1 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Operational Implementation Strategies Moderator: Bryan Franz Topic 3

2 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Goals  Identification of issues unique to satellite retrieval of IOPs  Understanding of satellite R rs generation  Agreement on common IOP model inputs (a w & b bw )  Agreement on algorithm failure conditions & masking  Understanding impact of IOP inversion at L2 versus L3

3 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Satellite Focus  Multiple sensors - varying wavelength sets  SeaWIFS, MODIS, MERIS --> OCM-2, VIIRS, OLCI  Multiple data processing systems (NASA, ESA, ISRO)  Global application  wide range of water classes, distribution dominated by low-C a water  large data volumes, want best IOP algorithm that is “practical”  Imperfect L w retrieval  satellite sensor calibration & noise  atmospheric correction error  R rs normalization  wide range of viewing geometry (0 <  v < 60)  transition through interface

4 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Multi-Sensor Processing Framework Level-1 to Level-2 (common algorithms) SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B Level-2 to Level-3 Level-2 File Level-3 Global Product observed radiances AOPs R rs ( ) IOPs a( ), b b ( ) Level-2 File atmospheric correction L w normalization derived products flags (failure & quality) observed L t ( ) ,  0,  spatial averaging temporal averaging masking

5 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG R rs from Satellite Radiances t d ( ) L w ( ) = L t ( ) / t g ( ) / f p ( ) - TL g ( ) - tL f ( ) - L r ( ) - L a ( ) TOAgaspolglintwhitecapairaerosol nL w ( ) = L w ( ) / t d0 ( )  0 f 0 Sun full-band water-leaving radiance normalized to non-attenuating atmosphere with Sun overhead fb nL w ( ) = nL w ( ) f fb correct from full-band to nominal 10-nm center-band via Morel model nL w ( ) = nL w ( ) ex correct for Fresnel reflection refraction and inhomogeneity of subsurface light field via LUT  0 (f/Q) 0  (f/Q) R rs ( ) = nL w ( ) / F 0 ( ) ex solar irradiance from Thuillier nm square-band-pass average

6 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG IOP Model Implementation Issues  transition across air/sea interface  Lee et al  pure sea-water values (a w & b bw )  a w : Pope & Fry, Kou et al. 1993, b bw : Smith & Baker 1981  10-nm square band-pass average (consistent with R rs retrieval)  salinity & temperature sensitivity  significant impact on IOP retrieval when a w & b bw = f(T,S)  need to identify ancillary data sources

7 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Inversion Methods and Efficiency  sequential (class 1 & 3)  model-specific (wavelength-specific)  may be iterative  simultaneous (class 2)  Matrix inversion – Lower-Upper Decomposition (LUD) – Singular-Valued Decomposition (SVD)  Iterative cost-function minimization – Levenburg-Marquart (LM) – Downhill Simplex (Amoeba, AMB) None38 QAA39 GSM SVD42 GSM LM55 GSM AMB98 AlgorithmTime (secs) one SeaWIFS GAC orbit

8 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG IOP Models Implemented at NASA  GSM (Garver-Siegel-Maritorena)  a, a ph, a dg, b b, b bp, C a  QAA (Quasi-Analytical Algorithm)  a, a ph, a dg, b b, b bp  LAS (Loisel and Stramski)  a, b, c, b b, b bp  PML (Plymouth Marine Labs)  a, a ph, a dg, b b, b bp  HAL (Hoge & Lyon, via GIOP)  a, a ph, a dg, b b, b bp  GIOP (Generalized IOP Model)  a, a ph, a dg, b b, b bp, C a, flags, , S TBD:  NIWA  Boss & Roesler

9 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Generalized IOP Model (GIOP)  specify sensor wavelengths to fit  e.g., 412,443,490,510,555  e.g., 412,490,555  select a ph form and set params  tabulated:, a p *( )  gaussian: ,   select a dg form and set params  exponential: , S  select b bp form and set params  power law: ,   power law: ,  via Hoge & Lyon  power law: ,  via QAA  select Rrs[0-] to bb/(a+bb)  quadratic: g1, g2  f/Q: (tbd)  specify inversion method  Levenburg-Marquart  Amoeba (downhill simplex)  Lower-Upper Decomposition  Singular-Value Decomposition  specify output products  a ( ), a ph ( ), a dg ( ), b b ( ), b bp ( )  = any sensor wavelength(s)  C a (given a p * at  )   (dynamic model params)  internal flags

