A review of collaborative remote sensing observation of atmospheric gaseous and particulate pollution 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:zhang_ying@aircas.ac.cn
  • Introduction:张莹,研究方向为大气遥感。E-mail:zhang_ying@aircas.ac.cn
ZHANG Ying1,  
  • 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:lizq@radi.ac.cn
  • Introduction:李正强,研究方向为环境遥感研究。E-mail:lizq@radi.ac.cn
LI Zhengqiang1*,  
  • Affiliation:

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

ZHAO Shaohua2,  
  • Affiliation:

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, Beijing 100081, China

ZHANG Xingying3,  
  • Affiliation:

    Department of Atmospheric and Ocean Sciences, School of Physics, Peking University, Beijing 100871, China

LIN Jintai4,  
  • Affiliation:

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

QIN Kai5,  
  • Affiliation:

    School of Engineering Science, University of Science and Technology of China, Hefei 230027, China

LIU Cheng6,  
  • Affiliation:

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

ZHANG Yuanxun7

реферат

Air pollution, as important environmental problem, directly affects daily life and physical health of public. The gradual maturity of polluted gas and particulate matter observation technology has rapidly developed the monitoring of air pollutants near the surface based on satellite platforms. This study aims to clarify the collaborative observation’s history for aerosols and gases and then provide a reference for future satellite platform design.In this study, the popular remote sensing methods for trace gases and atmospheric particulates that are concerned on atmospheric environment are first described, and the applicable scenarios, advantages, and disadvantages of each method are discussed. Next, satellite platforms for collaborative observations of trace gases and aerosols are reviewed. According to the characteristics of remote sensing principle for the trace gases, the satellite platform is divided into ultraviolet and infrared bands, and the development course of sensors and satellite platforms are discussed and analyzed. Finally, we discuss the issues to be solved urgently by satellite platforms and remote sensing algorithms aiming to monitor air pollutants near the ground, as well as possible future development directions.For various trace gases, the good universal remote sensing methods are differential absorption spectrometry method and optimal estimation algorithm, which can fully utilize the absorption spectrum lines to achieve inversion of gases. The differential absorption spectroscopy method is effective for the monitoring of trace gases. However, the optimized estimation algorithm can further extract the layered information of trace gases from the hyperspectral information, which is helpful for obtaining a more detailed vertical distribution of trace gases in the atmospheric column. The band residual method and linear fitting method have strong pertinence to specific pollutant gases (such as sulfur dioxide). These simplified algorithms also have great advantages and application value. The core issue of the aerosol inversion algorithm is the signal decoupling of ground and atmosphere. Adding the information from spectrum, angle, polarization, and time series can effectively increase the decoupling capabilities. The algorithms derived from these principles include dark target algorithm, deep blue algorithm, empirical orthogonal function algorithm, polarization algorithm, and time series algorithm. Since the launch of NOAA-9 carrying SBUV/2 and AVHRR/2 in 1984, the collaborative detection of polluted gases and particulate matter has begun. Subsequently, Europe, the United States, South Korea, and China have launched satellites carrying advanced sensors, from the polar orbit to geostationary orbit. In the future, FY-4A of China, Geo-kompsat-2b of South Korea, Sentinel-4 of Europe, and TEMPO of the United States can be forming a global geostationary satellite constellation with high spatial resolution and hourly monitoring capability to achieve collaborative monitoring of polluted gases and particulate matter.On the basis of the summary of trace gas and atmospheric aerosol inversion algorithms, the development history of satellite platforms and sensors is combined from the perspective of cooperative observation of gas and particulate matter. The advantages of cooperative observation of sensors in the ultraviolet, visible, and infrared bands are discussed. The high temporal and spatial resolution air pollution monitoring capabilities of the geostationary satellite constellation in the future and the contribution of Chinese satellites are prospected.

