Précédent|Prochain

A review of collaborative remote sensing observation of greenhouse gases and aerosol with atmospheric environment satellites

  • role： First author第一作者
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：lizq@aircas.ac.cn
  • Introduction：李正强，研究方向为大气环境遥感。E-mail： lizq@aircas.ac.cn
LI Zhengqiang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：xieys@aircas.ac.cn
  • Introduction：谢一凇，研究方向为大气环境遥感。E-mail： xieys@aircas.ac.cn
XIE Yisong1*,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

SHI Yusheng1,  
  • Affiliation：

    Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China

LI Qing2,  
  • Affiliation：

    School of Atmosphere Sciences, Sun Yat-Sen University, Zhuhai 519082, China

    School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

COHEN Jason34,  
  • Affiliation：

    Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China

ZHANG Yuzhong5,  
  • Affiliation：

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

HAN Yinghui6,  
  • Affiliation：

    Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

XIONG Wei7,  
  • Affiliation：

    Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

LIU Yi8

résumé

Climate change is the most critical issue related to human survival and economic development currently being faced by the whole world. Greenhouse gases (GHGs) and aerosol are the main factors contributing to global warming and atmospheric environmental degradation caused by anthropogenic emissions; thus, they are the core detection targets of satellite remote sensing platforms. Compared with traditional single-target satellites, the collaborative monitoring of GHGs and aerosol on the same airborne platform, “Greenhouse gases and Aerosol Collaborative Observation Constellation” (GACOC), could significantly improve the accuracy of CO2 and CH4 retrieval. This way could improve the ability to estimate the carbon source and sink via the “top-down” method, as well as the ability to distinguish anthropogenic/natural sources of CO2, CH4, and atmospheric particulate matters. The GACOC has become an important spatial detection approach actively developed by aerospace agencies of various countries.This study introduces the satellites launched by the European Union, Japan, China, and the United States that can monitor GHGs and aerosol in one space-borne platform. These satellites are further divided into two categories according to their missions. The first one is the comprehensive atmospheric sounding satellites that independently detect GHGs and aerosol. These satellites can provide the temporal and spatial distribution of columnar CO2 or CH4 concentration and aerosol properties in the global context. The representative satellites of this category include ENVISAT, Sentinel-5P, FY-3D, and GF-5, as well as GF-5(02), DQ-1, DQ-2, and MetOp-SG-A that are about to launch in 1-3 years. The second category is the GHG monitoring satellites. Synchronous aerosol and cloud observations on the same platform provide necessary information for high-precision inversion of GHGs. The typical GHG satellites include GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat, and the ESA-planned CO2M series.Focusing on the significant national demands such as assessment of carbon neutrality pathways and atmospheric environmental governance, this study also discusses the development tendencies of monitoring GHGs and aerosol within the framework of a collaborative observation constellation.(1) Identification and quantitative monitoring of large anthropogenic emission sources. The anthropogenic CO2/CH4 and aerosol particles (and other tracers such as NO2) emitted from large-scale industrial areas or cities have some similarities in source, environment, and meteorological condition. Therefore, the high-resolution GHGs and aerosol observation by collaborative satellites can be employed to improve the ability to identify, track, and monitor large-scale, fixed, anthropogenic sources more efficiently.(2) High-precision joint inversion of atmospheric GHGs and aerosol. The scattering of aerosol and cloud greatly impact the inversion accuracy of CO2/CH4 satellite products. The advanced spaceborne technology that combines multi-angle, multi-band, and polarimetric measurements obtain high-precision aerosol optical and microphysical parameters. These parameters can be used to generate observation-based aerosol models when dealing with aerosol scattering during GHG’s inversion, and these models are more appropriate than the traditional models from modeling data.(3) Active–passive satellite networking. No single satellite can acquire a daily, global-coverage GHG or aerosol product due to the issues such as limited swath width, large number of cloudy pixels, and strict data quality criteria. Therefore, active–passive satellite networking is an essential approach to satisfy the demands of operationally observing the earth. The GACOC could fill in the data gap effectively and generate a spatially–temporally continuous global dataset of GHGs and aerosol. These data can provide a solid foundation for scientific research such as accurate assessments of climate change and dynamic monitoring of the atmospheric environment.

