume":"104","author":"Peters","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"3515","DOI":"10.5194\/gmd-11-3515-2018","article-title":"CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations","volume":"11","author":"He","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"D24309","DOI":"10.1029\/2005JD006390","article-title":"Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data","volume":"110","author":"Chevallier","year":"2005","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s00376-019-8150-x","article-title":"Evaluation of Simulated CO2 Concentrations from the CarbonTracker-Asia Model Using In-situ Observations over East Asia for 2009\u20132013","volume":"36","author":"Kenea","year":"2019","journal-title":"Adv. Atmos. Sci."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1111\/j.1365-2486.2009.02078.x","article-title":"Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations","volume":"16","author":"Peters","year":"2010","journal-title":"Glob. Chang. Biol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"5807","DOI":"10.5194\/acp-14-5807-2014","article-title":"Estimating Asian terrestrial carbon fluxes from CONTRAIL aircraft and surface CO2 observations for the period 2006\u20132010","volume":"14","author":"Zhang","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.5194\/gmd-10-2785-2017","article-title":"The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: Implementation and global carbon balance 2001\u20132015","volume":"10","author":"Tsuruta","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_122","unstructured":"Kim, J., Kim, H.M., Cho, C.H., and Boo, K.O. (2024, June 25). Estimation of Surface CO2 Flux Using a Carbon Tracking System Based on Ensemble Kalman Filter. AGU Fall Meeting Abstracts. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2015AGUFM.B23G0666K\/abstract."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"5311","DOI":"10.5194\/bg-10-5311-2013","article-title":"Nested atmospheric inversion for the terrestrial carbon sources and sinks in China","volume":"10","author":"Jiang","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.1002\/2014JG002792","article-title":"Global carbon-assimilation system using a local ensemble Kalman filter with multiple ecosystem models","volume":"119","author":"Zhang","year":"2014","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1007\/s11434-014-0238-1","article-title":"The Chinese carbon cycle data-assimilation system (Tan-Tracker)","volume":"59","author":"Tian","year":"2014","journal-title":"Chin. Sci. Bull."},{"key":"ref_126","unstructured":"Lu, L. (2020). Development of Regional High-Resolution Carbon Assimilation System and Research on Anthropogenic Carbon Emission Estimation. [Ph.D. Thesis, China University of Mining and Technology]."},{"key":"ref_127","first-page":"747","article-title":"A Review of the Research Status of Data Assimilation Algorithms","volume":"27","author":"Ma","year":"2012","journal-title":"Adv. Earth Sci."},{"key":"ref_128","first-page":"24","article-title":"Advances in Observation, Simulation, and Assimilation of Surface Net Radiation Flux","volume":"23","author":"Zhao","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_129","unstructured":"Zou, X. (2009). The Theory and Application of Data Assimilation, China Meteorological Press."},{"key":"ref_130","first-page":"386","article-title":"Objective weather-map analysis","volume":"6","author":"Panofsky","year":"1949","journal-title":"J. Atmos. Sci."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"272","DOI":"10.3402\/tellusa.v7i2.8775","article-title":"Routine Forecasting with the Barotropic Model","volume":"7","author":"Bergthorsson","year":"1955","journal-title":"Tellus"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1111\/j.1600-0870.1986.tb00459.x","article-title":"Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects","volume":"38A","author":"Dimet","year":"1986","journal-title":"Tellus A"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"11143","DOI":"10.1073\/pnas.97.21.11143","article-title":"Data assimilation and its applications","volume":"97","author":"Wang","year":"2000","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"289","DOI":"10.2151\/jmsj1965.75.1B_289","article-title":"Advances in Sequential Estimation for Atmospheric and Oceanic Flows (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice)","volume":"75","author":"Ghil","year":"1997","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_135","unstructured":"Julier, S.J., Uhlmann, J.K., and Durrant-Whyte, H.F. (1995, January 21\u201323). A new approach for filtering nonlinear systems. Proceedings of the 1995 American Control Conference\u2014ACC\u201995, Seattle, WA, USA."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.1029\/94JC00572","article-title":"Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics","volume":"99","author":"Evensen","year":"1994","journal-title":"J. Geophys. Res."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1175\/1520-0493(2001)129<0420:ASWTET>2.0.CO;2","article-title":"Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects","volume":"129","author":"Bishop","year":"2001","journal-title":"Mon. Weather Rev."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1175\/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2","article-title":"Ensemble Data Assimilation without Perturbed Observations","volume":"130","author":"Whitaker","year":"2002","journal-title":"Mon. Weather Rev."