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  • 1  

       Atmospheric Correction for Dust Contaminated Ocean Regions  

    Menghua Wang and Wei Shi

    NOAA/NESDIS/STAR

    E/RA3, Room 102, 5200 Auth Rd.

    Camp Springs, MD 20746, USA  

    Report of FY11 NASA ACE Funded Project

    March 14, 2012

    Acknowledgements:  We  thank  Oleg  Dubovik  and  the   AERONET  group  for  providing  dust  model  data.   MODIS  and  CALIPSO  data  were  obtained  from   NASA/GSFC  and  NASA  Langley  Research  Center   Atmospheric  Science  Data  Center.  

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    Project Summary: This is a demonstration study for deriving improved MODIS-Aqua ocean color products over dust-contaminated ocean regions using the dust vertical profile data from CALIPSO and dust models that have been developed from the AERONET ground-based measurements.

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    Current  Satellite  Ocean  Color  Retrievals  Under  Dust  Condi;on    

    1.  World  oceans  are  frequently   covered  with  dust,  especially  in   the  West  Africa  coast,  Arabian   Sea  and  Persian  Gulf,  US  west   coast,  etc.  

    2.  Dust  aerosols  are  strongly   absorbing  in  the  blue  and  deep   blue  band.  

    3.  Current  aerosol  models  for   satellite  ocean  color  processing   are  not  working  under  dust   condiPon  (also  need  aerosol   verPcal  distribuPon  info).  

    4.  Shi  and  Wang  (2007)  developed   a  method  to  detect  absorbing   aerosols,  e.g.,  dust,  smoke.  

    Shi, W., and Wang, M. (2007), Detection of turbid waters and absorbing aerosols for the MODIS ocean color data processing, Remote Sens. Environ., 110, 149-161.

  • 4  

    Efforts in Addressing Absorbing Aerosol Issue   Ø  There have been significant efforts for addressing dust aerosol issue & its

    effects on ocean color remote sensing (list a few): –  Gordon, H. R., Du, T., and Zhang, T. (1997), Remote sensing of ocean color and aerosol

    properties: resolving the issue of aerosol absorption, Appl. Opt., 36, 8670-8684. –  Fukushima, H., and Toratani, H. (1997), Asian dust aerosol: optical effect on satellite

    ocean color signal and a scheme of its correction, J. Geophys. Res., 102, 17119-17130. –  Moulin, C., Gordon, H. R., Banzon, V. F., and Evans, R. H. (2001a), Assessment of

    Saharan dust absorption in the visible from SeaWiFS imagery, J. Geophys. Res., 106, 18,239-218,249.

    –  Moulin, C., Gordon, H. R., Chomko, R. M., Banzon, V. F., and Evans, R. H. (2001b), Atmospheric correction of ocean color imagery through thick layers of Saharan dust, Geophys. Res. Letters, 28, 5-8.

    –  Claustre, H., Morel, A., Hooker, S.B., Babin, M., Antoine, D., Oubelkheir, K., Bricaud, A., Leblanc, K., Queuiner, B. and Maritorena, S. (2002), Is desert dust making oligotrophic water greener? Geophy. Research Letter, 29, 1469, doi: 10.1029/2001GL014056.

    –  Cattrall, C., Carder, K. L., and Gordon, H. R. (2003), Columnar aerosol single-scattering albedo and phase function retrieved from sky radiance over the ocean: Measurements of Saharan dust, J. Geophys. Res., 108 (D9), 4287, doi:10.1029/2002JD002497.

    –  Wiggert, J. D., Murtugudde, R. G. and Christian, J. R. (2006), Annual ecosystem variability in the tropical Indian Ocean: Results of a coupled bio-physical ocean general circulation model. Deep-Sea Research Part II, 53: 644-676.

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    AERONET Dust Aerosol Model   Ø AERONET dust models developed by Dubovik et al. are used for

    generating aerosol lookup tables: –  Dubovik, O., Holben, B. N., Eck, T. F., Smirnov, A., Kaufman, Y. J., King,

    M. D., Tanre, D., and Slutsker, I. (2002a), Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590-608.

    –  Dubovik, O., Holben, B. N., Lapyonok, T., Sinyuk, A., Mishchenko, M., Yang, P., and Slutsker, I. (2002b), Non-spherical aerosol retrieval method employing light scattering by spheroids, Geophy. Res. Lett., 29, 1451, doi: 1410.1029/2001GL014506.

    –  Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang, P., Eck, T. F., Volten, H., Munoz, O., Veihelmann, B., Zande, W. J. v. d., Leon, J.-F., Sorokin, M., and Slutsker, I. (2006), Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust, J. Geophys. Res., 111, D11208, doi: 11210.11029/12005JD006619.

