Science in China Series D: Earth Sciences

, Volume 52, Issue 1, pp 132–144 | Cite as

Review of the applications of Multiangle Imaging SpectroRadiometer to air quality research

  • Yang Liu
  • Dan Chen
  • Ralph A. Kahn
  • KeBin HeEmail author


The Multiangle Imaging SpectroRadiometer (MISR) launched by NASA in late 1999 has a unique multiangle design, which points nine cameras at fixed angles along the satellite flight track and collects reflected solar radiation simultaneously. This design allows the retrieval of a rich dataset of particle abundance, shape and composition over both land and ocean. Some of its capabilities have not been seen by any currently operating satellite aerosol sensors. Since MISR is sensitive to fine particles, it provides a new data source to study the spatial and temporal characteristics of air quality over large geographical regions. We first briefly introduce the MISR instrument, the retrieval and structure of MISR aerosol data, and then review the applications of MISR aerosol data in various aspects of air quality research since its launch. These include the spatial distributions of particle pollution events such as dust storms, wild fires, and urban pollution. Because of the high quality of MISR aerosol data, they can be used as quantitative indicators of particle pollution levels. We review the current modeling studies of surface level particle concentrations. Next, we introduce research results using MISR’s advanced data such as the plume heights, and particle microphysical properties. In the discussion, we compare MISR research with current MODIS research to the best of our ability as MODIS data have been more extensively explored by the Chinese scientific community. Finally, we summarize the advantages and disadvantages of MISR data related to its applications to the air quality research. Given the highly quantitative measurements and comprehensive aerosol information MISR can provide, we believe that it will provide great values to advance our understanding of the particle air pollution in China.


aerosol remote sensing MISR multiangle imaging aerosol optical thickness particulate matters PM2.5 PM10 air pollution 