10 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG  specify sensor wavelengths to fit  e.g., 412,443,490,510,555  e.g., 412,490,555  select a ph form and set params  tabulated:, a p *( )  gaussian: ,   select a dg form and set params  exponential: , S  select b bp form and set params  power law: ,   power law: ,  via Hoge & Lyon  power law: ,  via QAA Generalized IOP Model (GIOP)  select Rrs[0-] to bb/(a+bb)  quadtratic: g1, g2  f/Q: (tbd)  specify inversion method  Levenburg-Marquart  Amoeba (downhill simplex)  Lower-Upper Decomposition  Singular-Value Decomposition  specify output products  a ( ), a ph ( ), a dg ( ), b b ( ), b bp ( )  = any sensor wavelength(s)  C a (given a p * at  )   (dynamic model params)  internal flags 5-Band GSM

11 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG  specify sensor wavelengths to fit  e.g., 412,443,490,510,555  e.g., 412,490,555  select a ph form and set params  tabulated:, a p *( )  gaussian: ,   select a dg form and set params  exponential: , S  select b bp form and set params  power law: ,   power law: ,  via Hoge & Lyon  power law: ,  via QAA Generalized IOP Model (GIOP)  select Rrs[0-] to bb/(a+bb)  quadratic: g1, g2  f/Q: (tbd)  specify inversion method  Levenburg-Marquart  Amoeba (downhill simplex)  Lower-Upper Decomposition  Singular-Value Decomposition  specify output products  a ( ), a ph ( ), a dg ( ), b b ( ), b bp ( )  = any sensor wavelength(s)  C a (given a p * at  )   (dynamic model params)  internal flags Hoge & Lyon

12 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Flags & Masks

13 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Multi-Sensor Processing Framework Level-1 to Level-2 (common algorithms) SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B Level-2 to Level-3 Level-2 File Level-3 Global Product observed radiances AOPs R rs ( ) IOPs a( ), b b ( ) Level-2 File atmospheric correction L w normalization derived products flags (failure & quality) observed L t ( ) ,  0,  spatial averaging temporal averaging masking

14 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Level-2 Flags & Level-3 Masking 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 08NEGLWNegative 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 31PRODFAILDerived product failure 32OCEANNot cloud or land Level-2 flags used as masks in Level-3 processing

15 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Proposed Conditions for IOP Product Failure  R rs < 0 in any required band?  not required for R rs ( ) minimization  required for matrix inversion (no positive roots in Gordon quad.)  required for band ratio component algorithms (e.g., QAA, HAL)  Failure within model computation  e.g., inputs out of range of LUTs, divide by zero errors  Tests on IOP retrievals (for 400 < < 600) 0.95 a w ( ) < a( ) < a w ( ) < a ph ( ) < a w ( ) < a dg ( ) < b bw ( ) < b b ( ) < b bw ( ) < b bp ( ) <0.015 initial proposal employed in some of our global analyses for this workshop or R rs < -  ( )

16 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG L2 vs L3 Inversion

17 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG IOP Inversion at Level-2 Level-1 to Level-2 (common algorithms) SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B Level-2 to Level-3 Level-2 File Level-3 Global Product observed radiances AOPs R rs ( ) IOPs a( ), b b ( ) Level-2 File atmospheric correction L w normalization derived products flags (failure & quality) observed L t ( ) ,  0,  Standard NASA Approach

18 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG IOP Inversion at Level-3 Level-1 to Level-2 (common algorithms) SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B Level-2 to Level-3 Level-2 File Level-3 Global Product observed L t ( ) ,  0,  AOPs R rs ( ) Level-2 File atmospheric correction L w normalization derived products flags (failure & quality) Level-3 Global Product IOPs a( ), b b ( ) averaged R rs ( ) ,  0,  Alternative Approach

19 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG L2 vs L3 Inversion: GSM Model

20 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG L2 vs L3 Inversion: GSM Model b b ( ) < mask GSM: Largest differences in eutrophic b b.

21 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG L2 vs L3 Inversion: QAA Model QAA: Largest differences in eutrophic a (band ratio algorithm, mean of ratio not same as ratio of means).

