operational implementation strategies

1IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

Operational Implementation Operational Implementation StrategiesStrategies

Moderator: Bryan Franz

Topic 3


GoalsGoals

Identification of issues unique to satellite retrieval of IOPs

Understanding of satellite Rrs generation

Agreement on common IOP model inputs (aw & bbw)

Agreement on algorithm failure conditions & masking

Understanding impact of IOP inversion at L2 versus L3


Satellite FocusSatellite 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-Ca water

large data volumes, want best IOP algorithm that is “practical”

Imperfect Lw retrieval satellite sensor calibration & noise atmospheric correction error

Rrs normalization

wide range of viewing geometry (0 < v < 60)

transition through interface


Multi-Sensor Processing FrameworkMulti-Sensor Processing Framework

Level-1 to Level-2(common algorithms)

SeaWiFS L1A- or -

MODISA L1BMODIST L1B

OCTS L1AMOS L1BOSMI L1ACZCS L1AMERIS L1BOCM L1B

Level-2 to Level-3

Level-2 File

Level-3 GlobalProduct

observed radiances

AOPsRrs()

IOPsa(), bb()

Level-2 FileLevel-2 File

atmospheric correction

Lw normalization

derived products

flags (failure & quality)

observedLt()

, 0,

spatial averaging

temporal averaging

masking


RRrsrs from Satellite Radiances from Satellite Radiances

td() Lw() = Lt() / tg() / fp() - TLg() - tLf() - Lr() - La()

TOA gas pol glint whitecap air aerosol

nLw() = Lw() / td0() 0 f0

Sun full-band water-leaving radiance normalized to non-attenuating atmosphere with Sun overhead

fb

nLw() = nLw() ffb correct from full-band to nominal

10-nm center-band via Morel model

nLw() = nLw() ex correct for Fresnel reflection

refraction and inhomogeneity of subsurface light field via LUT

0 (f/Q)0

(f/Q)

Rrs() = nLw() / F0 () ex ex solar irradiance from Thuillier 2002

10-nm square-band-pass average


IOP Model Implementation IssuesIOP Model Implementation Issues

transition across air/sea interface Lee et al. 2002

pure sea-water values (aw & bbw)

aw: Pope & Fry, Kou et al. 1993, bbw: Smith & Baker 1981

10-nm square band-pass average (consistent with Rrs retrieval)

salinity & temperature sensitivity significant impact on IOP retrieval when aw & bbw = f(T,S)

need to identify ancillary data sources

€

Rrs(0−) =

Rrs(0+)

0.52 +1.7Rrs(0+)


Inversion Methods and EfficiencyInversion 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)

None 38

QAA 39

GSM SVD 42

GSM LM 55

GSM AMB 98

Algorithm Time (secs)

one SeaWIFS GAC orbit


IOP Models Implemented at NASAIOP Models Implemented at NASA

GSM (Garver-Siegel-Maritorena) a, aph, adg, bb, bbp, Ca

QAA (Quasi-Analytical Algorithm) a, aph, adg, bb, bbp

LAS (Loisel and Stramski) a, b, c, bb, bbp

PML (Plymouth Marine Labs) a, aph, adg, bb, bbp

HAL (Hoge & Lyon, via GIOP) a, aph, adg, bb, bbp

GIOP (Generalized IOP Model) a, aph, adg, bb, bbp, Ca, flags, , S

TBD: NIWA Boss & Roesler


Generalized IOP Model (GIOP)Generalized IOP Model (GIOP)

specify sensor wavelengths to fit e.g., 412,443,490,510,555 e.g., 412,490,555

select aph form and set params

tabulated: , ap*()

gaussian: ,

select adg form and set params

exponential: , S

select bbp 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 (), aph (), adg (), bb (), bbp ()

= any sensor wavelength(s)

Ca (given ap* at ) (dynamic model params) internal flags




tabulated: , ap*()

gaussian: ,


exponential: , S





select Rrs[0-] to bb/(a+bb) quadtratic: g1, g2 f/Q: (tbd)