ключеви́че слова́

satellite;trace gas;particle;atmospheric environment;remote sensing;collaborative observation

References

  1. 1.
    Abad G G, Souri A H, Bak J, Chance K, Flynn L E, Krotkov N A, Lamsal L, Li C, Liu X, Miller C C, Nowlan C R, Suleiman R and Wang H Q. 2019. Five decades observing Earth's atmospheric trace gases using ultraviolet and visible backscatter solar radiation from space. Journal of Quantitative Spectroscopy and Radiative Transfer, 238: 106478
  2. 2.
    Bates D R and Nicolet M. 1950. The photochemistry of atmospheric water vapor. Journal of Geophysical Research, 55(3): 301-327
  3. 3.
    Beirle S, Lampel J, Wang Y, Mies K, Dörner S, Grossi M, Loyola D, Dehn A, Danielczok A, Schröder M and Wagner T. 2018. The ESA GOME-evolution “Climate” water vapor product: a homogenized time series of H2O columns from GOME, SCIAMACHY, and GOME-2. Earth System Science Data, 10(1): 449-468
  4. 4.
    Bhartia P K and Wellemeyer C W. 2002. TOMS-V8 total O3 algorithm//Bhartia P K, ed. OMI Algorithm Theoretical Basis Document, Volume II: OMI Ozone Products. Greenbelt, MD: NASA Goddard Space Flight Center: 15-32
  5. 5.
    Boersma K F, Jacob D J, Eskes H J, Pinder R W, Wang J and Van der A R J. 2008. Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: observing the diurnal evolution of chemistry and emissions from space. Journal of Geophysical Research: Atmospheres, 113(D16): D16S26
  6. 6.
    Bovensmann H, Burrows J P, Buchwitz M, Frerick J, Noël S, Rozanov V V, Chance K V and Goede A P H. 1999. SCIAMACHY: mission objectives and measurement modes. Journal of the Atmospheric Sciences, 56(2): 127-150
  7. 7.
    Cairns B, Travis L, Mishchenko M I and Chowdhary J. 2001. 3.7 Aerosol retrievals over land surfaces (The advantages of polarization). At 81st American Meteorological Society Annual Meeting, Albuquerque, NM.
  8. 8.
    Charlson R J and Rodhe H. 1982. Factors controlling the acidity of natural rainwater. Nature, 295(5851): 683-685
  9. 9.
    Clerbaux C, Hadji-Lazaro J, Turquety S, George M, Coheur P F, Hurtmans D, Wespes C, Herbin H, Blumstein D, Tourniers B and Phulpin T. 2007. The IASI/MetOp1 mission: first observations and highlights of its potential contribution to GMES2. Space Research Today, 168: 19-24
  10. 10.
    Clerbaux C, Hadji-Lazaro J, Turquety S, Mégie G and Coheur P F. 2003. Trace gas measurements from infrared satellite for chemistry and climate applications. Atmospheric Chemistry and Physics, 3(5): 1495-1508
  11. 11.
    Cornu A M. 1879. II. Sur la limite ultraviolette du spectre solaire. Proceedings of the Royal Society of London, 29(196/199): 47-55
  12. 12.
    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
  13. 13.
    Diner D J, Xu F, Martonchik J V, Rheingans B E, Geier S, Jovanovic V M, Davis A, Chipman R A and McClain S C. 2012. Exploration of a polarized surface bidirectional reflectance model using the ground-based multiangle SpectroPolarimetric imager. Atmosphere, 3(4): 591-619
  14. 14.
    Dittman M G, Ramberg E, Chrisp M, Rodriguez J V, Sparks A L, Zaun N H, Hendershot P, Dixon T, Philbrick R H and Wasinger D. 2002. Nadir ultraviolet imaging spectrometer for the NPOESS Ozone Mapping and Profiler Suite (OMPS)//Proceedings Volume 4814, Earth Observing Systems VII. Seattle, WA, United States: SPIE
  15. 15.
