1. SeaDAS Training ~ NASA Ocean Biology Processing Group 1 Level-2 ocean color data processing basics NASA Ocean Biology Processing Group Goddard Space Flight Center, Greenbelt, Maryland, USA SeaDAS Training Material

2. SeaDAS Training ~ NASA Ocean Biology Processing Group 2 Light paths to the sensor the satellite observes both the ocean and the atmosphere

3. SeaDAS Training ~ NASA Ocean Biology Processing Group 3 Ocean color the atmosphere is 80-90% of the total top-of-atmosphere signal in blue- green wavelengths (400-600 nm) ~1% error in instrument calibration or atmospheric model leads to ~10% error in Lw( )

4. SeaDAS Training ~ NASA Ocean Biology Processing Group 4 Effects of the atmosphere gaseous absorption (ozone, water vapor, oxygen) Rayleigh scattering by air molecules Mie scattering and absorption by aerosols (haze, dust, pollution) polarization (MODIS response varies with polarization of signal) Rayleigh (80-85% of total signal) small molecules compared to nm wavelength, scattering efficiency decreases with wavelength as -4 reason for blue skies and red sunsets can be accurately approximated for a given atmospheric pressure and geometry (using a radiative transfer code) Aerosols (0-10% of total signal) particles comparable in size to the wavelength of light, scattering is a complex function of particle size whitens or yellows the sky significantly varies and cannot be easily approximated

5. SeaDAS Training ~ NASA Ocean Biology Processing Group 5 Surface effects Sun glint whitecaps corrections based on statistical models (wind & geometry)

6. SeaDAS Training ~ NASA Ocean Biology Processing Group 6 Atmospheric correction t d ( ) L w ( ) = L t ( ) / t g ( ) / f p ( ) - TL g ( ) - tL f ( ) - L r ( ) - L a ( ) TOAgaspolglintwhitecapairaerosol nL w ( ) = L w ( ) f b ( ) / t d0 ( )  0 f 0 brdf Sun L w ( =NIR) ≈ 0 and can be estimated (model extrapolation from VIS) in waters where Chl is the primary driver of L w ( ) But, we need aerosol to get L w ( )

7. SeaDAS Training ~ NASA Ocean Biology Processing Group 7 Magnitudes of Lw(NIR  Lw(NIR) = 0 (clear water) Lw(NIR) ≠ 0 (turbid or highly productive water)

8. SeaDAS Training ~ NASA Ocean Biology Processing Group 8 Aerosol determiniation in visible wavelengths  a (  ) &  a (  )  L F 0 ·  0  =  a (NIR)   as (NIR)  (  ) =  as (  )  as (  ) model  ( ,  )  (,  ) =  as ( )  as (  ) Given retrieved aerosol reflectance at two  and a set of aerosol models fn( ,  0,  ).

9. SeaDAS Training ~ NASA Ocean Biology Processing Group 9 Iterative correction for non-zero Lw(NIR) (1) assume Lw(NIR) = 0 (2) compute La(NIR) (3) compute La(VIS) from La(NIR) (4) compute Lw(VIS) (5) estimate Lw(NIR) from Lw(VIS) + model (6) repeat until Lw(NIR) stops changing iterating up to 10 times

10. SeaDAS Training ~ NASA Ocean Biology Processing Group 10 Level-2 ocean color processing (1) determine atmospheric and surface contributions to total radiance at TOA and subtract, iterating as needed. (2) normalize to the condition of Sun directly overhead at 1 AU and a non-attenuating atmosphere (nLw or Rrs = nLw/F 0 ). (3) apply empirical or semi-analytical algorithms to relate the spectral distribution of nLw or Rrs to geophysical quantities. (4) assess quality (set flags) at each step

11. SeaDAS Training ~ NASA Ocean Biology Processing Group 11 Level-2 flags and masking ChlRGB image glint sediments cloud

12. SeaDAS Training ~ NASA Ocean Biology Processing Group 12 Level-2 flags and masking nLw(443)RGB image glint sediments cloud Add masking for high glintAdd masking for straylight

13. SeaDAS Training ~ NASA Ocean Biology Processing Group 13 Level-2 ocean color flags BITNAMEDESCRIPTION 01ATMFAILAtmospheric correction failure 02LANDPixel is over land 03BADANCReduced quality of ancillary data 04HIGLINTHigh sun glint 05HILTObserved radiance very high or saturated 06HISATZENHigh sensor view zenith angle 07COASTZPixel is in shallow water 08NEGLW Negative water-leaving radiance retrieved 09STRAYLIGHTStraylight contamination is likely 10CLDICEProbable cloud or ice contamination 11COCCOLITHCoccolithophores detected 12TURBIDWTurbid water detected 13HISOLZENHigh solar zenith 14HITAUHigh aerosol optical thickness 15LOWLW Very low water-leaving radiance (cloud shadow) 16CHLFAILDerived product algorithm failure BITNAMEDESCRIPTION 17NAVWARNNavigation quality is reduced 18ABSAERpossible absorbing aerosol 19TRICHOPossible trichodesmium contamination 20MAXAERITERAerosol iterations exceeded max 21MODGLINTModerate sun glint contamination 22CHLWARNDerived product quality is reduced 23ATMWARNAtmospheric correction is suspect 24DARKPIXELRayleigh-subtracted radiance is negative 25SEAICEPossible sea ice contamination 26NAVFAILBad navigation 27FILTERPixel rejected by user-defined filter 28SSTWARNSST quality is reduced 29SSTFAILSST quality is bad 30HIPOLHigh degree of polarization 31spare 32OCEANnot cloud or land Level-2 flags used as masks in Level-3 processing

14. SeaDAS Training ~ NASA Ocean Biology Processing Group 14 References H.R. Gordon and M. Wang, Appl. Opt. 33, 443-452 (1994) F. S. Patt et al., NASA Tech. Memo. 206892 22, 74 pp (2003) Ch. 4 & 9: NIR correction Ch. 5: atmospheric correction, spectral responses, BRDF Ch. 6: masks and flags http://oceancolor.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?22