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  1. 1.
    Samet J M, Dominici F, Curriero F C, et al. Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N Engl J Med, 2000, 343(24): 1742–1749CrossRefGoogle Scholar
  2. 2.
    Vedal S, Petkau J, White R, et al. Acute effects of ambient inhalable particles in asthmatic and nonasthmatic children. Am J Respir Crit Care Med, 1998, 157(4): 1034–1043Google Scholar
  3. 3.
    Pope C A, Dockery D W. Health effects of fine particulate air pollution: Lines that connect. J Air Waste Manage Assoc, 2006, 56(6): 709–742Google Scholar
  4. 4.
    Smith K R, Jantunen M. Why particles? Chemosphere, 2002, 49(9): 867–871CrossRefGoogle Scholar
  5. 5.
    He K B, Yang F M, Ma Y L, et al. The characteristics of PM2.5 in Beijing. China. Atmos Environ, 2001, 35(29): 4959–4970CrossRefGoogle Scholar
  6. 6.
    Ye B, Jia X, Yang H, et al. Concentration and chemical composition of PM2.5 in Shanghai for a 1-year period. Atmos Environ, 2003, 37(4): 499–510CrossRefGoogle Scholar
  7. 7.
    Kaufman Y J, Herring D, Ranson K, et al. Earth observing system AM 1 mission to Earth. IEEE Trans Geosci Remote Sensing, 1998, 36(4): 1045–1055CrossRefGoogle Scholar
  8. 8.
    Remer L A, Kaufman Y J, Tanre D, et al. The MODIS aerosol algorithm, products, and validation. J Atmos Sci, 2005, 62(4): 947–973CrossRefGoogle Scholar
  9. 9.
    Diner D, Beckert J, Reilly T H, et al. Multi-angle Imaging Spectro-Radiometer (MISR) instrument description and experiment overview. IEEE Trans Geosci Remote Sensing, 1998, 36(4): 1072–1087CrossRefGoogle Scholar
  10. 10.
    Martonchik J V, Diner D J, Kahn R A, et al. Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging. IEEE Trans Geosci Remote Sensing, 1998, 36(4): 1212–1227CrossRefGoogle Scholar
  11. 11.
    Diner D J, Braswell B H, Davies R, et al. The value of multiangle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces. Remote Sens Environ, 2005, 97(4): 495–518CrossRefGoogle Scholar
  12. 12.
    Feng X, Zhao S, Chen Y. Inversion of MISR Broadband albedo and its relationship with atmospheric conditions (in Chinese). Remote Sens Land Resour, 2003, 58(4): 22–25Google Scholar
  13. 13.
    Han B, Kang L, Chen Y, et al. An optimized fusion predictor for MISR remote sensing data (in Chinese). J Wuhan Univ Technol, 2006, 28(7): 97–100Google Scholar
  14. 14.
    Han B, Kang L, Chen Y, et al. A fusion prediction model for spatial target based on remote sensing data (in Chinese). Comput Eng, 2006, 32(14): 35–39Google Scholar
  15. 15.
    Kahn R, Banerjee P, McDonald D. Sensitivity of multiangle imaging to natural mixtures of aerosols over ocean. J Geophys Res, 2001, 106(D16): 18219–18238CrossRefGoogle Scholar
  16. 16.
    Kahn R, Banerjee P, McDonald D, et al. Sensitivity of multiangle imaging to aerosol optical depth and to pure-particle size distribution and composition over ocean. J Geophys Res, 1998, 103(D24): 32195–32213CrossRefGoogle Scholar
  17. 17.
    Kalashnikova O V, Kahn R. Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: 2. Sensitivity over dark water. J Geophys Res, 2006, 111(D11): Art. No. D11207Google Scholar
  18. 18.
    Kalashnikova O V, Kahn R, Sokolik I N, et al. Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: Optical models and retrievals of optically thick plumes. J Geophys Res, 2005, 110(D18): D18S14CrossRefGoogle Scholar
  19. 19.
    Holben B, Eck T F, Slutsker I, et al. AERONET: A federated instrument network and data archive for aerosol characterization. Remote Sens Environ, 1998, 66(1): 1–16CrossRefGoogle Scholar
  20. 20.
    Smirnov A. Cloud-screening and quality control algorithms for the AERONET database. Remote Sens Environ, 2000, 73(3): 337–349CrossRefGoogle Scholar
  21. 21.
    Russell P B, Livingston J M, Redemann J, et al. Multi-grid-cell validation of satellite aerosol property retrievals in INTEX/ITCT/ICARTT 2004. J Geophys Res, 2007, 112(D12): D12S09CrossRefGoogle Scholar
  22. 22.
    Schmid B, Redemann J, Russell P, et al. Coordinated airborne, spaceborne, and ground-based measurements of massive thick aerosol layers during the dry season in southern Africa. J Geophys Res, 2003, 108(D13): 8496CrossRefGoogle Scholar
  23. 23.