    Dobson G M B. 1931. A photoelectric spectrophotometer for measuring the amount of atmospheric ozone. Proceedings of the Physical Society, 43(3): 324-339
  16. 16.
    Dong C H, Yang J, Zhang W J, Yang Z D, Lu N M, Shi J M, Zhang P, Liu Y J and Cai B. 2009. An overview of a new Chinese weather satellite FY-3A. Bulletin of the American Meteorological Society, 90(10): 1531-1544
  17. 17.
    Dubovik O, Herman M, Holdak A, Lapyonok T, Tauré 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
  18. 18.
    Elias T G, Cairns B and Chowdhary J. 2004. Surface optical properties measured by the airborne research scanning polarimeter during the CLAMS experiment//Proceedings Volume 5235, Remote Sensing of Clouds and the Atmosphere VIII. Barcelona, Spain: SPIE
  19. 19.
    Fabry C and Buisson H. 1913. L'absorption de l'ultra-violet par l'ozone et la limite du spectre solaire. Journal de Physique Théorique et Appliquée, 3(1): 196-206
  20. 20.
    Farman J C, Gardiner B G and Shanklin J D. 1985. Large losses of total ozone in Antarctica reveal seasonal ClOx/NOx interaction. Nature, 315(6016): 207-210
  21. 21.
    Finlayson-Pitts B J and Pitts J N. 2000. Chemistry of the Upper and Lower Atmosphere. San Diego: Academic Press.
  22. 22.
    Fioletov V E, Mclinden C A, Krotkov N, Yang K, Loyola D G, Valks P, Theys N, Van Roozendael M, Nowlan C R, Chance K, Liu X, Lee C and Martin R V. 2013. Application of OMI, SCIAMACHY, and GOME-2 satellite SO2 retrievals for detection of large emission sources. Journal of Geophysical Research: Atmospheres, 118(19): 11399-11418
  23. 23.
    Flynn L, Long C, Wu X, Evans R, Beck C T, Petropavlovskikh I, Mcconville G, Yu W, Zhang Z, Niu J, Beach E, Hao Y, Pan C, Sen B, Novicki M, Zhou S and Seftor C. 2014. Performance of the Ozone Mapping and Profiler Suite (OMPS) products. Journal of Geophysical Research Atmospheres, 119(10): 6181-6195
  24. 24.
    Ge B Y, Mei X D, Li Z Q, Hou W Z, Xie Y S, 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
  25. 25.
    Gille J C, Drummond J R, Wang J X, Edwards D P, Deeter M N, Khattatov B, Lamarque J F, Warner J and Ziskin D C. 1999. EOS MOPITT experiment: extracting the information from the measurements//Proceedings Volume 3756, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research III. Denver, CO, United States: SPIE
  26. 26.
    Grainger J F and Ring J. 1962. Anomalous fraunhofer line profiles. Nature, 193(4817): 762
  27. 27.
    Guyenne T D and Readings C. 1993. GOME: Global Ozone Monitoring Experiment. Interim Science Report. Paris: European Space Agency
  28. 28.
    Haagen-Smit A J. 1952. Chemistry and physiology of los angeles smog. Industrial and Engineering Chemistry, 44(6): 1342-1346
  29. 29.
    Haagen-Smit A J and Fox M M. 1954. Photochemical ozone formation with hydrocarbons and automobile exhaust. Air Repair, 4(3): 105-136
  30. 30.
    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
  31. 31.
    Hartley W N. 1880. On the probable absorption of solar radiation by atmospheric ozone. Chemistry News, 39(1): 111-128.
  32. 32.
    He J L. 2020. Study on the influence of cloud adjacency effects on the aerosol optical depth retrieval and its reducing method. Acta Geodaetica et Cartographica Sinica, 49(1): 132
  33. 33.
    Heath D F, Krueger A J, Roeder H A and Henderson B D. 1975. The solar backscatter ultraviolet and total ozone mapping spectrometer (SBUV/TOMS) for NIMBUS G. Optical Engineering, 14(4): 323-331