mots-clés

greenhouse gases;aerosol;satellite remote sensing;carbon dioxide;collaborative observation constellation

References

  1. 1.
    Abshire J B, Ramanathan A K, Riris H, Allan G R, Sun X L, Hasselbrack W E, Mao J P, Wu S, Chen J, Numata K, Kawa S R, Yang M Y M and DiGangi J. 2018. Airborne measurements of CO2 column concentrations made with a pulsed IPDA lidar using a multiple-wavelength-locked laser and HgCdTe APD detector. Atmospheric Measurement Techniques, 11(4): 2001-2025
  2. 2.
    Bertaux J L, Hauchecorne A, Lefèvre F, Bréon F M, Blanot L, Jouglet D, Lafrique P and Akaev P. 2020. The use of the 1.27 µm O2 absorption band for greenhouse gas monitoring from space and application to MicroCarb. Atmospheric Measurement Techniques, 13(6): 3329-3374
  3. 3.
    Bilal M, Nichol J E and Chan P W. 2014. Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms. Remote Sensing of Environment, 153: 50-60
  4. 4.
    Buchwitz M, de Beek R, Burrows J P, Bovensmann H, Warneke T, Notholt J, Meirink J F, Goede A P H, Bergamaschi P, Körner S, Heimann M and Schulz A. 2005. Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models. Atmospheric Chemistry and Physics, 5(4): 941-962
  5. 5.
    Buchwitz M, Rozanov V V and Burrows J P. 2000. 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. Journal of Geophysical Research: Atmospheres, 105(D12): 15231-15245
  6. 6.
    Chen H, Li Q, Wang Z T, Ma P F, Li Y and Zhao A M. 2020. Retrieval of aerosol optical depth using FY3D MERSI2 data. Journal of Geo-Information Science, 22(9): 1887-1896
  7. 7.
    Che H, Zhang X Y, Xia X, Goloub P, Holben B, Zhao H, Wang Y, Zhang X C, Wang H, Blarel L, Damiri B, Zhang R, Deng X, Ma Y, Wang T, Geng F, Qi B, Zhu J, Yu J, Chen Q and Shi G. 2015. Ground-based aerosol climatology of China: aerosol optical depths from the China Aerosol Remote Sensing Network (CARSNET) 2002-2013. Atmospheric Chemistry and Physics, 15(13): 7619-7652
  8. 8.
    Chen X, Wang J, Liu Y, Xu X G, Cai Z N, Yang D X, Yan C X and Feng L. 2017. Angular dependence of aerosol information content in CAPI/TanSat observation over land: effect of polarization and synergy with A-train satellites. Remote Sensing of Environment, 196: 163-177
  9. 9.
    Choi M, Kim J, Lee J, Kim M, Park Y J, Holben B, Eck T F, Li Z Q and Song C H. 2018. GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia. Atmospheric Measurement Techniques, 11(1): 385-408
  10. 10.
    Choi M, Lim H, Kim J, Lee S, Eck T F, Holben B N, Garay M J, Hyer E J, Saide P E and Liu H Q. 2019. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign. Atmospheric Measurement Techniques, 12(8): 4619-4641
  11. 11.
    Cohen J B and Wang C E. 2014. Estimating global black carbon emissions using a top-down Kalman Filter approach. Journal of Geophysical Research: Atmospheres, 119(1): 307-323
  12. 12.
    Connor B, Bösch H, McDuffie J, Taylor T, Fu D J, Frankenberg C, O’Dell C, Payne V H, Gunson M, Pollock R, Hobbs J, Oyafuso F and Jiang Y B. 2016. Quantification of uncertainties in OCO-2 measurements of XCO2: simulations and linear error analysis. Atmospheric Measurement Techniques, 9(10): 5227-5238
  13. 13.
    Crisp D, O’Dell C, Eldering A, Fisher B, Oyafuso F, Payne V, Drouin B, Toon G, Laughner J, Somkuti P, McGarragh G, Merrelli A, Nelson R, Gunson M, Frankenberg C, Osterman G, Boesch H, Brown L, Castano R, Christi M, Connor B, McDuffie J, Miller C, Natraj V, O’Brien D, Polonski I, Smyth M, Thompson D and Granat R. 2021. Orbiting Carbon Observatory (OCO) -2: Level 2 Full Physics Algorithm Theoretical Basis Document [OCO D-55207]
