Motivation. Aerosol Retrieval Over Urban Areas with High Resolution Hyperspectral Sensors

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1 Motivation Aerosol etrieval Over Urban Areas with High esolution Hyperspectral Sensors Barry Gross (CCNY) Oluwatosin Ogunwuyi (Ugrad CCNY) Brian Cairns (NASA-GISS) Istvan Laszlo (NOAA-NESDIS) Aerosols are difficult to retrieve over land due to ground reflection (highly variable and directional) which can dominate the atmospheric signal Aerosols over urban areas are extremely important for health and climate studies and sources may be much more localized in space. GOES- HES (Hyperspectral Environment Suite) for 22 has plans for a CW imager with high spatial resolution (5 meters). Use Hyperion and AVIIS as precursor sensors to see if accurate aerosol reflectance can be derived over urban environments. NOAA GOES- Hyperspectral Imaging Sensor Capabilities Conventional Aerosol etrievals over Land Select dark pixels (I.e. no ground radiance) in near I, assume it applies to red and blue bands. (Problem many scenes do not have such dark pixels or the fluctuations of the dark pixels are very large) Band Spatial esolution Spectral ange (nm) Spectral ange (microns) S/N Our Approach: Perform Spatial egression between VIS and SWI channel. Then find Aerosol reflectance in visible by taking the limit of I reflectivity going to zero. VIS NI 5m 9m nm 2nm Does not fly until 22. Need substitute sensors to study MODIS-Hyperion Using the continental aerosol model, derive optical depth in the red and blue bands using look up table (LUT) approach including multiple scattering. efine aerosol model (LUT) using single-scattering relationship between red and blue bands Adjust the optical depth according to the new aerosol model.

2 egression Approach Most surfaces have co-varying spectral responses between the VIS and MI If the spectral responses co-vary in a similar way (one component model), a strong linear correlation between the bands occurs Surf Atm ( <.) TOA Surf Atm vis A vis + vis vis vis TOA Surf MI MI no aerosol interaction Surf Surf TOA TOA Atm If MI = cvis vis kmi + vis TOA TOA Atm MI vis = vis Hyperion as model of GOES- Hyperspectral Imager 3 meter pixel resolution S/N between 6 and 5 (oh well) from blue to red. Hyperion hyperspectral Image 6nm observation Sept 2 2 Towards WTC egions of interest include vegetation (central park green), Urban areas (red-black) the river (orange), as well as near the WTC Correlation Coefficient vs wavelength ρ( ) VIS, MI Possible Shadow/Urban effect light urban vegatation middle urban near wtc Urban environments Have very similar Spectral correlations to vegetation Making correlation Approach very obust in urban Environments Note a sharp difference for lower manhattan due to shadowing /urban effect 2

3 43nm (Quite noisy) Hyperion Scene Aerosol eflection Validation Correlation between VIS and MI Only keep ρ Aerosol eflection (bad pixels Masked with navy blue) Aeronet CIMEL provides estimates of scattering phase function, single scattering albedo and optical depth This information allows reconstruction of aerosol reflectance signal (given solar zenith) The total atmosphere reflection is just the sum of the ayleigh and the aerosol signal in the single scattering limit. Since aerosol optical thicknesses are so small, single scattering is sufficient. T = a + m τm P m( Θs ) ωaτ a P a( Θs ) m = a = 4µ oµ 4µ oµ Aeronet Optical Depth Hyperion etrieval of Aerosol eflection over box region 4 From GISS 2 th St 2 Total eflection Single Scattering Hyperion Fly By 8 Hyperion etrieval Large Deviations Above 7nm Aerosol eflection based on Aeronet Data of AOT and Phase Function ayleigh Scattering ω a = [.] 3

4 Ground Dispersion Analysis Two Component Ground Model [ f ] ( ) Two component model Surf = fveg ( λ) + Soil λ General Linear egression TOA = a + btoa( f=fraction V TOA to Surface elation TOA = Atm + TAtm Surf TOA = Surf [ TOA( NI) TOA( NI)].[ TOA( TOA( ] a = TOA( NI) btoa( b = 2 [ TOA( TOA( ] Assuming 2 components Different Surface MI/VIS Statistics only in f T b Atm Veg Soil Veg + Veg =. TAtm( Veg Soil ( NI) Soil Soil a = Atm + TAtm Surf. Surf ( () Veg () Soil ( V ) a = Atm (VIS)+T Atm (VIS) δ Veg (SWI) Soil (SWI) Veg (SWI) Soil (SWI) Effects of Shadow on diverse ground models Numerical esults Variation metric given by percentage Veg Soil ( VIS) + ( VIS) ( SWI) ( SWI) Veg Soil δ α Shadowing effects all models the same through multiplicative factor ( Vis SWI) = f ( Vis SWI) eff veg, soil, shad veg, soil, Shadowing effects dynamic range making regressions more accurate No shadowing f = shadowing f = Improvement of etieval and correlation with shadowing α δ α VIS-MI spread between ground models shadow range Increased shadowing lowers retrieval percentage error δ α VIS-MI spread between ground models.5..5 Increased shadowing increases correlation ρ VIS SWI shadow range

5 egressions for different window resolutions to make a 3 km aerosol product 5 W = 3m W = 2m W = 6m W = 5m eflection errors using regression with different windows egression = ± Dark Pixel Thresholding over estimated by 2% W = 3m W = 6 m Does not include systemic bias From decreases dynamic range W = 75m W = m 5 5 W W 5 5 AVIIS Image (94nm) Sept 6 2 shadowing Pixel Footprint ~5 meters Aeronet Measurments Optically thin with large Angstrom Coefficient Little shadowing 5

6 VIS-SWI correlation for different regions. Aerosol Signals compared with total water signal Aerosol Signal over Land with land shadowing Aerosol Signal over Land with little land shadowing Total Signal over water Qualitatively Consistent Wavelength Dependence ρ VIS SWI good shadowing (no elimination of outliers) good shadowing (elimination of outliers) poor shadowing (no elimination of outliers) poor shadowing (elimination of outliers) Arb Units.5..5 Deviations occur due to water leaving radiance wavelength (nm) Water leaving radiance (subtracting ground decoupled signal from total water signal).2 Conclusions egression curves extrapolating reflection to the dark pixel limit seems to work very well for wavelengths < 7 Examination of Hyperion and AVIIS data shows the regression approach can be applied to complex reflecting surfaces and is improved when significant shadowing is present..5 Shadowing Better water leaving retrieval Errors above 7 nm may be explained by the variety of surface reflectance models. In particular, we show that the regressed intercept in a mixed surface model will not be due only to the atmosphere but is contaminated by the surface mixture but is improved using shadowing. Arb Units..5 Weak shadowing Poor water leaving retrieval High spatial resolution is important in reducing the regression error for a given aerosol product footprint and is much better then dark pixel thresholding Subtraction between total water leaving signal and atmosphere retrieved signal gives reasonable water leaving radiance reflectance spectrum giving indirect validation of aerosol product. -.5 GOES- Imager (with superior S/N) should be able to easily extract the aerosol reflectance over urban areas and continuous observation with different solar zenith angles is invaluable in examining the surface reflection models above 7nm This scheme however requires a significant spatial resolution improvement in the SWI for GOES- 6