  34. 34.
    Herman J, Huang L, McPeters R, Ziemke J, Cede A and Blank K. 2018. Synoptic ozone, cloud reflectivity, and erythemal irradiance from sunrise to sunset for the whole earth as viewed by the DSCOVR spacecraft from the earth–sun Lagrange 1 orbit. Atmospheric Measurement Techniques, 11(1): 177-194
  35. 35.
    Herman J R and Celarier E A. 1997. Earth surface reflectivity climatology at 340-380 nm from TOMS data. Journal of Geophysical Research: Atmospheres, 102(D23): 28003-28011
  36. 36.
    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
  37. 37.
    Hsu N C, Tsay S C, King M D and Herman J R. 2004. Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569
  38. 38.
    Hsu N C, Tsay S C, King M D and Herman J R. 2006. Deep blue retrievals of asian aerosol properties during ACE-Asia. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3180-3195
  39. 39.
    Joiner J and Bhartia P K. 1997. Accurate determination of total ozone using SBUV continuous spectral scan measurements. Journal of Geophysical Research: Atmospheres, 102(D11): 12957-12969
  40. 40.
    Kaufman Y J and Sendra C. 1988. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. International Journal of Remote Sensing, 9(8): 1357-1381
  41. 41.
    Kim J, Jeong U, Ahn M H, Kim J H, Park R J, Lee H, Song C H, Choi Y S, Lee K H, Yoo J M, Jeong M J, Park S K, Lee K M, Song C K, Kim S W, Kim Y J, Kim S W, Kim M, Go S, Liu X, Chance K, Miller C C, Al-Saadi J, Veihelmann B, Bhartia P K, Torres O, Abad G G, Haffner D P, Ko D H, Lee S H, Woo J H, Chong H E S N, Park S S, Nicks D, Choi W J, Moon K J, Cho A, Yoon J, Kim S K, Hong H, Lee K, Lee H, Lee S, Choi M, Veefkind P, Levelt P F, Edwards D P, Kang M N, Eo M, Bak J, Baek K, Kwon H A, Yang J W N, Park J, Han K M, Kim B R, Shin H W, Choi H, Lee E, Chong J, Cha Y, Koo J H, Irie H, Hayashida S, Kasai Y, Kanaya Y, Liu C, Lin J T, Crawford J H, Carmichael G R, Newchurch M J, Lefer B L, Herman J R, Swap R J, Lau A K H, Kurosu T P, Jaross G, Ahlers B, Dobber M, McElroy C T and Choi Y. 2020. New era of air quality monitoring from space: geostationary environment monitoring spectrometer (GEMS). Bulletin of the American Meteorological Society, 101(1): E1-E22
  42. 42.
    Klaes K D. 2018. The EUMETSAT polar system. Comprehensive Remote Sensing, 1: 192-219
  43. 43.
    Koelemeijer R B A, de Haan J F and Stammes P. 2003. A database of spectral surface reflectivity in the range 335—772 nm derived from 5.5 years of GOME observations. Journal of Geophysical Research: Atmospheres, 108(D2): 4070
  44. 44.
    Krotkov N A, Carn S A, Krueger A J, Bhartia P K and Yang K. 2006. Band residual difference algorithm for retrieval of SO2 from the aura ozone monitoring instrument (OMI). IEEE Transactions on Geoscience and Remote Sensing, 44(5): 1259-1266
  45. 45.
    Levelt P F, Joiner J, Tamminen J, Veefkind J P, Bhartia P K, Stein Zweers D C, Duncan B N, Streets D G, Eskes H, Van Der A R, McLinden C, Fioletov V, Carn S, De Laat J, Deland M, Marchenko S, McPeters R, Ziemke J, Fu D J, Liu X, Pickering K, Apituley A, González Abad G, Arola A, Boersma F, Chan Miller C, Chance K, De Graaf M, Hakkarainen J, Hassinen S, Ialongo I, Kleipool Q, Krotkov N, Li C, Lamsal L, Newman P, Nowlan C, Suleiman R, Tilstra L G, Torres O, Wang H Q and Wargan K. 2018. The Ozone Monitoring Instrument: overview of 14 years in space. Atmospheric Chemistry and Physics, 18(8): 5699-5745