  14. 14.
    de Graaf M, de Haan J and Sanders A. 2019. TROPOMI ATBD of the Aerosol Layer Height [S5P-KNMI-L2-0006-RP]
  15. 15.
    de Leeuw G, Holzer-Popp T, Bevan S, Davies W H, Descloitres J, Grainger R G, Griesfeller J, Heckel A, Kinne S, Klüser L, Kolmonen P, Litvinov P, Martynenko D, North P, Ovigneur B, Pascal N, Poulsen C, Ramon D, Schulz M, Siddans R, Sogacheva L, Tanré D, Thomas G E, Virtanen T H, von Hoyningen Huene W, Vountas M and Pinnock S. 2015. Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis. Remote Sensing of Environment, 162: 295-315
  16. 16.
    Diner D J, Abdou W A, Ackerman T P, Crean K, Gordon H R, Kahn R A, Martonchik J V, McMuldroch S, Paradise S R, Pinty B, Verstraete M M, Wang M H and West R A. 2008. MISR: Level 2 Aerosol Retrieval Algorithm Theoretical Basis [JPL D-11400]
  17. 17.
    Diner D J, Martonchik J V, Kahn R A, Pinty B, Gobron N, Nelson D L and Holben B N. 2005. Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land. Remote Sensing of Environment, 94(2): 155-171
  18. 18.
    Dubovik O, Herman M, Holdak A, Lapyonok T, Tanré D, Deuzé J L, Ducos F, Sinyuk A and Lopatin A. 2011. Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations. Atmospheric Measurement Techniques, 4(5): 975-1018
  19. 19.
    Dubovik O, Lapyonok T, Litvinov P, Herman M, Fuertes D, Ducos F, Torres B, Derimian Y, Huang X, Lopatin A, Chaikovsky A, Aspetsberger M and Federspiel C. 2014. GRASP: a versatile algorithm for characterizing the atmosphere. SPIE Newsroom, 25 [DOI ]
  20. 20.
    Dubovik O, Li Z Q, Mishchenko M I, Tanré D, Karol Y, Bojkov B, Cairns B, Diner D J, Espinosa W R, Goloub P, Gu X F, Hasekamp O, Hong J, Hou W Z, Knobelspiesse K D, Landgraf J, Li L, Litvinov P, Liu Y, Lopatin A, Marbach T, Maring H, Martins V, Meijer Y, Milinevsky G, Mukai S, Parol F, Qiao Y L, Remer L, Rietjens J, Sano I, Stammes P, Stamnes S, Sun X B, Tabary P, Travis L D, Waquet F, Xu F, Yan C X and Yin D K. 2019. Polarimetric remote sensing of atmospheric aerosols: instruments, methodologies, results, and perspectives. Journal of Quantitative Spectroscopy and Radiative Transfer, 224: 474-511
  21. 21.
    Dubovik O, Litvinov P, Poustomis F, Vinuesa J, Fuertes D and Ducos F. 2018. ERA - Enhanced Retrieval of Aerosol properties: reference and NRT algorithm prototype for 3MI mission
  22. 22.
    Ehret G, Bousquet P, Pierangelo C, Alpers M, Millet B, Abshire J B, Bovensmann H, Burrows J P, Chevallier F, Ciais P, Crevoisier C, Fix A, Flamant P, Frankenberg C, Gibert F, Heim B, Heimann M, Houweling S, Hubberten H W, Jöckel P, Law K, Löw A, Marshall J, Agusti-Panareda A, Payan S, Prigent C, Rairoux P, Sachs T, Scholze M and Wirth M. 2017. MERLIN: a French-German space lidar mission dedicated to atmospheric methane. Remote Sensing, 9(10): 1052
  23. 23.
    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 J, 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. 2017. The Orbiting Carbon Observatory-2: first 18 months of science data products. Atmospheric Measurement Techniques, 10(2): 549-563
  24. 24.
    Eldering A, Taylor T E, O’Dell C W and Pavlick R. 2019. The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data. Atmospheric Measurement Techniques, 12(4): 2341-2370
  25. 25.
