Rrs(λ) IOPs

What to do with the retrieved IOPs?

Chlorophyll concentration: [Chl]

)(pha

)()()()( 21 dgphw aMaMaa

))(),(()( brs baFR

Examples: Carder et al (1999), GSM (2002), GIOP (2013)

:)(pha Varies with temperature in Carder et al (1999); Fixed globally in GSM (Maritorena et al 2002); Varies with [Chl] in GIOP (Werdell et al 2013)

“On a global scale, marine phytoplankton consume fifty thousand million tones of carbon every year in a process referred to as primary production.” -- IOCCG Report #2

“One of the principal applications of satellite ocean color data is to derive net primary production (NPP).” --- McClain (Annu. Rev. Mar. Sci., 2009)

food chain; carbon cycle

1. Estimate global primary production (PP)

Why chlorophyll concentration?

Elements of photosynthesis:

CO2 + H2O + phytoplankton + light + nutrient chemical energy

Components for PP quantification:

(Platt and Sathyendranath, 2007)

1. Phytoplankton 2. Light penetration

3. Photosynthesis parameters

[chl]

Traditional strategy: [chl] centered system

Works for waters where IOPs co-vary with [Chl].

Ocean color spectrum

Light field

color

PP

auxi.

Works for most waters.

Kd()

IOPs

a, bb, aph

IOP-based approach:

1.1 Estimation of diffuse attenuation coefficient (Kd) (for light field)

)(

)()490(

2

1

Rrs

RrsKd

Or,

ChlKd )(

2

3

b

1

a

21

)490(

aa)490(

XbK

or

XK

d

d

)551(

)488(

)551(

)488(

Rrs

RrsX

or

Lwn

LwnX

with

(Darecki and Stramisk, 2004)

Ratio-derived Kd

odbdoubuu EbrEbraEdz

d )(

odbdoubud EbraEbrEdz

d)(

Aas (1987) two-stream solution:

')','()','()cos(4

wdLLczd

Ld

oubuodbdodd EbrEbrbEcEdz

d )(

u

u

bud

d

bdd E

brE

braE

dz

d

b

u

u

d

d

d

d bRrra

K

oubuodbdd EbrEbraEdz

d )(

)( bdd baDK

Empirical approximation:

bd baK v)005.01( s

)52.01(18.4v 8.10 ae

Rrs a&bb dK

(Lee et al. 2005)

No division of “Case 1” or “Case 2” waters.

Kd(490) - measured [m-1

]

0.03 0.05 0.3 0.5 3 50.1 1

Kd(4

90

) [

m-1

]

0.03

0.05

0.3

0.5

3

5

0.1

1

1:1

Arabian Sea

GOM

Baltic

)490(dK

(IOP-model)

(Lee et al. 2005) 0.03 0.05 0.3 0.5 3 50.1 1

0.25

0.75

1.25

1.75

2.25

0.00

0.50

1.00

1.50

2.00Arabian Sea

GOM

Baltic

)4

90

(d

er

dK

)4

90

(m

ead

K/

)490(dK - measured [m-1]

(IOP-model)

0.75

1.25

Oceanic & Coastal waters

Other wavelengths:

X Data

350 400 450 500 550

Y D

ata

0.0

0.1

0.2

0.3

profile-Kd

IOPs-Kd

X Data

350 400 450 500 550

Y D

ata

0.00

0.02

0.04

0.06

0.08

profile-Kd

IOPs-Kd

Wavelength [nm]

Kd [

m-1

]

Wavelength [nm]

Kd [

m-1

]

(a) (b)

(Lee et al 2013)

zKPARePARzPAR

)0()(

1.2 Attenuation of PAR (KPAR)

Good for earlier days, Not so good for the 21st century

(Morel, 1988, JGR)

Change of light quality with depth:

deEzPAR zzK

700

400

),(

0 )0,()(

(Kirk 1994)

Light at deeper depth is associated with lower attenuation coefficient

)0(

)(ln

1

PAR

zPAR

zK PAR

KPAR is light-quality weighted!

Broadband attenuation is light dependent, so no-longer additive!

zzK parePARzPAR)(

)0()(

5.0

21

)1()(

z

KKzKPAR

Euphotic depth (zeu):

%1)0(

)(

PAR

zPAR

)(

6.4

euPAR

euzK

z

Key: KPAR varies with depth, especially in the upper water column!

