ESTIMATION OF INHERENT OPTICAL ESTIMATION OF INHERENT OPTICAL PROPERTIES AND WATER CONSTITUENT PROPERTIES AND WATER CONSTITUENT CONCENTRATIONS FROM THE REMOTECONCENTRATIONS FROM THE REMOTE--

SENSING REFLECTANCE SPECTRA IN THE SENSING REFLECTANCE SPECTRA IN THE ALBEMARLEALBEMARLE--PAMLICO ESTUARY, USAPAMLICO ESTUARY, USA

Leonid Sokoletsky and Ross Lunetta

U.S. Environmental Protection Agency National Exposure Research Laboratory

Landscape Characterization BranchResearch Triangle Park, North Carolina, USA

2nd MERIS – (A)ATSR WorkshopFrascati (Rome), ItalySeptember 22-26, 2008

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•• Leonid Leonid SokoletskySokoletsky,, Ross Ross LunettaLunetta, , Joseph Knight, Yang Joseph Knight, Yang ShaoShao (U.S. (U.S. Environmental Protection Agency)Environmental Protection Agency)

•• Alexander Alexander KokhanovskyKokhanovsky (Institute of Environmental Physics, (Institute of Environmental Physics, IniversityIniversity of Bremen, Germany)of Bremen, Germany)

•• JayanthaJayantha EdiriwickremaEdiriwickrema (SRA International) (SRA International) •• Darryl Keith (Atlantic Ecology Division, U.S. Environmental Darryl Keith (Atlantic Ecology Division, U.S. Environmental

Protection Agency)Protection Agency)•• Hans Hans PaerlPaerl, Michael , Michael WetzWetz,, Benjamin Benjamin PeierlsPeierls (Institute of Marine (Institute of Marine

Sciences, University of North Carolina at Chapel Hill) Sciences, University of North Carolina at Chapel Hill) •• Anatoly Anatoly GitelsonGitelson (Center for Advanced Land Management (Center for Advanced Land Management

Information Technologies, University of NebraskaInformation Technologies, University of Nebraska--Lincoln)Lincoln)

Project Participantsand Consultants

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Research ObjectivesResearch Objectives

•• Develop simple underwater algorithm forDevelop simple underwater algorithm for estimation of water estimation of water quality components (CDOM, quality components (CDOM, ChlChl aa, VSS, FSS, and TSS) from , VSS, FSS, and TSS) from measured underwater remotemeasured underwater remote--sensing reflectance spectra based on sensing reflectance spectra based on radiativeradiative transfer theorytransfer theory

•• Develop simple atmospheric correction algorithm based on Develop simple atmospheric correction algorithm based on radiativeradiativetransfer theorytransfer theory

•• Parameterize and integrate both algorithms for water quality Parameterize and integrate both algorithms for water quality satellite monitoring abovesatellite monitoring above thethe AlbemarleAlbemarle--PamlicoPamlico Estuary, USAEstuary, USA

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Fig. 1. The Neuse River-Pamlico Sound Estuarine System with ModMon stations and FerryMon routes.

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ModMonModMon Project:Project:•• RadiometricRadiometric quantities [spectral quantities [spectral downwellingdownwelling

irradiance just above the surface, irradiance just above the surface, EEdd((λλ, 0+); , 0+); underwater spectral upwelling radiance, underwater spectral upwelling radiance, LLuu((λλ, z)] , z)] achieved from the ship measurements (achieved from the ship measurements (SatlanticSatlanticHyperOCRHyperOCR HyperspectralHyperspectral Radiometer, Fig. 2)Radiometer, Fig. 2)

•• InIn--water quality parameters: water quality parameters: ChlChl aa, CDOM, , CDOM, volatile (organic) and fixed (inorganic) particles volatile (organic) and fixed (inorganic) particles estimated from the laboratory analysisestimated from the laboratory analysis

•• Auxiliary parameters: T, S, Auxiliary parameters: T, S, SecchiSecchi depth, PAR depth, PAR attenuation coefficient, turbidity etc.attenuation coefficient, turbidity etc.

Fig. 2. Satlantic HyperspectralOcean Colour Radiometer(HyperOCR).

In situ measurementsIn situ measurements

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In situ measurements (cont.)In situ measurements (cont.)FerryMon Project:ChlChl aa and auxiliary parameters (T, S, pH, dissolved oxygen, turbidityand auxiliary parameters (T, S, pH, dissolved oxygen, turbidity) )

measuredmeasured from two ferries (one in Neuse River, one in Pamlico Sound), Fifrom two ferries (one in Neuse River, one in Pamlico Sound), Fig. 3g. 3

Fig. 3. Ferry work. Taken from:http://www.unc.edu/ims/paerllab/research/ferrymon/how.htm

6 Fig. 4. Fig. 4. In situ In situ remoteremote--sensing reflectance spectra in Neuse River.sensing reflectance spectra in Neuse River.