    Ge B Y. 2020. Retrieval of Aerosol Optical Parameters Based on Directional Polarimetric Camera (DPC) Onboard the GF-5 Satellite. University of Chinese Academy of Sciences
  26. 26.
    Ge B Y, Mei X D, Li Z Q, Hou W Z, Xie Y, Zhang Y, Xu H, Li K T and Wei Y Y. 2020. An improved algorithm for retrieving high resolution fine-mode aerosol based on polarized satellite data: application and validation for POLDER-3. Remote Sensing of Environment, 247: 111894
  27. 27.
    Ge B Y, Yang L K, Chen X F, Li Z Q, Mei X D and Liu L. 2018. Study on aerosol optical depth retrieval over land from Himawari-8 data based on dark target method. Journal of Remote Sensing, 22(1): 38-50
  28. 28.
    GHG-CCI Project Team. 2020. GHG-CCI: User Requirements Document for the GHG-CCI+ project of ESA’s Climate Change Initiative, Version 3.0
  29. 29.
    Giles D M, Sinyuk A, Sorokin M G, Schafer J S, Smirnov A, Slutsker I, Eck T F, Holben B N, Lewis J R, Campbell J R, Welton E J, Korkin S V and Lyapustin A I. 2019. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques, 12(1): 169-209
  30. 30.
    Gloudemans A M S, Schrijver H, Kleipool Q, van den Broek M M P, Straume A G, Lichtenberg G, van Hees R M, Aben I and Meirink J F. 2005. The impact of SCIAMACHY near-infrared instrument calibration on CH4 and CO total columns. Atmospheric Chemistry and Physics, 5(9): 2369-2383
  31. 31.
    Gong S Y and Shi Y S. 2021. Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China. Science of the Total Environment, 784: 147116
  32. 32.
    Guerlet S, Butz A, Schepers D, Basu S, Hasekamp O P, Kuze A, Yokota T, Blavier J F, Deutscher N M, Griffith D W T, Hase F, Kyro E, Morino I, Sherlock V, Sussmann R, Galli A and Aben I. 2013. Impact of aerosol and thin cirrus on retrieving and validating XCO2 from GOSAT shortwave infrared measurements. Journal of Geophysical Research: Atmospheres, 118(10): 4887-4905
  33. 33.
    Hagolle O, Dedieu G, Mougenot B, Debaecker V, Duchemin B and Meygret A. 2008. Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: application to Formosat-2 images. Remote Sensing of Environment, 112(4): 1689-1701
  34. 34.
    Han G, Xu H, Gong W, Liu J Q, Du J, Ma X and Liang A L. 2018. Feasibility study on measuring atmospheric CO2 in urban areas using Spaceborne CO2-IPDA LIDAR. Remote Sensing, 10(7): 985
  35. 35.
    Hasekamp O, Lorente A, Hu H L, Butz A, de Brugh J and Landgraf J. 2019. Algorithm Theoretical Baseline Document for Sentinel-5 Precursor Methane Retrieval [SRON-S5P-LEV2-RP-001]
  36. 36.
    He L J, Wang L C, Li Z Q, Jiang D Y, Sun L, Liu D, Liu L, Yao R, Zhou Z G and Wei J. 2021. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China. Atmospheric Environment, 250: 118265
  37. 37.
    Heymann J, Reuter M, Hilker M, Buchwitz M, Schneising O, Bovensmann H, Burrows J P, Kuze A, Suto H, Deutscher N M, Dubey M K, Griffith D W T, Hase F, Kawakami S, Kivi R, Morino I, Petri C, Roehl C, Schneider M, Sherlock V, Sussmann R, Velazco V A, Warneke T and Wunch D. 2015. Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm. Atmospheric Measurement Techniques, 8(7): 2961-2980
  38. 38.
    Holben B N, Eck T F, Slutsker I, Tanré D, Buis J P, Setzer A, Vermote E, Reagan J A, Kaufman Y J, Nakajima T, Lavenu F, Jankowiak I and Smirnov A. 1998. AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1): 1-16
  39. 39.
    Hsu N C, Jeong M J, Bettenhausen C, Sayer A M, Hansell R, Seftor C S, Huang J and Tsay S C. 2013. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. Journal of Geophysical Research: Atmospheres, 118(16): 9296-9315