  46. 46.
    Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R and Eck T F. 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420
  47. 47.
    Li C, Krotkov N A, Carn S, Zhang Y, Spurr R J D and Joiner J. 2017. New-generation NASA aura ozone monitoring instrument (OMI) volcanic SO2 dataset: algorithm description, initial results, and continuation with the suomi-NPP ozone mapping and profiler suite (OMPS). Atmospheric Measurement Techniques, 10(2): 445-458
  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. 2018. 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.
    Lin J T, McElroy M B and Boersma K F. 2010. Constraint of anthropogenic NOx emissions in China from different sectors: a new methodology using multiple satellite retrievals. Atmospheric Chemistry and Physics, 10(1): 63-78
  50. 50.
    Lin J T, Liu M Y, Xin J Y, Boersma K F, Spurr R, Martin R and Zhang Q. 2015. Influence of aerosols and surface reflectance on satellite NO2 retrieval: seasonal and spatial characteristics and implications for NOx emission constraints. Atmospheric Chemistry and Physics, 15(19): 11217-11241
  51. 51.
    Lin J T, Martin R V, Boersma K F, Sneep M, Stammes P, Spurr R, Wang P, Van Roozendael M, Clémer K and Irie H. 2014. Retrieving tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen dioxide. Atmospheric Chemistry and Physics, 14(3): 1441-1461
  52. 52.
    Liu M Y, Lin J T, Kong H, Boersma K F, Eskes H, Kanaya Y, He Q, Tian X, Qin K, Xie P H, Spurr R, Ni R J, Yan Y Y, Weng H J and Wang J X. 2020. A new TROPOMI product for tropospheric NO2 columns over East Asia with explicit aerosol corrections. Atmospheric Measurement Techniques, 13(8): 4247-4259
  53. 53.
    Liu Y N, Sun D X, Cao K Q, Liu S F, Chai M Y, Liang J and Yuan J. 2020. Evaluation of GF-5 AHSI on-orbit instrument radiometric performance. Journal of Remote Sensing, 24(4): 352-359
  54. 54.
    Lorente A, Boersma K F, Yu H, Dörner S, Hilboll A, Richter A, Liu M Y, Lamsal L N, Barkley M, De Smedt I, Van Roozendael M, Wang Y, Wagner T, Beirle S, Lin J T, Krotkov N, Stammes P, Wang P, Eskes H J and Krol M. 2016. Structural uncertainty in air mass factor calculation for NO2 and HCHO satellite retrievals. Atmospheric Measurement Techniques, 10(3): 759-782
  55. 55.
    Luo S, Di D and Cui L L. 2019. Study on FY-4A/GIIRS infrared spectrum detection capability based on information content. Journal of Infrared and Millimeter Waves, 38(6): 765-776
  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.
    Maignan F, Bréon F M, Fédèle E and Bouvier M. 2009. Polarized reflectances of natural surfaces: spaceborne measurements and analytical modeling. Remote Sensing of Environment, 113(12): 2642-2650
  58. 58.
    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
  59. 59.
    Migeotte M V. 1948. Lines of methane at 7.7μ in the solar spectrum. Physical Review, 74(1): 112-113
  60. 60.
    Migeotte M V. 1949. The fundamental band of carbon monoxide at 4.7μ in the solar spectrum. Physical Review, 75(7): 1108-1109
  61. 61.