5-Band GSM




tabulated: , ap*()

gaussian: ,


exponential: , S





select Rrs[0-] to bb/(a+bb) quadratic: g1, g2 f/Q: (tbd)





Hoge & Lyon


Flags & MasksFlags & Masks


Multi-Sensor Processing FrameworkMulti-Sensor Processing Framework


SeaWiFS L1A- or -



Level-2 to Level-3

Level-2 File


observed radiances

AOPsRrs()

IOPsa(), bb()



Lw normalization

derived products


observedLt()

, 0,

spatial averaging

temporal averaging

masking


Level-2 Flags & Level-3 MaskingLevel-2 Flags & Level-3 Masking

BIT NAME DESCRIPTION

01 ATMFAIL Atmospheric correction failure

02 LAND Pixel is over land

03 BADANC Reduced quality of ancillary data

04 HIGLINT High sun glint

05 HILT Observed radiance very high or saturated

06 HISATZEN High sensor view zenith angle

07 COASTZ Pixel is in shallow water

08 NEGLW Negative water-leaving radiance retrieved

09 STRAYLIGHT Straylight contamination is likely

10 CLDICE Probable cloud or ice contamination

11 COCCOLITH Coccolithophores detected

12 TURBIDW Turbid water detected

13 HISOLZEN High solar zenith

14 HITAU High aerosol optical thickness

15 LOWLWVery low water-leaving radiance (cloud shadow)

16 CHLFAIL Derived product algorithm failure

BIT NAME DESCRIPTION

17 NAVWARN Navigation quality is reduced

18 ABSAER possible absorbing aerosol

19 TRICHO Possible trichodesmium contamination

20 MAXAERITER Aerosol iterations exceeded max

21 MODGLINT Moderate sun glint contamination

22 CHLWARN Derived product quality is reduced

23 ATMWARN Atmospheric correction is suspect

24 DARKPIXEL Rayleigh-subtracted radiance is negative

25 SEAICE Possible sea ice contamination

26 NAVFAIL Bad navigation

27 FILTER Pixel rejected by user-defined filter

28 SSTWARN SST quality is reduced

29 SSTFAIL SST quality is bad

30 HIPOL High degree of polarization

31 PRODFAIL Derived product failure

32 OCEAN Not cloud or land

Level-2 flags used as masks in Level-3 processing


Proposed Conditions for IOP Product FailureProposed Conditions for IOP Product Failure

Rrs < 0 in any required band?

not required for Rrs() 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 aw() < a() < 5.0

-0.05 aw() < aph() < 5.0

-0.05 aw() < adg() < 5.0

0.95 bbw() < bb() < 0.015

-0.05 bbw() < bbp() < 0.015

initial proposal employed in some of our global analyses for this workshop

or Rrs < -()


L2 vs L3 InversionL2 vs L3 Inversion


IOP Inversion at Level-2IOP Inversion at Level-2


SeaWiFS L1A- or -



Level-2 to Level-3

Level-2 File


observed radiances

AOPsRrs()

IOPsa(), bb()



Lw normalization

derived products


observedLt()

, 0,

Standard NASA Approach


IOP Inversion at Level-3IOP Inversion at Level-3


SeaWiFS L1A- or -



Level-2 to Level-3

Level-2 File


observedLt()

, 0,

AOPsRrs()



Lw normalization

derived products



IOPsa(), bb()

averagedRrs()

, 0,

Alternative Approach


L2 vs L3 Inversion: GSM ModelL2 vs L3 Inversion: GSM Model


L2 vs L3 Inversion: GSM ModelL2 vs L3 Inversion: GSM Model

bb() < 0.015 maskGSM: Largest differences in eutrophic bb.


L2 vs L3 Inversion: QAA ModelL2 vs L3 Inversion: QAA Model

QAA: Largest differences in eutrophic a (band ratio algorithm, mean of ratio not same as ratio of means).