    Liu Y, Sarnat J A, Coull B A, et al. Validation of multiangle imaging spectroradiometer (MISR) aerosol optical thickness measurements using aerosol robotic network (AERONET) observations over the contiguous United States. J Geophys Res, 2004, 109(D6): D06205CrossRefGoogle Scholar
  24. 24.
    Kahn R A, Gaitley B J, Martonchik J V, et al. Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations. J Geophys Res, 2005, 110(D10): D10S04CrossRefGoogle Scholar
  25. 25.
    Abdou W A, Diner D J, Martonchik J V, et al. Comparison of coincident Multiangle Imaging SpectroRadiometer and Moderate Resolution Imaging Spectroradiometer aerosol optical depths over land and ocean scenes containing Aerosol Robotic Network sites. J Geophys Res, 2005, 110(D10): D10S07CrossRefGoogle Scholar
  26. 26.
    Prasad A, Singh R. Comparison of MISR-MODIS aerosol optical depth over the Indo-Gangetic basin during the winter and summer seasons (2000–2005). Remote Sens Environ, 2007, 107(1–2): 109–119CrossRefGoogle Scholar
  27. 27.
    Li C C, Mao J T, Lau K-H A, et al. Characteristics of distribution and seasonal variation of aerosol optical depth in eastern China with MODIS products. Chin Sci Bull, 2003, 48(22): 2488–2495Google Scholar
  28. 28.
    Martonchik J, Diner D, Kahn R, et al. Comparison of MISR and AERONET aerosol optical depths over desert sites. Geophys Res Lett, 2004, 31(16): L16102CrossRefGoogle Scholar
  29. 29.
    Christopher S A, Wang J. Intercomparison between Multi-angle Imaging SpectRoradiometer (MISR) and sunphotometer aerosol optical thickness in dust source regions over China: Implications for satellite aerosol retrievals and radiative forcing calculations. Tellus Ser B-Chem Phys Meteorol, 2004, 56(5): 451–456CrossRefGoogle Scholar
  30. 30.
    Jiang X, Liu Y, Yu B, et al. Comparison of MISR aerosol optical thickness with AERONET measurements in Beijing metropolitan area. Remote Sens Environ, 2007, 107(1–2): 45–53CrossRefGoogle Scholar
  31. 31.
    Solomon F, Giorgi F, Liousse C. Aerosol modelling for regional climate studies: Application to anthropogenic particles and evaluation over a European/African domain. Tellus Ser B-Chem Phys Meteorol, 2006, 58(1): 51–72CrossRefGoogle Scholar
  32. 32.
    Liu L, Lacis A A, Carlson B E, et al. Assessing Goddard Institute for Space Studies ModelE aerosol climatology using satellite and ground-based measurements: A comparison study. J Geophys Res, 2006, 111(D20): D20212CrossRefGoogle Scholar
  33. 33.
    Li C C, Mao J T, Liu Q. Characteristics of aerosol optical depth distributions over Sichuan Basin derived from MODIS data. J Appl Meteorol, 2003, 14(1): 1–7Google Scholar
  34. 34.
    Liu G, Mao J T, Li C C. Optical depth study on atmospheric aerosol in Yangtze River Delta region, Shanghai (in Chinese). Environ Sci, 2003, 22(Suppl.): 58–63Google Scholar
  35. 35.
    Li C C, Liu Q, Mao J T, et al. An aerosol pollution episode in Hongkong with remote sensing products of MODIS and Lidar. J Appl Meteorol, 2004, 15(6): 641–651Google Scholar
  36. 36.
    Martonchik J, Diner D J, Crean K A, et al. Regional aerosol retrieval results from MISR. IEEE Trans Geosci Remote Sensing, 2002, 40(7): 1520–1531CrossRefGoogle Scholar
  37. 37.
    Kahn R A, Li W H, Moroney C, et al. Aerosol source plume physical characteristics from space-based multiangle imaging. J Geophys Res, 2007, 112(D11): D11205CrossRefGoogle Scholar
  38. 38.
    Frank T D, Di Girolamo L, Geegan S. The spatial and temporal variability of aerosol optical depths in the Mojave Desert of southern California. Remote Sens Environ, 2007, 107(1–2): 54–64CrossRefGoogle Scholar
  39. 39.
    Prasad A K, Singh R P, Kafatos M. Influence of coal based thermal power plants on aerosol optical properties in the Indo-Gangetic basin. Geophys Res Lett, 2006, 33(5): L05805CrossRefGoogle Scholar
  40. 40.
    Di Girolamo L, Bond T C, Bramer D, et al. Analysis of Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depths over greater India during winter 2001–2004. Geophys Res Lett, 2004, 31(23): L23115CrossRefGoogle Scholar
  41. 41.
    Jordan C E, Dibb J E, Anderson B E, et al. Uptake of nitrate and sulfate on dust aerosols during TRACE-P. J Geophys Res, 2003, 108(D21): 1–10Google Scholar
  42. 42.