    Munro R, Lang R, Klaes D, Poli G, Retscher C, Lindstrot R, Huckle R, Lacan A, Grzegorski M, Holdak A, Kokhanovsky A, Livschitz J and Eisinger M. 2016. The GOME-2 instrument on the Metop series of satellites: instrument design, calibration, and level 1 data processing - An overview. Atmospheric Measurement Techniques, 9(3): 1279-1301
  62. 62.
    Nowlan C R, Liu X, Chance K, Cai Z, Kurosu T P, Lee C and Martin R V. 2011. Retrievals of sulfur dioxide from the global ozone monitoring experiment 2 (GOME-2) using an optimal estimation approach: algorithm and initial validation. Journal of Geophysical Research, 116(D18): D18301
  63. 63.
    Perner D, Ehhalt D H, Pätz H W, Platt U, Röth E P and Volz A. 1976. OH-Radicals in the lower troposphere. Geophysical Research Letters, 3(8): 466-468
  64. 64.
    Platt U and Stutz J. 2008. Differential Optical Absorption Spectroscopy. Berlin Heidelberg: Springer
  65. 65.
    Qiu J H. 1997. Principle and inversion method of atmospheric aerosol optical thickness and vegetation from space remote sensing//Lv D R, ed. Earth Environment and Climate Change Detection and Process Research. Beijing: China Meteorological Press: 71-77
  66. 66.
    Rodgers C D. 2000. Inverse Methods for Atmospheric Sounding: Theory and Practice. Singapore: World Scientific
  67. 67.
    Schönbein C F. 1840. Beobachtungen über den bei der Elektrolysation des Wassers und dem Ausströmen der gewöhnliehen Elektricität aus Spitzen sich entwikkelnden Geruch. Annalen der Physik, 126(8): 616-635
  68. 68.
    Shin M, Kang Y, Park S, Im J, Yoo C and Quackenbush L J. 2020. Estimating ground-level particulate matter concentrations using satellite-based data: a review. GIScience and Remote Sensing, 57(5): 174-189
  69. 69.
    Strow L L, Hannon S E, De Souza-Machado S, Motteler H E and Tobin D. 2003. An overview of the AIRS radiative transfer model. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 303-313
  70. 70.
    Tanré D, Bréon F M, Deuzé J L, Dubovik O, Ducos F, François P, Goloub P, Herman M, Lifermann A and Waquet F. 2011. Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: the PARASOL mission. Atmospheric Measurement Techniques, 4(7): 1383-1395
  71. 71.
    Veefkind J P, Aben I, McMullan K, Förster H, De Vries J, Otter G, Claas J, Eskes H J, De Haan J F, Kleipool Q, Van Weele M, Hasekamp O, Hoogeveen R, Landgraf J, Snel R, Tol P, Ingmann P, Voors R, Kruizinga B, Vink R, Visser H and Levelt P F. 2012. TROPOMI on the ESA Sentinel-5 precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120: 70-83
  72. 72.
    Wang W H, Flynn L, Zhang X Y, Wang Y M, Wang Y J, Jiang F, Zhang Y, Huang F X, Li X J, Liu R X, Zheng Z J, Yu W and Liu G Y. 2012. Cross-calibration of the total ozone unit (TOU) with the ozone monitoring instrument (OMI) and SBUV/2 for environmental applications. IEEE Transactions on Geoscience and Remote Sensing, 50(12): 4943-4955
  73. 73.
    Wang Z T, Ma P F, Zhang L J, Chen H, Zhao S H, Zhou W, Chen C H, Zhang Y H, Zhou C Y, Mao H Q, Wang Y, Wang Y L, Zhang L H, Zhao A M, Weng G Q and Hu K W. 2021. Systematics of atmospheric environment monitoring in China via satellite remote sensing. Air Quality, Atmosphere and Health, 14(2): 157-169
  74. 74.
    Waquet F, Goloub P, Deuzé J L, Léon J F, Auriol F, Verwaerde C, Balois J Y and François P. 2007. Aerosol retrieval over land using a multiband polarimeter and comparison with path radiance method. Journal of Geophysical Research: Atmospheres, 112(D11): D11214