L2 vs L3 Inversion: PML ModelL2 vs L3 Inversion: PML Model

PML: differences everywhere (f/Q from mean geometry)

Oligotrophic Mesotrophic Eutrophica(443)

bb(443)


Model-to-Model DifferencesModel-to-Model Differences

Oligotrophic Mesotrophic Eutrophica(443)

bb(443)


My ViewMy 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 Rrs minimization to matrix inversion

can handle negative Rrs (small Rrs +/- 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


DiscussDiscuss ... ...


GoalsGoals

Identification of issues unique to satellite retrieval of IOPs

Understanding of satellite Rrs generation

Agreement on common IOP model inputs (aw & bbw)

Agreement on algorithm failure conditions & masking

Understanding impact of IOP inversion at L2 versus L3


Inversion MethodInversion Method


Inversion MethodsInversion 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)€

[Rrsm (λ ) − Rrs(λ )]

2

σ λ2

λ

∑

A x = b

€

a(λ 0) = f (Rrs(λ1),Rrs(λ 2),...)

bb (λ 0) = f (Rrs(λ 0),a(λ 0))


RRrsrs Minimization vs Matrix Inversion 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 = 412,443,490,510,5556-Band = 5-Band + 670

0.01 1.0a(443)


Matrix Inversion: Linearization IssueMatrix Inversion: Linearization Issue

Rrs[0-] = g1 u + g2 u2

where u bb/(a+bb)

where v = 1 - 1/u

aph() + adg ( + v bbp() = -[aw() + v bbw()]

u aph() + u adg ( + (u-1) bbp() = -[u aw() + (u-1) bbw()]

a = -v bb

u a = (1 - u) bb

1) Traditional Approach: System of Equations Proportional to 1/Rrs

2) Alternate Approach: System of Equations Proportional to Rrs

Rrs[0-] = f/Q u

- or -


Alternate Linearization Improves Inversion ConsistencyAlternate Linearization Improves Inversion Consistency

Linearization Method 2

a(443) GSM 6-Band

a(443) GSM 6-Band

Linearization Method 1

LM LM

SVDSVD

a(443) Global bb(443) Global

a(443) Global bb(443) Global

LM

SVD

LM

SVD


Matrix Inversion Still Missing Highs in a & bMatrix Inversion Still Missing Highs in a & bbb

SVD AmoebaEutrophic Waters, 5-Band GSMLM vs SVD

NOMAD aph(443)

a(443)

bb(443)

6-Band

3-Band

5-Band

4-Band


UncertaintiesUncertainties


Standard Deviation of RStandard Deviation of Rrsrs Distribution DistributionSeaWiFS March 2005SeaWiFS March 2005

412 443

510490

555 670

0.0 0.005reflectance units


MiscMisc


Trophic SubsetsTrophic Subsets

Deep-Water (Depth > 1000m) Oligotrophic (Chlorophyll < 0.1)

Mesotrophic (0.1 < Chlorophyll < 1) Eutrophic (1 < Chlorophyll < 10)


SalinitySalinity


ap*ap*

I used the Bricaud function to compute aph* for 25 chl concentrations between0.05 and 3 (evenly distributed in log space), then computed the averagespectra and spit out the 10nm wide aph* values for SeaWiFS wavelengths:

In the attached plot, the average aph* spectra is the white line, the10nm (wvl-5 <= wvl < wvl+5) version is in blue, and GSM is in red.


Model Differences: Global ViewModel Differences: Global View


QAA vs 6-Band GSM: a(443) & a(555)QAA vs 6-Band GSM: a(443) & a(555)

Oligotrophic Mesotrophic Eutrophic

443

555

GSM

QAA

1.05 aw


QAA - GSM: a(443) & a(555)QAA - GSM: a(443) & a(555)


443

555


QAA vs 6-Band GSM: bb(443) & bb(555)QAA vs 6-Band GSM: bb(443) & bb(555)


443

555

GSM

QAA

GSM

QAA


QAA - GSM: bb(443) & bb(555)QAA - GSM: bb(443) & bb(555)


443

555

operational implementation strategies

Documents

inherent optical properties

iop retrieval

bbp form

iop models

adg form

aph form

generalized iop model

common iop model inputs