     

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    Dust  Aerosol  Sca

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    Dust  Aerosol  Proper;es:  Single-­‐sca

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    Dust Aerosol Lookup Tables   Ø Dust aerosol lookup tables (including atmospheric diffuse

    transmittance tables) were generated with the vector radiative transfer model for different aerosol vertical profiles located at (from bottom): 0-km, 1-km, 2-km, 4-km, 6-km, 8-km, 10-km, and 99-km.

    Ø  4 dust aerosol size distributions corresponding to AOT at 1020 nm of 0.3, 0.6, 1.0, and 1.5.

    Ø  14 dust AOT at 865 nm are: 0.02, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.5, 2.0, 2.5, 3.0.

    Ø  Solar-zenith angles from 0 to 80 (Deg.) at every 2.5 (Deg.). Ø  Sensor-zenith angles from 1 to 75 (Deg.) at every ~2 (Deg.). Ø Relative azimuth angle from 0 to 180 (Deg.) at every 10 (Deg.). Ø MODIS 16 spectral bands at 412, 443, 469, 488, 531, 551, 555,

    640, 667, 678, 748, 859, 869, 1240, 1640, and 2130 nm.  

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    TOA  Reflectance  

    10-2

    10-1

    400 800 1200 1600 2000

    τa(869) = 0.1 τa(869) = 0.3

    τa(869) = 0.6 τa(869) = 1.0

    TO A

    Re fle

    ct an

    ce

    Wavelength (nm)

    TOA Typical Radiance, θ0 = 20 o, θ = 45o, Δφ = 90o

    AERONET Dust Model, 4km Layer, Black Ocean (c)

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    Effects  of  Dust  Aerosol  Ver;cal  Distribu;on  

    0

    0.005

    0.01

    0.015

    400 500 600 700 800

    1km vs. 3km bottom-layer 1km vs. 6km bottom-layer 1km vs. 9km bottom-layer

    TO A

    Re fle

    ct an

    ce D

    iff er

    en ce

    Wavelength (nm)

    τa(869) = 0.6, θ0 = 60 o, θ = 45o, Δφ = 90o

    AERONET Dust Model, Black Ocean

    (d)

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    Atmospheric  Correc;on:   Simula;ons  

    -0.002

    -0.001

    0

    0.001

    0.002

    0 10 20 30 40 50 60 70 80

    412 nm 443 nm 488 nm 531 nm 551 nm

    Solar Zenith Angle (Deg.)

    τa(865) = 0.1, θ = 20 o, Δφ = 90o

    (a)

    Er ro

    r Δ [t ρ

    w (λ ) ]

    The SWIR Algorithm: 1240 nm and 2130 nm Aeronet Dust Model (dH) at 3km bottom layer Assuming: dust at 2km layer

    -0.003

    -0.002

    -0.001

    0

    0.001

    0 10 20 30 40 50 60 70 80

    412 nm 443 nm 488 nm 531 nm 551 nm

    Solar Zenith Angle (Deg.)

    τa(865) = 0.3, θ = 20 o, Δφ = 90o

    (b)

    Er ro

    r Δ [tρ

    w ( λ )]

    The SWIR Algorithm: 1240 nm and 2130 nm Aeronet Dust Model (dH) at 3km bottom layer Assuming: dust at 2km layer

    Derived  water-­‐leaving   reflectances  are  biased  low  due   to  a  wrong  assumpPon  of  dust   aerosol  layer  (more  so  for  larger   aerosol  opPcal  thickness  at   shorter  wavelengths).  

    Dust  layer  at  3-­‐km,  but   assumed  at  2-­‐km.  

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    NASA  Cloud-­‐Aerosol  Lidar  and  Infrared  Pathfinder   Satellite  Observa;on  (CALIPSO)  

    •   Launched  on  April  28,  2006   •   Part  of  the  Aqua  satellite  constellaPon  (or  A-­‐Train)   •   CALIPSO  lags  MODIS-­‐Aqua  by  1  to  2  minutes.   •   Wavelengths:  532  nm  &  1064  nm   •   Pulse  energy:  110  mJoule/channel   •   Footprint/FOV:  100  m/  130  µrad   •   VerPcal  resoluPon:  30-­‐60  m   •   Horizontal  resoluPon:  333  m  

  • 13  

    CALIPSO  L2  Aerosol  &  Cloud  Products  

    An  example  of  data  collected  by   CALIPSO's  lidar  in  June  2006   Aerosols  • Height,  Thickness  

    • OpPcal  depth,  τ   • Backscager,  &  betaa(z)   • ExPncPon,  σa  

    Clouds   • Height   • Thickness   • OpPcal  depth,  τ   • Backscager,  &betac(z)   • ExPncPon,  σc   • Ice/water  phase   • Ice  cloud  emissivity,  ε   • Ice  parPcle  size  

  • 14  

    CASE  ONE  :  Dust  In  Japan  Sea  on  5/26/2007   MODIS  Granule  (2007146)  

    Ca lip so  tr ac k  

    Dust  height  0–2.5  km  

    MODIS  True  Color  Image  and  CALIPSO

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