22 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG L2 vs L3 Inversion: PML Model PML: differences everywhere (f/Q from mean geometry) Oligotrophic Mesotrophic Eutrophic a(443) b b (443)

23 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Model-to-Model Differences Oligotrophic Mesotrophic Eutrophic a(443) b b (443)

24 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG My View  I like simultaneous solutions (class-1)  take advantage of full spectral suite, readily adapted to multiple sensors, easy to incorporate new ideas or alternative basis functions.  I prefer R rs minimization to matrix inversion  can handle negative R rs (small R rs +/- noise)  seems less sensitive to noise (perhaps a weighting issue)  Efficiency in algorithm/inversion selection is not a primary concern  satellite data processing is i/o intensive (exception Boss & Roesler)  Inversion at Level-3 vs Level-2 is not a primary concern  differences between popular models are much greater  Mask all IOP products at Level-3 if:  any one product exceeds valid (TBD) range

25 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Discuss...

26 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Goals  Identification of issues unique to satellite retrieval of IOPs  Understanding of satellite R rs generation  Agreement on common IOP model inputs (a w & b bw )  Agreement on algorithm failure conditions & masking  Understanding impact of IOP inversion at L2 versus L3

27 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

28 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Inversion Method

29 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Inversion Methods  sequential  model-specific  may be iterative  simultaneous  Iterative cost-function minimization – Levenburg-Marquart (LM) – Downhill Simplex (Amoeba, AMB)  Matrix inversion – Lower-Upper Decomposition (LUD) – Singular-Valued Decomposition (SVD) A x = b

30 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG R rs Minimization vs Matrix Inversion a(443), 6-Band GSM Model, LM Fit a(443), 5-Band GSM Model, LM Fit a(443), 6-Band GSM Model, SVD Fit a(443), 5-Band GSM Model, SVD Fit 5-Band = 412,443,490,510,555 6-Band = 5-Band a(443)

31 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Matrix Inversion: Linearization Issue R rs [0-] = g1 u + g2 u 2 where u  b b /(a+b b ) where v = 1 - 1/u a ph ( ) + a dg (  + v b bp ( ) = -[a w ( ) + v b bw ( )] u a ph ( ) + u a dg (  + (u-1) b bp ( ) = -[u a w ( ) + (u-1) b bw ( )] a = -v b b u a = (1 - u) b b 1) Traditional Approach: System of Equations Proportional to 1/Rrs 2) Alternate Approach: System of Equations Proportional to Rrs R rs [0-] =  f/Q u - or -

32 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Alternate Linearization Improves Inversion Consistency Linearization Method 2 a(443) GSM 6-Band Linearization Method 1 LM SVD a(443) Global b b (443) Global a(443) Global b b (443) Global LM SVD LM SVD

33 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Matrix Inversion Still Missing Highs in a & b b SVD Amoeba Eutrophic Waters, 5-Band GSM LM vs SVD NOMAD a ph (443) a(443) b b (443) 6-Band 3-Band 5-Band 4-Band

34 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Uncertainties

35 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Standard Deviation of R rs Distribution SeaWiFS March reflectance units

36 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Misc

37 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Trophic Subsets Deep-Water (Depth > 1000m)Oligotrophic (Chlorophyll < 0.1) Mesotrophic (0.1 < Chlorophyll < 1) Eutrophic (1 < Chlorophyll < 10)

38 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Salinity

39 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG ap* I used the Bricaud function to compute aph* for 25 chl concentrations between 0.05 and 3 (evenly distributed in log space), then computed the average spectra and spit out the 10nm wide aph* values for SeaWiFS wavelengths: In the attached plot, the average aph* spectra is the white line, the 10nm (wvl-5 <= wvl < wvl+5) version is in blue, and GSM is in red.

40 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG Model Differences: Global View

41 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG QAA vs 6-Band GSM: a(443) & a(555) Oligotrophic Mesotrophic Eutrophic GSM QAA 1.05 a w

42 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG QAA - GSM: a(443) & a(555) Oligotrophic Mesotrophic Eutrophic

43 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG QAA vs 6-Band GSM: bb(443) & bb(555) Oligotrophic Mesotrophic Eutrophic GSM QAA GSM QAA

44 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG QAA - GSM: bb(443) & bb(555) Oligotrophic Mesotrophic Eutrophic