  40. 40.
    Hsu N C, Tsay S C, King M D, Herman J R. 2004. Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569
  41. 41.
    Huang H L, Ti R F, Zhang D Y, Fang W, Sun X B and Yi W N. 2020. Inversion of aerosol optical depth over land from directional polarimetric camera onboard Chinese Gaofen-5 satellite. Journal of Infrared and Millimeter Waves, 39(4): 454-461
  42. 42.
    Jiang F, Wang H M, Chen J M, Ju W M, Tian X J, Feng S Z, Li G C, Chen Z Q, Zhang S P, Lu X H, Liu J, Wang H K, Wang J, He W and Wu M S. 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmospheric Chemistry and Physics, 21(3): 1963-1985
  43. 43.
    Kaufman Y J, Tanré D, Remer L A, Vermote E F, Chu A and Holben B N. 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067
  44. 44.
    Kuhlmann G, Broquet G, Marshall J, Clément V, Löscher A, Meijer Y and Brunner D. 2019. Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission. Atmospheric Measurement Techniques, 12(12): 6695-6719
  45. 45.
    Kuze A, Suto H, Nakajima M and Hamazaki T. 2009. Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. Applied Optics, 48(35): 6716-6733
  46. 46.
    Levy R C, Remer L A, Mattoo S, Vermote E F and Kaufman Y J. 2007. Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of Geophysical Research: Atmospheres, 112: D13211
  47. 47.
    Li B G, Gasser T, Ciais P, Piao S, Tao S, Balkanski Y, Hauglustaine D, Boisier J P, Chen Z, Huang M T, Li L Z, Li Y, Liu H Y, Liu J F, Peng S S, Shen Z H, Sun Z Z, Wang R, Wang T, Yin G D, Yin Y, Zeng H, Zeng Z Z and Zhou F. 2016. The contribution of China’s emissions to global climate forcing. Nature, 531(7594): 357-361
  48. 48.
    Li Z Q, Hou W Z, Hong J, Zheng F X, Luo D G, Wang J, Gu X F and Qiao Y L. 2018a. Directional Polarimetric Camera (DPC): monitoring aerosol spectral optical properties over land from satellite observation. Journal of Quantitative Spectroscopy and Radiative Transfer, 218: 21-37
  49. 49.
    Li Z Q, Li D H, Li K T, Xu H, Chen X F, Chen C, Xie Y S, Li L, Li L, Li W, Lv Y, Qie L L, Zhang Y and Gu X F. 2015. Sun-sky radiometer observation network with the extension of multi-wavelength polarization measurements. Journal of Remote Sensing, 19(3): 495-519
  50. 50.
    Li Z Q, Xu H, Li K T, Li D H, Xie Y S, Li L, Zhang Y, Gu X F, Zhao W, Tian Q J, Deng R R, Su X L, Huang B, Qiao Y L, Cui W Y, Hu Y, Gong C L, Wang Y Q, Wang X F, Wang J P, Du W B, Pan Z Q, Li Z Z and Bu D. 2018b. Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: an overview of sun–sky radiometer observation network (SONET) measurements. Bulletin of the American Meteorological Society, 99(4): 739-755
  51. 51.
    Liang A L, Gong W, Han G and Xiang C Z. 2017. Comparison of satellite-observed XCO2 from GOSAT, OCO-2, and ground-based TCCON. Remote Sensing, 9(10): 1033
  52. 52.
    Liu Y, Wang J, Che K, Cai Z N, Yang D X and Wu L. 2021. Satellite remote sensing of greenhouse gases: progress and trends. National Remote Sensing Bulletin, 25(1): 53-64
  53. 53.
    Liu Y, Wang J, Yao L, Chen X, Cai Z N, Yang D X, Yin Z S, Gu S Y, Tian L F, Lu N M and Lyu D. 2018. The TanSat mission: preliminary global observations. Science Bulletin, 63(18): 1200-1207
  54. 54.
    Lorente A, Borsdorff T, Butz A, Hasekamp O, aan de Brugh J, Schneider A, Wu L H, Hase F, Kivi R, Wunch D, Pollard D F, Shiomi K, Deutscher N M, Velazco V A, Roehl C M, Wennberg P O, Warneke T and Landgraf J. 2021. Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements. Atmospheric Measurement Techniques, 14(1): 665-684