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1175\/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2","article-title":"An Ensemble Adjustment Kalman Filter for Data Assimilation","volume":"129","author":"Anderson","year":"2001","journal-title":"Mon. Weather Rev."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"415","DOI":"10.3402\/tellusa.v56i5.14462","article-title":"A local ensemble Kalman filter for atmospheric data assimilation","volume":"56","author":"Ott","year":"2004","journal-title":"Tellus A"},{"key":"ref_141","first-page":"W06416","article-title":"Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter\u2013based steady state Kalman filter","volume":"44","author":"Mynett","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1175\/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2","article-title":"The National Meteorological Center\u2019s Spectral Statistical-Interpolation Analysis System","volume":"120","author":"Parrish","year":"1992","journal-title":"Mon. Weather Rev."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1002\/qj.49711347812","article-title":"Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory","volume":"113","author":"Talagrand","year":"1987","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Lahoz, W., Khattatov, B., and Menard, R. (2010). Bias Estimation. Data Assimilation: Making Sense of Observations, Springer.","DOI":"10.1007\/978-3-540-74703-1"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"D24304","DOI":"10.1029\/2005JD006157","article-title":"An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations","volume":"110","author":"Peters","year":"2005","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_146","unstructured":"Zhu, L. (2005). Application Research of Background Field Error Covariance Estimation Technique. [Master\u2019s Thesis, Nanjing University of Information Science and Technology]."},{"key":"ref_147","unstructured":"Evensen, G. (2005). Data Assimilation. The Ensemble Kalman Filter, Springer. Available online: https:\/\/link.springer.com\/book\/10.1007\/978-3-642-03711-5."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Liang, S., Li, X., and Xie, X. (2013). Land Surface Observation, Modeling, and Data Assimilation, World Scientific.","DOI":"10.1142\/8768"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1175\/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2","article-title":"A Hybrid Ensemble Kalman Filter\u20133D Variational Analysis Scheme","volume":"128","author":"Hamill","year":"2000","journal-title":"Mon. Weather Rev."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.ocemod.2003.12.004","article-title":"Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter","volume":"8","author":"Annan","year":"2005","journal-title":"Ocean Model."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"128105","DOI":"10.1016\/j.neucom.2024.128015","article-title":"An overview:Attention mechanisms in multi-agent reinforcement learning","volume":"598","author":"Hu","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"128068","DOI":"10.1016\/j.neucom.2024.128068","article-title":"A Review of Research on Reinforcement Learning Algorithms for Multi-Agent","volume":"599","author":"Hu","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"101171","DOI":"10.1016\/j.jocs.2020.101171","article-title":"Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model","volume":"44","author":"Brajard","year":"2020","journal-title":"J. Comput. Sci."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"101468","DOI":"10.1016\/j.jocs.2021.101468","article-title":"A comparison of combined data assimilation and machine learning methods for offline and online model error correction","volume":"55","author":"Farchi","year":"2021","journal-title":"J. Comput. Sci."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"305","DOI":"10.3934\/fods.2020015","article-title":"Online learning of both state and dynamics using ensemble Kalman filters","volume":"3","author":"Bocquet","year":"2021","journal-title":"Found. Data Sci."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1002\/qj.4297","article-title":"State, global, and local parameter estimation using local ensemble Kalman filters: Applications to online machine learning of chaotic dynamics","volume":"148","author":"Malartic","year":"2022","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"e2022MS003474","DOI":"10.1029\/2022MS003474","article-title":"Online Model Error Correction With Neural Networks in the Incremental 4D-Var Framework","volume":"15","author":"Farchi","year":"2023","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.1016\/j.eswa.2009.11.054","article-title":"Parameter identification of chaotic dynamic systems through an improved particle swarm optimization","volume":"37","author":"Modares","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"101525","DOI":"10.1016\/j.jocs.2021.101525","article-title":"Data Learning: Integrating Data Assimilation and Machine Learning","volume":"58","author":"Buizza","year":"2022","journal-title":"J. Comput. Sci."