Rrs(λ - IOCCGRrs Rrs K d v Or, K Chl d (O) v. 2 3 b 1 a 1 2 ( 490 ) ( 490 ) a a K b X or K X d d...
Transcript of Rrs(λ - IOCCGRrs Rrs K d v Or, K Chl d (O) v. 2 3 b 1 a 1 2 ( 490 ) ( 490 ) a a K b X or K X d d...
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
Estimate 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. 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
Rrs(λblue) = Rrs(443)>Rrs(486)
0.000 0.004 0.008 0.012 0.016
0.000
0.004
0.008
0.012
R2 = 0.99N = 43122
Rrs(443) [sr-1]
Rrs
(48
6)
[sr
-1]
GoM
MB
CB
The standard Kd(490) and Chl products are 100% co-vary in coastal waters; further…
)555(
)490()490(
rs
rsd R
RFunK
AOP: sun angle dependent
Nearly independent of sun angle
The two sides do not match in optical nature.
(Darecki and Stramisk, 2004)
In addition, ratio-derived Kd has large uncertainties
bd baK vm0
)1(v 321
amemm
Rrsa&bb dK
(Lee et al. 2005, 2013)
No division of “Case 1” or “Case 2” waters.
(m0,1,2 are wavelength independent)
Via radiative transfer equation:
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
)49
0(
der
dK
)49
0(
mea
dK/
)490(dK - measured [m-1]
(IOP-model)
0.75
1.25
Oceanic &Coastal waters
Kd spectrum:
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]K
d[m
-1]
(a) (b)
(Lee et al 2013)
zKPARePARzPAR )0()(
2. Attenuation of PAR (KPAR) and euphotic depth
Good for earlier days,Not so good for the 21st century
(Morel, 1988, JGR)
Change of light 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!
zzK parePARzPAR)(
)0()(
5.0
21
)1()(
z
KKzKPAR
Key: KPAR varies with depth, especially in the upper water column!
1 1( (490), (490)bK f a b 2 2 ( (490), (490)bK f a b
The [Chl] based models
MA05 Mu02
MA94 OS00
Kpar
Transmittance of PAR:
Transmission estimated from remote sensing:
The IOP based model
IOP05
Model R2 Mean │error│
MA94 0.92 41.2%
OS00 0.88 35.7%
MA05 0.90 49.2%
Mu02 0.87 152.8%
IOPs05 0.95 21.9%
(Zoffoli et al., 2017)
Zeu
%1)0(
)(
PAR
zPAR eu
Euphotic depth (zeu): %1)0(
)(
PAR
zPAR
)(
6.4
euPAR
euzK
z
(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
Zeu [
m]
0
20
40
60
80 1:1
1:1R
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
Ocean color Kd, aph PP
dtdzaztEzPP ph ),(),,()( 0
3. 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 p
roduct
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
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’
4. Secchi depth (ZSD)
Water clarity
(Olmanson et al 2008)
empirical approach:
013.0ln
5.2
1trwT
trd
SD
rr
KZ
trdK : attenuation coefficient in the transparent window
(Lee et al 2015)
New theoretical relationship for ZSD:
Verification of the new Secchi disk theory
(Lee et al 2015)
Rrs a&bb Kd
QAA (2002) Lee et al (2013)
Global ZSD
A much more straightforward product for water clarity!
(Arnone et al 2004)
5. Water mass classification
(Carnizzaro et al 2006)
6. HAB identification-1
aph(675)
(Lee and Carder 2004)
7. 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)
8. Bloom dynamics
Distribution of aph(440) at Luzon Street
(Shang et al, 2012)
Monthly distribution of aph(440) at Luzon Street
9. Global physiology of ocean phytoplankton
φ: quantum yield of fluorescence (Behrenfeld et al 2009)
(Behrenfeld et al 2009)
IOP
(Vodacek et al 1997)
10. Salinity estimation
(Castellio et al 1999)SSS = x aCDOM + y
Lohrenz and Cai (2006) pCO2 = f(T, S, Chl)
11. pCO2 estimation
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, Zsd, PP, could be estimated easily and more accurately.
4. When IOPs are known, many other applications could
be carried out. Be creative!!!
Thank you!