  55. 55.
    Lu Q F, Zhou F, Qi C L, Hu X Q, Xu H L and Wu C Q. 2019. Spectral performance evaluation of high-spectral resolution infrared atmospheric sounder onboard FY-3D. Optics and Precision Engineering, 27(10): 2105-2115
  56. 56.
    Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS Collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765
  57. 57.
    Martonchik J V, Diner D J, Kahn R A, Ackerman T P, Verstraete M M, Pinty B and Gordon H R. 1998. Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1212-1227
  58. 58.
    Meijer Y, Boesch H, Bombelli A, Brunner D, Buchwitz M, Ciais P, Crisp D, Engelen R, Holmlund K, Houweling S, Janssens-Maenhout G, Marshall J, Nakajima M, Pinty B, Scholze M, Bezy J, Drinkwater M, Fehr T, Fernandez V, Loescher A, Nett H and Sierk B. 2020. Copernicus CO2 Monitoring Mission Requirements Document [EOP-SM/3088/YM-ym]
  59. 59.
    Moore B III, 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. 2018. 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: 109
  60. 60.
    O'Dell C W, Connor B, Bösch H, O'Brien D, Frankenberg C, Castano R, Christi M, Eldering D, Fisher B, Gunson M, McDuffie J, Miller C E, Natraj V, Oyafuso F, Polonsky I, Smyth M, Taylor T, Toon G C, Wennberg P O and Wunch D. 2012. The ACOS CO2 retrieval algorithm – Part 1: description and validation against synthetic observations. Atmospheric Measurement Techniques, 5(1): 99-121
  61. 61.
    O'Dell C W, Eldering A, Wennberg P O, Crisp D, Gunson M R, Fisher B, Frankenberg C, Kiel M, Lindqvist H, Mandrake L, Merrelli A, Natraj V, Nelson R R, Osterman G B, Payne V H, Taylor T E, Wunch D, Drouin B J, Oyafuso F, Chang A, McDuffie J, Smyth M, Baker D F, Basu S, Chevallier F, Crowell S M R, Feng L, Palmer P I, Dubey M, García O E, Griffith D W T, Hase F, Iraci L T, Kivi R, Morino I, Notholt J, Ohyama H, Petri C, Roehl C M, Sha M K, Strong K, Sussmann R, Te Y, Uchino O and Velazco V A. 2018. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm. Atmospheric Measurement Techniques, 11(12): 6539-6576
  62. 62.
    Olson M R, Wang Y Q, de Foy B, Li Z Q, Bergin M H, Zhang Y X and Schauer J J. 2022. Source attribution of black and Brown carbon near-UV light absorption in Beijing, China and the impact of regional air-mass transport. Science of the Total Environment, 807: 150871
  63. 63.
    Qie L, Li D H, Li Z Q, Zhang Y, Hou W Z and Chen X F. 2015. A sensitivity study of atmospheric reflectance to aerosol layer height based on multi-angular polarimetric measurements//Proceedings Volume 9678, AOPC 2015: Telescope and Space Optical Instrumentation. Beijing: SPIE
  64. 64.
    Reuter M, Buchwitz M, Schneising O, Noël S, Bovensmann H and Burrows J P. 2017. A fast atmospheric trace gas retrieval for hyperspectral instruments approximating multiple scattering—Part 2: application to XCO2 retrievals from OCO-2. Remote Sensing, 9(11): 1102
  65. 65.
    Sanghavi S, Nelson R, Frankenberg C and Gunson M. 2020. Aerosols in OCO-2/GOSAT retrievals of XCO2: an information content and error analysis. Remote Sensing of Environment, 251: 112053
  66. 66.
    Santer R, Ramon D, Vidot J and Dilligeard E. 2007. A surface reflectance model for aerosol remote sensing over land. International Journal of Remote Sensing, 28(3/4): 737-760
  67. 67.