    Xia L, Wang D, Wang F. Researches on the advanced warning system and advanced warning grade of the photochemical smog pollution in Guangzhou city based on MODIS data (in Chinese). Remote Sens Land Resour, 2006, 70(4): 73–76Google Scholar
  43. 43.
    Wang H, Zha Y. Urban air quality by MODIS AOT products (in Chinese). Urban Environ Urban Ecol, 2006, 19(3): 21–24Google Scholar
  44. 44.
    Han J, Wang S, Qi B, et al. Distribution of aerosol optical thickness and its relation with dusty weather in China (in Chinese). J Desert Res, 2006, 26(3): 362–369Google Scholar
  45. 45.
    Li C C, Mao J T, Liu Q, et al. Research on the air pollution in Beijing and its surroundings with MODIS AOD products (in Chinese). Chin J Atmos Sci, 2003, 27(5): 869–882Google Scholar
  46. 46.
    Sun J, Su J, Lu X, et al. Application of aerosol optical depth data from MODIS to retrieve visibility. Environ Sci Manag, 2006, 31(5): 97–101Google Scholar
  47. 47.
    Chow J, Watson J, Lowenthal D, et al. Comparability between PM2.5 and particle light scattering measurements. Environ Monit Assess, 2002, 79(1): 29–45CrossRefGoogle Scholar
  48. 48.
    Li C C, Lau K-H A, Mao J T, et al. Retrieval, validation, and application of the 1-km aerosol optical depth from MODIS measurements over Hong Kong. IEEE Trans Geosci Remote Sensing, 2005, 43(11): 2650–2658CrossRefGoogle Scholar
  49. 49.
    Liu Y, Sarnat J A, Kilaru A, et al. Estimating ground-level PM2.5 in the eastern united states using satellite remote sensing. Environ Sci Technol, 2005, 39(9): 3269–3278CrossRefGoogle Scholar
  50. 50.
    Liu Y, Franklin M, Kahn R, et al. Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS. Remote Sens Environ, 2007, 107(1–2): 33–44CrossRefGoogle Scholar
  51. 51.
    Vermote E F, Roger J C, Sinyuk A, et al. Fusion of MODIS-MISR aerosol inversion for estimation of aerosol absorption. Remote Sens Environ, 2007, 107(1–2): 81–89CrossRefGoogle Scholar
  52. 52.
    Liu Y, Park R J, Jacob D J, et al. Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States. J Geophys Res, 2004, 109(D22): D22206CrossRefGoogle Scholar
  53. 53.
    Bey I, Jacob D J, Yantosca R M, et al. Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation. J Geophys Res, 2001, 106(D19): 23073–23095CrossRefGoogle Scholar
  54. 54.
    Park R J, Jacob D J, Field B D, et al. Natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United States: Implications for policy. J Geophys Res, 2004, 109(D15): D15204CrossRefGoogle Scholar
  55. 55.
    Ginoux P, Chin M, Tegen I, et al. Sources and distributions of dust aerosols simulated with the GOCART model. J Geophys Res, 2001, 106(D17): 20255–20273CrossRefGoogle Scholar
  56. 56.
    Van Donkelaar A, Martin R V, Park R J. Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. J Geophys Res, 2006, 111(D21): D21201CrossRefGoogle Scholar
  57. 57.
    Muller J P, Mandanayake A, Moroney C, et al. MISR stereoscopic image matchers: Techniques and results. IEEE Trans Geosci Remote Sensing, 2002, 40(7): 1547–1559CrossRefGoogle Scholar
  58. 58.
    Stenchikov G, Lahoti N, Diner D J, et al. Multiscale plume transport from the collapse of the World Trade Center on September 11, 2001. Environ Fluid Mech, 2006, 6(5): 425–450CrossRefGoogle Scholar
  59. 59.
    Mazzoni D, Logan J, Diner D, et al. A data-mining approach to associating MISR smoke plume heights with MODIS fire measurements. Remote Sens Environ, 2007, 107(1–2): 138–148CrossRefGoogle Scholar
  60. 60.
    Liu Y, Kahn R, Koutrakis P. Estimating PM2.5 component concentrations and size distributions using satellite retrieved fractional aerosol optical depth: Part I—Method development. J Air Waste Manage Assoc, 2007, 57(11): 1351–1359Google Scholar
  61. 61.
    Liu Y, Kahn R, Turquety S, et al. Estimating PM2.5 component concentrations and size distributions using satellite retrieved fractional aerosol optical depth: Part II—A case study. J Air Waste Manage Assoc, 2007, 57(11): 1360–1369CrossRefGoogle Scholar

Copyright information

© Science in China Press and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.School of Public HealthHarvard UniversityBostonUSA
  2. 2.Department of Environmental Engineering and SciencesTsinghua UniversityBeijingChina
  3. 3.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA

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