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Remote sensing measurementsRemote sensing measurements

Fig. 5. Imaging Spectrometer MERIS on board ENVISAT. Taken fromhttp://www.mumm.ac.be/Assets/OceanColour/Pages/MERIS_sensor.gif

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Table 1. The band center wavelengthsTable 1. The band center wavelengths λλ (in nm) and calibration coefficients (in nm) and calibration coefficients kk((λλ) = ) = LLTOATOA((λλ)/DN()/DN(λλ)) (in (in μμWW cmcm--22 srsr--11 DNDN--11) for the MERIS instrument ) for the MERIS instrument (after Barnes and (after Barnes and ZalewskiZalewski, 2003; , 2003; GovaertsGovaerts and and ClericiClerici, 2004), 2004)

Channel λ k Channel λ k

1 412.5 0.01683 8 681.25 0.00404 2 442.5 0.01436 9 709 0.00359 3 490 0.01111 10 753.75 0.00305 4 510 0.00995 11 760 0.00299 5 560 0.00753 12 779 0.00282 6 620 0.00545 13 870 0.00223 7 665 0.00434

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θ0

Ed(λ, 0+)

LTOA(λ)

Lu(λ, 0-)

Fig. 6. A schematic geometry for inFig. 6. A schematic geometry for in--water and atmosphere processes and water and atmosphere processes and measurements.measurements.

θrefr

θv = 0º

10 Fig. 7. Relationships between the water quality parameters in NeFig. 7. Relationships between the water quality parameters in Neuse River.use River.

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Main assumptions of the studyMain assumptions of the study

aaTSSTSS, red, red = = aa11Chl = Chl = aa22VSS = VSS = aa33FSS = FSS = aa44TSS; TSS = VSS+FSSTSS; TSS = VSS+FSS

aavisvis = = aaw, w, visvis + + aaTSSTSS, , visvis + + aaCDOMCDOM, , visvis; ; aaNIRNIR = = aaw, NIRw, NIR + + aaCDOMCDOM, NIR, NIR; ; bbb, redb, red ≈≈ bbb, NIRb, NIR = = bbbb; ;

aaCDOMCDOM, blue, blue = = aa55((GGgreengreen//GGredred))aa66; ; aaCDOMCDOM((λλ) = ) = aaCDOMCDOM(412.5)(412.5/(412.5)(412.5/λλ))aa77,,

GG((λλ) ) ≡≡ bbbb((λλ)/[)/[aa((λλ)+)+bbbb((λλ)], )], λ λ = 412.5 nm (blue), 560 nm (green), 665 nm (red), = 412.5 nm (blue), 560 nm (green), 665 nm (red),

709 nm (NIR); 709 nm (NIR);

RRrsrs((λλ, , 00--) = ) = LLuu((λλ, 0, 0--)/)/EEdd((λλ, 0, 0--) = ) = (1/(1/ππ)[)[ddEESA + (1 SA + (1 -- ddEE)RFcos()RFcos(θθrefrrefr)] (current )] (current

study) study)

SA = SA = FF11(IOPs), RF = (IOPs), RF = FF22[IOPs, [IOPs, cos(cos(θθrefrrefr)] by )] by

KokhanovskyKokhanovsky and and SokoletskySokoletsky (2006);(2006);

ddEE ≡≡ EEd, difd, dif//EEd d = F(= F(θθ00, , λλ) by ) by HHøøjerslevjerslev (2001, 2004) (2001, 2004)

RRrsrs((λλ, , 00--) = ) = RRrsrs((λλ, , 0+)/[0.52+1.70+)/[0.52+1.7RRrsrs((λ, λ, 0+)] by Craig et al. (2006)0+)] by Craig et al. (2006)

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ChlChl = = aaTSSTSS, red, red//aa11;; VSS = VSS = aaTSSTSS, red, red//aa22; FSS = ; FSS = aaTSSTSS, red, red//aa3 3 ; TSS = ; TSS = aaTSSTSS, red, red//aa44; ;

aa33 = = aa22aa44/(/(aa2 2 -- aa44), where), where

aaTSSTSS, red, red = (= (aaw, NIRw, NIR + + aaCDOMCDOM, NIR, NIR + + bbbb)()(GGNIRNIR//GGredred) ) -- ((aaw, redw, red + + aaCDOMCDOM, red, red + + bbbb)), where, where

aaCDOMCDOM, blue, blue = = aa55((GGgreengreen//GGredred))a6a6, , aaCDOMCDOM((λλ) = ) = aaCDOMCDOM(412.5)(412.5/(412.5)(412.5/λλ))a7a7,,

GG((λλ) ) = = 8.9458.945RRrsrs(0(0--) ) –– 37.98[37.98[RRrsrs(0(0--)])]2 2 + 140.2[+ 140.2[RRrsrs(0(0--)])]3 3 –– 286.7[286.7[RRrsrs(0(0--)])]44 ,,

bbb b = = aaNIRNIRGGNIRNIR/(1/(1--GGNIRNIR))