    Schepers D, Guerlet S, Butz A, Landgraf J, Frankenberg C, Hasekamp O, Blavier J F, Deutscher N M, Griffith D W T, Hase F, Kyro E, Morino I, Sherlock V, Sussmann R and Aben I. 2012. Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: performance comparison of proxy and physics retrieval algorithms. Journal of Geophysical Research: Atmospheres, 117(D10): D10307
  68. 68.
    Schneising O, Buchwitz M, Burrows J P, Bovensmann H, Bergamaschi P and Peters W. 2009. Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite - Part 2: methane. Atmospheric Chemistry and Physics, 9(2): 443-465
  69. 69.
    Schneising O, Buchwitz M, Burrows J P, Bovensmann H, Reuter M, Notholt J, Macatangay R and Warneke T. 2008. Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 1: carbon dioxide. Atmospheric Chemistry and Physics, 8(14): 3827-3853
  70. 70.
    Schuster G L, Dubovik O and Arola A. 2016. Remote sensing of soot carbon – Part 1: distinguishing different absorbing aerosol species. Atmospheric Chemistry and Physics, 16(3): 1565-1585
  71. 71.
    Shi G M, Li C C and Ren T. 2014. Sensitivity analysis of single-angle polarization reflectance observed by satellite. Chinese Science Bulletin, 59(14): 1519-1528
  72. 72.
    Shi H L, Li Z W, Ye H H, Luo H Y, Xiong W and Wang X H. 2021. First level 1 product results of the greenhouse gas monitoring instrument on the GaoFen-5 satellite. IEEE Transactions on Geoscience and Remote Sensing, 59(2): 899-914
  73. 73.
    Sinyuk A, Holben B N, Eck T F, Giles D M, Slutsker I, Korkin S, Schafer J S, Smirnov A, Sorokin M and Lyapustin A. 2020. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2. Atmospheric Measurement Techniques, 13(6): 3375-3411
  74. 74.
    Suto H, Kataoka F, Kikuchi N, Knuteson R O, Butz A, Haun M, Buijs H, Shiomi K, Imai H and Kuze A. 2021. Thermal and near-infrared sensor for carbon observation Fourier transform spectrometer-2 (TANSO-FTS-2) on the Greenhouse gases Observing SATellite-2 (GOSAT-2) during its first year in orbit. Atmospheric Measurement Techniques, 14(3): 2013-2039
  75. 75.
    Tang F Y, Zhou H J, Wang W H, Yang T P and Si F Q. 2021. Absorbing aerosol index inversion algorithm of TROPOMI and its application. Acta Optica Sinica, 41(16): 1601001
  76. 76.
    Tanré D, Remer L A, Kaufman Y J, Mattoo S, Hobbs P V, Livingston J M, Russell P B and Smirnov A. 1999. Retrieval of aerosol optical thickness and size distribution over ocean from the MODIS airborne simulator during TARFOX. Journal of Geophysical Research: Atmospheres, 104(D2): 2261-2278
  77. 77.
    Taylor T E, O'Dell C W, Frankenberg C, Partain P T, Cronk H Q, Savtchenko A, Nelson R R, Rosenthal E J, Chang A Y, Fisher B, Osterman G B, Pollock R H, Crisp D, Eldering A and Gunson M R. 2016. Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data. Atmospheric Measurement Techniques, 9(3): 973-989
  78. 78.
    Wu H. 2019. Influence of Cloud and Aerosol in Atmospheric CO2 Inversion and its Correlation Method. Hefei: University of Science and Technology of China
  79. 79.
    Wu K Y, Hou W Z, Shi Z, Xu H and Wen Y N. 2021. Research progress of aerosol remote sensing retrieval algorithm based on satellite multi-angle observation. Journal of Atmospheric and Environmental Optics, 16(4): 283-298
  80. 80.
    Wu S C, Wang X H, Ye H H, Li C, An Y and Wang X D. 2021. Atmospheric CO2 cooperative inversion algorithm applied to GF-5 satellite. Acta Optica Sinica, 41(15): 1501002
  81. 81.