  75. 75.
    Waquet F, Léon J F, Cairns B, Goloub P, Deuzé J L and Auriol F. 2009. Analysis of the spectral and angular response of the vegetated surface polarization for the purpose of aerosol remote sensing over land. Applied Optics, 48(6): 1228
  76. 76.
    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
  77. 77.
    Yan H, Chen L, Tao J, Su L, Huang J, Han D and Yu C. 2012. Corrections for OMI SO2 BRD retrievals influenced by row anomalies. Atmospheric Measurement Techniques, 5(11): 2635-2646
  78. 78.
    Yan H H, Li X J, Wang W H, Zhang X Y, Chen L F, Han D, Yu C and Gao L. 2017. Comparison of SO2 column retrievals from BRD and DOAS algorithms. Science China Earth Sciences, 60(9): 1694-1706
  79. 79.
    Yang K, Bhartia P K, Wellemeyer C G, Qin W, Spurr R J D, Veefkind J P and de Haan J F. 2004. Application of spectral fitting method to GOME and comparison with OMI-DOAS and TOMS-V8 total ozone//Proceedings Quadrennial Ozone Symposium. Kos, Greece: [s.n.]
  80. 80.
    Yang K, Krotkov N A, Krueger A J, Carn S A, Bhartia P K and Levelt P F. 2007. Retrieval of large volcanic SO2 columns from the Aura Ozone Monitoring Instrument: comparison and limitations. Journal of Geophysical Research: Atmosphere, 112: D24S43
  81. 81.
    Zhang C X, Liu C, Chan K L, Hu Q H, Liu H R, Li B, Xing C Z, Tan W, Zhou H J, Si F Q and Liu J G. 2020. First observation of tropospheric nitrogen dioxide from the Environmental Trace Gases Monitoring Instrument onboard the GaoFen-5 satellite. Light: Science and Applications, 9(1): 66
  82. 82.
    Zhang X Y, Wang F, Wang W H, Huang F X, Chen B L, Gao L, Wang S P, Yan H H, Ye H H, Si F Q, Hong J, Li X Y, Cao Q, Che H Z and Li Z Q. 2020. The development and application of satellite remote sensing for atmospheric compositions in China. Atmospheric Research, 245: 105056
  83. 83.
    Zhang X Y, Zhang P, Fang Z Y, Qiu H, Li X J and Zhang Y. 2007. The progress in trace gas remote sensing study based on the satellite monitoring. Meteorological Monthly, 33(7): 3-14
  84. 84.
    Zhang Y, Li Z Q, Bai K X, Wei Y Y, Xie Y S, Zhang Y X, Ou Y, Cohen J, Zhang Y H, Peng Z R, Zhang X Y, Chen C, Hong J, Xu H, Guang J, Lv Y, Li K T and Li D H. 2021. Satellite remote sensing of atmospheric particulate matter mass concentration: advances, challenges, and perspectives. Fundamental Research, 1(3): 240-258
  85. 85.
    Zhang Y, Li Z Q, Liu Z H, Zhang J, Qie L L, Xie Y S, Hou W A, Wang Y Q and Ye Z X. 2018. Retrieval of the fine-mode aerosol optical depth over east china using a grouped residual error sorting (GRES) method from multi-angle and polarized satellite data. Remote Sensing, 10(11): 1838
  86. 86.
    Zhang Y H, Li Z Q, Zhang Y, Hou W Z, Xu H, Chen C and Ma Y. 2014. High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: application and initial validation. Journal of Applied Remote Sensing, 8(1): 083612
  87. 87.
    Zhu L, Jacob D J, Kim P S, Fisher J A, Yu K R, Travis K R, Mickley L J, Yantosca R M, Sulprizio M P, De Smedt I, Abad G G, Chance K, Li C, Ferrare R, Fried A, Hair J W, Hanisco T F, Richter D, Jo Scarino A, Walega J, Weibring P and Wolfe G M. 2016. Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the southeast US. Atmospheric Chemistry and Physics, 16(21): 13477-13490

Читать полностью

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