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"e2022MS003208","DOI":"10.1029\/2022MS003208","article-title":"Regional CO2 Inversion Through Ensemble-Based Simultaneous State and Parameter Estimation: TRACE Framework and Controlled Experiments","volume":"15","author":"Chen","year":"2023","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"13149","DOI":"10.1007\/s00521-021-06739-4","article-title":"Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks","volume":"34","author":"Cheng","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"100179","DOI":"10.1016\/j.bdr.2020.100179","article-title":"A Data-Driven Method for Hybrid Data Assimilation with Multilayer Perceptron","volume":"23","author":"Huang","year":"2021","journal-title":"Big Data Res."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"83","DOI":"10.26599\/BDMA.2018.9020033","article-title":"Model error correction in data assimilation by integrating neural networks","volume":"2","author":"Zhu","year":"2019","journal-title":"Big Data Min. Anal."},{"key":"ref_164","unstructured":"Peckham, S.E., Grell, G.A., McKeen, S.A., Ahmadov, R., Wong, K.Y., Barth, M., Pfister, G., Wiedinmyer, C., Fast, J.D., and Gustafson, W.I. (2024, June 25). WRF-Chem Version 3.8.1 User\u2019s Guide, Available online: https:\/\/repository.library.noaa.gov\/view\/noaa\/14945."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.5194\/gmd-10-1703-2017","article-title":"Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1","volume":"10","author":"Appel","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.5194\/gmd-10-2397-2017","article-title":"CHIMERE-2017: From urban to hemispheric chemistry-transport modeling","volume":"10","author":"Mailler","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s007030170015","article-title":"A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period","volume":"77","author":"Jacob","year":"2001","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"23073","DOI":"10.1029\/2001JD000807","article-title":"Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation","volume":"106","author":"Bey","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/gmd-3-43-2010","article-title":"Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)","volume":"3","author":"Emmons","year":"2010","journal-title":"Geosci. Model Dev."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"417","DOI":"10.5194\/acp-5-417-2005","article-title":"The two-way nested global chemistry-transport zoom model TM5: Algorithm and applications","volume":"5","author":"Krol","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"e2019MS001892","DOI":"10.1029\/2019MS001892","article-title":"LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics","volume":"12","author":"Hourdin","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"3502","DOI":"10.1002\/2016GL067828","article-title":"A method for independent validation of surface fluxes from atmospheric inversion: Application to CO2","volume":"43","author":"Liu","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"e2019JD031922","DOI":"10.1029\/2019JD031922","article-title":"Using Space-Based Observations and Lagrangian Modeling to Evaluate Urban Carbon Dioxide Emissions in the Middle East","volume":"125","author":"Yang","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"6735","DOI":"10.5194\/acp-16-6735-2016","article-title":"Analysis of the potential of near-ground measurements of CO2 and CH4 in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model","volume":"16","author":"Boon","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"14703","DOI":"10.5194\/acp-16-14703-2016","article-title":"A first year-long estimate of the Paris region fossil fuel CO2 emissions based on atmospheric inversion","volume":"16","author":"Staufer","year":"2016","journal-title":"Atmos. Chem. Phys. Discuss."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"2991","DOI":"10.5194\/acp-19-2991-2019","article-title":"Characterizing uncertainties in atmospheric inversions of fossil fuel CO2 emissions in California","volume":"19","author":"Brophy","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.5194\/gmd-13-2695-2020","article-title":"Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2","volume":"13","author":"Super","year":"2020","journal-title":"Geosci. Model Dev."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"166035","DOI":"10.1016\/j.scitotenv.2023.166035","article-title":"A high-resolution monitoring approach of urban CO2 fluxes. Part 2\u2014surface flux optimisation using eddy covariance observations","volume":"903","author":"Stagakis","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"160216","DOI":"10.1016\/j.scitotenv.2022.160216","article-title":"A high-resolution monitoring approach of urban CO2 fluxes. Part 1\u2014bottom-up model development","volume":"858","author":"Stagakis","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1111\/j.1365-2486.2005.00917.x","article-title":"Model\u2013data synthesis in terrestrial carbon observation: Methods, data requirements and data uncertainty specifications","volume":"11","author":"Raupach","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.5194\/acp-9-2619-2009","article-title":"Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter","volume":"9","author":"Feng","year":"2009","journal-title":"Atmos. Chem. Phys."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"8695","DOI":"10.5194\/acp-13-8695-2013","article-title":"Global CO2 fluxes estimated from GOSAT retrievals of total column CO2","volume":"13","author":"Basu","year":"2013","journal-title":"Atmos. Chem. Phys."