    Wunch D, Toon G C, Sherlock V, Deutscher N M, Liu C, Feist D G and Wennberg P O. 2015. Documentation for the 2014 TCCON Data Release, CaltechDATA
  82. 82.
    Wunch D, Wennberg P O, Osterman G, Fisher B, Naylor B, Roehl C M, O'Dell C, Mandrake L, Viatte C, Kiel M, Griffith D W T, Deutscher N M, Velazco V A, Notholt J, Warneke T, Petri C, De Maziere M, Sha M K, Sussmann R, Rettinger M, Pollard D, Robinson J, Morino I, Uchino O, Hase F, Blumenstock T, Feist D G, Arnold S G, Strong K, Mendonca J, Kivi R, Heikkinen P, Iraci L, Podolske J, Hillyard P W, Kawakami S, Dubey M K, Parker H A, Sepulveda E, García O E, Te Y, Jeseck P, Gunson M R, Crisp D and Eldering A. 2017. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmospheric Measurement Techniques, 10(6): 2209-2238
  83. 83.
    Xie X D. 2020. Study on the Interactions of Air Pollution, Vegetation and Carbon Dioxide in China. Nanjing: Nanjing University
  84. 84.
    Xie Y S, Li Z Q, Hou W Z, Zhang Y, Qie L L, Li L, Li K T and Xu H. 2019. Retrieval of fine-mode aerosol optical depth based on remote sensing measurements of Directional Polarimetric Camera onboard GF-5 satellite. Aerospace Shanghai, 36(S2): 219-226
  85. 85.
    Xie Y S, Li Z Q, Zhang X Y, Xu H, Li D H and Li K T. 2015. A new ground-based differential absorption sunphotometer for measuring atmospheric columnar CO2 and preliminary applications//Proceedings Volume 9678, AOPC 2015: Telescope and Space Optical Instrumentation. Beijing: SPIE
  86. 86.
    Xiong W. 2019. Greenhouse gases Monitoring Instrument(GMI) on GF-5 satellite (invited). Infrared and Laser Engineering, 48(3): 0303002
  87. 87.
    Yang D, Boesch H, Liu Y, Somkuti P, Cai Z, Chen X, Di Noia A, Lin C, Lu N, Lyu D, Parker R J, Tian L, Wang M, Webb A, Yao L, Yin Z, Zheng Y, Deutscher N M, Griffith D W T, Hase F, Kivi R, Morino I, Notholt J, Ohyama H, Pollard D F, Shiomi K, Sussmann R, Té Y, Velazco V A, Warneke T and Wunch D. 2020. Toward high precision XCO2 retrievals from TanSat observations: retrieval improvement and validation against TCCON measurements. Journal of Geophysical Research: Atmospheres, 125(22): e2020JD032794
  88. 88.
    Ye H H, Wang X H, Wu S C, Li C, Li Z W, Shi H L and Xiong W. 2021. Atmospheric CO2 retrieval method for satellite observations of Greenhouse gases Monitoring Instrument on GF-5. Journal of Atmospheric and Environmental Optics, 16(3): 231-238
  89. 89.
    Zhang X Y, Meng X Y, Zhou M Q, Bai W G, Zhou L H, Hu Y M and Yu X. 2018. Review of the validation of atmospheric CO2 from satellite hyper spectral remote sensing. Climate Change Research, 14(6): 602-612
  90. 90.
    Zhang Y and Li Z Q. 2015. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160: 252-262
  91. 91.
    Zhang Y, Li Z Q, Qie L L, Hou W Z, Liu Z H, Zhang Y, Xie Y S, Chen X F and Xu H. 2017. Retrieval of aerosol optical depth using the empirical orthogonal functions (EOFs) based on PARASOL multi-angle intensity data. Remote Sensing, 9(6): 578
  92. 92.
    Zhao L. 2017. Remote Retrieval of Atmospheric CO2 and CH4 using GOSAT. Changchun: Jilin University
  93. 93.
    Zheng F X, Zhu J Y, Hou W Z and Li Z Q. 2021. Effect analysis of using different polarization quantities in aerosol retrieval from satellite observation. Spectroscopy and Spectral Analysis, 41(7): 2212-2218
  94. 94.
    Zweers D C S. 2018. TROPOMI ATBD of the UV aerosol index [S5P-KNMI-L2-0008-RP]

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website