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An algorithmic approach to analyzing aerosol properties over bright-reflecting surfaces using MODIS and SeaWiFS data, focusing on aerosol optical thickness and single-scattering albedo. The Deep Blue Algorithm effectively distinguishes aerosol types and improves retrieval accuracy.
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Aerosol Properties over Bright-Reflecting Source Regions: The Deep Blue Algorithm and its Applicability to MODIS N. Christina Hsu S.-C. Tsay, M. D. King, Y. J. Kaufman, J. R. Herman, and J. C. Wei NASA Goddard Space Flight Center Greenbelt, Maryland USA
645 nm 412 nm 470 nm MODIS Visible & NIR Bands: superimposed on the GOME spectral reflectance taken over the Sahara
3x3 Pixels Spatial Variance at 412 nm 412/490 Absorbing Aerosol Index Cloud Screening or Cloud Mask Algorithm Yes NORETRIEVAL Cloudy? No 490, 670... nm Surface Reflectivity (0.1ºx0.1º) 412 nm Surface Reflectivity (0.1ºx0.1º) Surface Reflectance Determination Dust Model Smoke Model Aerosol Type Dust Dominant Mixed Aerosols Maximum Likelihood Method Aerosol Optical Thickness Ångström Exponent Aerosol Optical Thickness Single-Scattering Albedo + + Flowchart for Deep Blue Algorithm Radiances 412, 490, 670 nm
MODIS/Aqua 412 nm (nadir) SeaWiFS 412 nm (nadir) Surface ReflectanceData Base - Sep 2004 MODIS/Aqua 650 nm (nadir) SeaWiFS 670 nm (nadir)
Tracking Movements and Evolutions of Aerosol Plumes MODIS/Terra – 12 September 2004 MODIS/Aqua 0.0 0.0 2.0 2.0 1.0 1.0 9/12/04 9/12/04 Aqua AOT 1:30 PM LST Terra AOT 10:30 AM LST
412 490 0 412 nm 0 490 nm Aerosol Model Refractive Index 412 nm Refractive Index 490 nm 470 470 Dust 1.00 1.00 1.55 – 0.020i 1.55 – 0.008i 0.91 0.96 Smoke 1.30 0.92 1.55 – 0.022i 1.55 – 0.026i 0.90 0.89 Aerosol Properties in Radiance Simulations • Aerosol layer: 1-km thick, peaked at 3 km height with a Gaussian distribution • For mixed aerosol types: • Rmixed = aRdust+ (1-a)Rsmoke
September 12, 2004 1.04 1.01 0.66 0.88 0.36 0.61 AERONET(500nm) = 0.66 0.56 Deep Blue Algorithm • The dust (coarse particles) front pushes the polluted air mass (fine particles) over both water and land on this day. • Compared reasonably well with AERONET retrievals in UAE2 (Aug.-Sep. 2004) 1.30 0.23 1.13 0.61 0.71 0.43 AERONET =0.44
Comparisons With AERONET Sun Photometer Measurements (August - September 2004) Harmim, UAE Mezaira, UAE
6 April 2001 MODIS Red-Green-Blue with Rayleigh scattering removed Current MODIS retrievals: Aerosol Optical Thickness New MODIS Deep Blue: Aerosol Optical Thickness 0.0 2.0 1.0 0.5 1.0 1.5 2.0 0
SeaWiFS AOT 0.0 0.0 2.0 2.0 1.0 1.0 MISR AOT MODIS AOT 6 April 2001 MODIS
It works! Deep-Blue Algorithmwell for SeaWiFS and MODIS measurements (… as well as future MODIS-like sensors); Compared well with surface/aircraft products; Separate dust well from other anthropogenic sources. Summary • We expect: • Implement Deep-Blue Algorithm for MODIS underway; • Produce MODIS Deep-Blue products over bright-reflecting surfaces, and to be integrated into operational MODIS product stream; • Continue to refine MODIS Deep-Blue retrievals, with polarization correction due to scanning mirror.
1.03 Sfc Ref = 0.07 1.02 1.01 1.00 Sfc Ref = 0.1 0.99 Pixel # Polarization Correction for MODIS • Use MODIS pre-launch polarization coefficientsfrom MCST and GSFC ocean group. • Generate lookup tables • for Stokes parameters and • simulate correction factors. Polarization Correction Factor Scans Surface reflectance = 0.1 Pixels
Deep Blue Algorithm for SeaWiFS/MODIS • Utilize solar reflectance at l = 412, 490, and 670 nm to retrieve aerosol optical thickness (ta) and single scattering albedo (o). • Less sensitive to aerosol height, compared to UV methods. • Works well on retrieving aerosol properties over various types of surfaces, including very bright desert. wo ta wo ta w0(dust670nm)=1.0
Aerosol Optical Thickness Retrieved from Deep Blue Algorithm: Dust plumes in Africa ta
2 May 2004 10 GMT 0.5 0.0 1.0 2.0 1.5 Dust Plume Over Israel SeaWiFS Aerosol Optical Thickness SEDE BOKER - AERONET AOT (500 nm) = 1.3 SeaWiFS AOT (490nm) = 1.1 Aqua/MODIS RGB MODIS AOT -collection 4