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"9351","DOI":"10.5194\/acp-13-9351-2013","article-title":"Regional CO2 flux estimates for 2009\u20132010 based on GOSAT and ground-based CO2 observations","volume":"13","author":"Maksyutov","year":"2013","journal-title":"Atmos. Chem. Phys."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1002\/2013GL058772","article-title":"Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2","volume":"41","author":"Chevallier","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1002\/2015JD024157","article-title":"Combining GOSAT XCO2 observations over land and ocean to improve regional CO2 flux estimates","volume":"121","author":"Deng","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_186","unstructured":"Maki, T., Sekiyama, T.T., Miyoshi, T., Nakamura, T., and Iwasaki, T. (2024, June 25). Multiple Satellite Data Assimilation in Carbon Cycle Analysis Using a Local Ensemble Transform Kalman Filter (LETKF). AGU Fall Meeting Abstracts. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2016AGUFM.A31E0085M\/abstract."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"12063","DOI":"10.5194\/acp-20-12063-2020","article-title":"The regional European atmospheric transport inversion comparison, EUROCOM: First results on European-wide terrestrial carbon fluxes for the period 2006\u20132015","volume":"20","author":"Monteil","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.5194\/acp-21-1963-2021","article-title":"Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System","volume":"21","author":"Jiang","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"3200","DOI":"10.1007\/s11434-014-0348-9","article-title":"Development of CMAQ for East Asia CO2 data assimilation under an EnKF framework: A first result","volume":"59","author":"Huang","year":"2014","journal-title":"Chin. Sci. Bull."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"118106","DOI":"10.1016\/j.atmosenv.2020.118106","article-title":"Assimilation of OCO-2 retrievals with WRF-Chem\/DART: A case study for the Midwestern United States","volume":"246","author":"Zhang","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"5213","DOI":"10.1002\/2015JD024473","article-title":"High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX)","volume":"121","author":"Lauvaux","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"10724","DOI":"10.1038\/ncomms10724","article-title":"Top\u2013down assessment of the Asian carbon budget since the mid 1990s","volume":"7","author":"Thompson","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1007\/s13351-017-6149-8","article-title":"Accounting for CO2 variability over East Asia with a regional joint inversion system and its preliminary evaluation","volume":"31","author":"Kou","year":"2017","journal-title":"J. Meteorol. Res."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.5194\/gmd-11-1725-2018","article-title":"Development of the WRF-CO2 4D-Var assimilation system v1.0","volume":"11","author":"Zheng","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.5194\/gmd-14-3383-2021","article-title":"Regional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0","volume":"14","author":"Monteil","year":"2021","journal-title":"Geosci. Model Dev."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"649","DOI":"10.5194\/gmd-15-649-2022","article-title":"A new exponentially decaying error correlation model for assimilating OCO-2 column-average CO2 data using a length scale computed from airborne lidar measurements","volume":"15","author":"Baker","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"13281","DOI":"10.5194\/acp-14-13281-2014","article-title":"A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations","volume":"14","author":"Tian","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.5194\/acp-15-1087-2015","article-title":"A regional carbon data assimilation system and its preliminary evaluation in East Asia","volume":"15","author":"Peng","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"4837","DOI":"10.5194\/acp-17-4837-2017","article-title":"Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter","volume":"17","author":"Peng","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"17387","DOI":"10.5194\/acp-18-17387-2018","article-title":"The impact of multi-species surface chemical observation assimilation on air quality forecasts in China","volume":"18","author":"Peng","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"e2020GL089030","DOI":"10.1029\/2020GL089030","article-title":"Impact of Assimilating Meteorological Observations on Source Emissions Estimate and Chemical Simulations","volume":"47","author":"Peng","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"6719","DOI":"10.5194\/acp-23-6719-2023","article-title":"The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)","volume":"23","author":"Kou","year":"2023","journal-title":"Atmos. Chem. Phys."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3394\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:54:46Z","timestamp":1760111686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":202,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183394"],"URL":"https:\/\/doi.org\/10.3390\/rs16183394","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,12]]}}}