Water quality in situ algorithmWater quality in situ algorithm

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Atmospheric correction algorithm

,)(

)()()()(

)(,

0,

,

,

λμλλ

λλ

λupdif

normpathTOA

updif

TOAww T

LLTL

L−

== ,/)(2

)(exp)(,⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ +−= va

RupdifT μλτλτλ

According to Gordon and Voss (1999):

wherea ,745.0)( 0

08.0

0

τ

λτλτ ⎟

⎠⎞

⎜⎝⎛= ).745.0(0 ma μττ ≡

m.in ),00013.00113.01(008569.0)( 424 μλλλλλτ −−− ++=R

),()()( ,,, λλλ downdifTOAddifs TEE = [ ] .)()( 0/,,

μμλλ vupdifdowndif TT =

After Sekera (1970) and Yang and Gordon (1997) (reciprocity principle):

After Hansen and Travis (1974):

After Haltrin (1998):

After Højerslev (2001, 2004):

).(/)()( , λλλ Edifss dEE =

vv θμ cos≡

Fig. 8. Relationships between GordonFig. 8. Relationships between Gordon’’s parameter and Rs parameter and Rrsrs(0(0--).).

Fig. 9. Fig. 9. Absolute errors of TSS absorption at the red spectral range undeAbsolute errors of TSS absorption at the red spectral range under r

using two approximations: using two approximations: aaCDOMCDOM = 0 and QSSA= 0 and QSSA: inin--waterwater algorithmalgorithm.

Fig. 10. Fig. 10. Absolute errors of TSS absorption at the red spectral range undeAbsolute errors of TSS absorption at the red spectral range under r

using two approximations: using two approximations: aaCDOMCDOM = 0 and QSSA= 0 and QSSA: remoteremote--sensingsensing algorithmalgorithm.

17 Fig. 11. Predicted vs. Fig. 11. Predicted vs. in situ in situ aaCDOMCDOM(412.5) in Neuse River (2006(412.5) in Neuse River (2006--2008).2008).

18 Fig. 12. Predicted vs. Fig. 12. Predicted vs. in situin situ ChlChl aa in Neuse River (2006in Neuse River (2006--2008).2008).

19 Fig. 13. Predicted vs. Fig. 13. Predicted vs. in situin situ VSS in Neuse River. 2006VSS in Neuse River. 2006--2008.2008.

20 Fig. 14. Predicted vs. Fig. 14. Predicted vs. in situin situ FSS in Neuse River. 2006FSS in Neuse River. 2006--2008.2008.

21 Fig. 15. Predicted vs. Fig. 15. Predicted vs. in situin situ TSS in Neuse River. 2006TSS in Neuse River. 2006--2008.2008.

22 Fig. 16. MERIS vs. Fig. 16. MERIS vs. FerryMonFerryMon ChlChl aa in APES ( 2007in APES ( 2007--2008).2008).

Fig. 17. Absolute errors for MERIS vs. Fig. 17. Absolute errors for MERIS vs. FerryMonFerryMon ChlChl a a estimations.estimations.

24 Fig. 18. Relative errors for MERIS vs. Fig. 18. Relative errors for MERIS vs. FerryMonFerryMon ChlChl a a estimations.estimations.

25 Fig. 19. Observed annual dynamics of Fig. 19. Observed annual dynamics of ChlChl a a in APES (2007in APES (2007--2008 ).2008 ).

26 Fig. 20. Estimated annual dynamics of Fig. 20. Estimated annual dynamics of ChlChl a a in APES (2007in APES (2007--2008 ).2008 ).

<5 <10 <15 <20 <25 <30 <35 <40 <45

<50 <60 <70 <80 <90 <100 <200 >200 Background

Fig. 21. Examples of Fig. 21. Examples of ChlChl aa imagery: bloom (left) & nonimagery: bloom (left) & non--bloom (right) periods.bloom (right) periods.

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Summary and future work:Summary and future work:• CCollected ollected in situin situ optical and water quality components (WQC) data in the optical and water quality components (WQC) data in the Neuse River and Pamlico Sound region (Neuse River and Pamlico Sound region (ChlChl aa, CDOM, FSS, VSS, and TSS), CDOM, FSS, VSS, and TSS)

•• Developed Developed in situ in situ and remoteand remote--sensing WQC algorithms based on applying sensing WQC algorithms based on applying radiativeradiative transfer and observation datatransfer and observation data

•• Further improvement of methods developed by using more rigorousFurther improvement of methods developed by using more rigorous inin--water water and atmospheric algorithms and codesand atmospheric algorithms and codes

•• Develop and implement a cloud mask algorithm to increase the teDevelop and implement a cloud mask algorithm to increase the temporal mporal frequency of MERIS observation data.frequency of MERIS observation data.

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Thank you!!!