[Chl]

Zeu (water clarity) rsR

PAR(0)

PAR(zeu)

1.3 Euphotic depth (Zeu)

%1)0(

)(

PAR

zPAR eu

(Morel 1988)

[Chl] approach

[Chl] (empirical) approach:

Rrs(λ) [Chl] zeu

IOP approach:

Rrs(λ) a(λ)&bb(λ) KPAR(z) zeu

5.0

21

)1(

))490(&)490(()490(&)490()(

z

baKbaKzK b

bPAR

Measured Zeu

[m]

0 20 40 60 80

Rrs

deri

ved Z

eu [

m]

0

20

40

60

80 1:1

1:1 R

rs-d

eri

ved

Zeu [m

]

Measured zeu [m]

IOP approach

Water clarity (zeu)

(Lee et al 2005)

water clarity (m)

Global distribution of Zeu

Zeu can be ‘measured’ with modern optical-electronic system

Ocean color Kd, aph PP

dtdzaztEzPP ph ),(),,()( 0

1.4 IOP based PP estimation:

(Kiefer and Mitchell, 1983, L&O)

ϕ

represents ‘photosynthesis’

φ: quantum yield of photosynthesis

measured production (mol/l/day)

0.3 31 10

calc

ula

ted

pro

duct

ion

(

mol

/l/d

ay)

0.3

3

1

10 aph-centered

Chl-centered

1:1

(Lee et al., Appl. Opt., 1996) (Lee et al., JGR, 2011)

Remotely-estimated PP compared with measured PP

Essence of present satellite Chl product:

b

brs

ba

bGR

)550()550(

)440()440(

)440(

)550(

)550(

)440(

bpbw

bpbw

rs

rs

bb

bb

a

a

R

R

Chlaaa

Chlaaa

a

a

R

R

phdgw

phdgw

rs

rs

)440()440()440(

)550()550()550(

)440(

)550(

)550(

)440(*

*

Change of Rrs band ratio not necessarily represents change of [Chl]!

)(

)(

2

1

rs

rs

R

RfunChl

Why aph based approach is likely better?

(Szeto et al 2011, JGR)

The change of Rrs ratio really reflects change of total absorption!

(Lee et al, 2010, JGR)

Rat

io-d

eriv

ed C

hl

(Carder et al, 1999, JGR)

To accurately retrieve spatially and temporally varying Chl from Rrs, we need:

1. Remove the influence of detritus/CDOM and particles 2. Take into account the spatial/temporal variation of a*ph

A promising effort:

Brief summary about current satellite Chl product: • The map of Rrs ratio represents a map of total absorption coefficient. • The GSM-derived Chl represents more of phytoplankton absorption coefficient.

pb:

(Platt et al 2008, RSE)

Pb is also dependent on a*ph,

which is not a constant either for a given Chl nor for varying Chl!

(Behrenfeld and Falkowski, 1997)

*

phm

b ap

Carbon based Production Model (CbPM)

)(/)( 0 geu IfIhZChlNPP

actually aph from GSM )(/ gIf

C

Chl

(Behrenfeld et al 2005; Westberry et al 2008)

Another example of using remotely sensed IOPs for PP

bbp

aph from GSM

‘IOP-based growth rate’

‘IOP-based NPP’

color

Works for most waters.

Kd()

KPAR

IOP

a, bb, aph

[chl]

ZSD, Zeu

[SPM]

{others}

2. Example of other applications of IOP products

PP

2.1 Secchi depth (ZSD)

Water clarity

(Olmanson et al 2008)

empirical approach:

(Doron et al 2007) )490(&)490( bba

IOP approach:

(Arnone et al 2004)

2.2 Water mass classification

(Carnizzaro et al 2006)

2.3 HAB identification-1

aph(657)

(Lee and Carder 2004)

2.3 HAB identification-2

Rrs(λ) aph(λ) = a(λ) – adg(λ) – aw(λ) QAA

(Craig et al 2006)

K. b

revi

s

(Shang et al, 2012)

2.4 Bloom dynamics

Distribution of aph(440) at Luzon Street

(Shang et al, 2012)

Monthly distribution of aph(440) at Luzon Street

2.5 Global physiology of ocean phytoplankton

φ: quantum yield of fluorescence (Behrenfeld et al 2009)

(Behrenfeld et al 2009)

IOP

(Vodacek et al 1997)

2.6 Salinity estimation

(Castellio et al 1999) SSS = x aCDOM + y

Lohrenz and Cai (2006) pCO2 = f(T, S, Chl)

2.7 pCO2 estimation

2.8 Concentrations of mineral particles (SPM; TSM)

Many publications in the literature

bbp [SPM]

(Neukermans et al 2009)

Key Points:

1. Many applications traditionally built around [Chl] can be built around IOPs.

2. Remote sensing and applications centered around IOPs avoided, when necessary, concentration-normalized optical properties.

3. With IOPs as inputs, many products, e.g. Kd, Zeu, PP, could be estimated easily and more accurately.

4. When IOPs are known, many other applications could

be carried out. Be creative!!!