OTSK1A
Atmospheric Correction for Ocean Color
A. Algorithm Outline(1) Algorithm name: Atmospheric Correction for Ocean Color(2) Product Code: NWLR
(3) PI names: G-0065 Hajime Fukushima(4) Overview of the algorithm (Status: Operational level)
This algorithm is an extension of the OCTS atmospheric correction algorithm (Fukushima et al., 1998). It treatedmultiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone
concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with
iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. Due tosaturation problems of the GLI nominal band16 (749nm) and band18 (865nm), alternative bands (band13:678nm andband19:865nm) are used to estimate base aerosol type. In addition, 380nm and 865nm bands are used to estimate
aerosol absorption. In the iteration, the water reflectance at near infrared and 380nm bands are estimated by an
in-water model for chlorophyll a (chl) and inorganic suspended matter (ism) concentrations as well as absorption
coefficient of colored dissolved organic matter (cdom). These parameter values are estimated by using a neuralnetwork in-water algorithm for further iteration.

The final output values for chl, ism, and cdom are generated via anempirical algorithm
B. Theoretical Description(1) Methodology and Logic flow
1-1 Radiative transfer model
The satellite-observed reflectance, ρT, is modeled as follows.

ρ T (λ ) = ρ M (λ ) + ρ A (λ ) + ρ MA (λ ) + T (λ ) ρ G (λ ) + t (λ ) ρWC (λ ) + t (λ ) ρW (λ )(1)where λ is wavelength, ρM is reflectance due to gas molecules, ρA is aerosol reflectance, ρMA is reflectance due to theinteraction between molecules and aerosol particles, ρG is the reflectance resulting from the specular reflection of direct
sun light, ρWC is the reflectance resulting from the whitecap, ρW is reflectance of the ocean, T is the direct transmittanceof the atmosphere, and t is the diffuse transmittance of the atmosphere.
The purpose of atmospheric correction is to retrieve ρW in the equation above where ρA+ρMA and t are estimated by
the satellite data with a help of aerosol lookup tables while ρM is calculated by a Rayleigh lookup table.
1-2 Aerosol models
We obtain the aerosol reflectance for 678nm and 865nm bands including the interaction part as follows,ρ A (λ ) + ρ MA (λ ) = ρ T (λ ) − ρ M (λ ) − T (λ ) ρ G (λ ) − t (λ ) ρWC (λ ) − t (λ ) ρ W (λ )(2)We need to estimate ρA+ρMA for band 1 to 12 and 14 but it is not straight forward since the spectral relation of ρA+ρMA
over the whole visible and near IR region is dependent onθ0, θ, the satellite zenith angle, and ∆φ, the relative azimuth
angle between the sun and the satellite, in addition to the type and optical thickness of aerosol. Similar to the method
proposed by Gordon and Wang (1994), we introduce the tables that store the relation between the aerosol reflectanceρA+ρMA and aerosol optical thickness τA for each band and use them to determine the magnitude of ρA+ρMA in theGLI Algorithm Description

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shorter wavelengths bands based on the estimated spectral ratio of aerosol reflectance ε between two NIR bands.
Since the relation between ρA+ρMA and τA is dependent on aerosol type, GLI algorithm has the following nine candidate
aerosol models:- Tropospheric aerosol with R.H. of 70% (Model 1)- Oceanic1600 aerosol with R.H. of 70% (Model 2)- Oceanic800 aerosol with R.H. of 70% (Model 3)- Oceanic400 aerosol with R.H. of 70% (Model 4)- Oceanic200 aerosol with R.H. of 60 and 73% (Models 5 and 6)- Oceanic100 aerosol with R.H. of 70% (Model 7)- Oceanic50 aerosol with R.H. of 70 and 83% (Model 8 and 9)
The definition of these models are based on Shettle and Fenn (1979). The “Oceanic1600 type” consists of 99.9375%(one 1600th) of tropospheric and 0.0625% of oceanic aerosols, in terms of number of particles. RH means theassumed relative humidity. Other models through Model 9 are defined similarly.
1-3 Lookup tables
The following lookup tables were prepared beforehand to speed up processing of atmospheric correction.
- ρM table that gives ρM(λ) for the given θ0, θ, and ∆φ.
- “ρA+ρMA →τA” table that contains coefficients a0, a1, a2, a3 and a4 in the equation,

τ A (λ ) = a 0 + a1 ⋅ X (λ ) + a 2 ⋅ X (λ ) 2 + a3 ⋅ X (λ ) 3 + a 4 ⋅ X (λ ) 4
X (λ ) = ρ A (λ ) + ρ MA (λ )(3)for all (θ0, θ, ∆φ), aerosol types and atmospheric correction bands such as 678nm (ch13) and 865nm (ch19, denoted as865A band).
- “τA → ρA+ρMA” table that contains coefficients a0, a1, a2, a3 and a4 in the equation,

ρ A ( λ) + ρMA ( λ) = a0 + a1 ⋅ τ A (λ ) + a2 ⋅ τ A ( λ) 2 + a3 ⋅ τ A (λ) 3 + a4 ⋅ τ A ( λ) 4(4)for all (θ0, θ, ∆φ), aerosol types and channels except atmospheric correction bands.
The entries for these tables were all generated by rstar5b code developed by T. Nakajima and his group.
1-3 consideration with absorptive aerosol
The standard GLI atmospheric correction algorithm (ver.1.5) has good performance, but it sometimes doesn’t work wellin East Asian region, when ocean is covered with absorptive aerosol like a forest fire smoke or anthropogenic blackcarbon.

Hence, we added the following scheme for version 2 atmospheric correction.

We first define magnitude of aerosol absorption (aalb) asaalb( λ ) =[ρA (λ) + ρMA (λ)](5)ρ A ( λ ) + ρ MA ( λ )where ρA(λ)+ρMA(λ) is calculated from selected model based on the NIR bands.
We obtain the aalb at 380nm from [ρA(380)+ρMA(380)] and ρA(380)+ρMA(380). [ρA(380)+ρMA(380)] is calculated fromeq.(2) by estimating water reflectance. When the absorption is significantly large, the [ρA(380)+ρMA(380)] becomesmuch different from ρA(380)+ρMA(380). In other words, the absorptive aerosol is detected from the difference, and is

GLI Algorithm Description

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corrected by a spectral absorption model.
aalb for other bands is estimated by the following expression.

aalb(λ ) = w(λ ) ⋅ aalb(380) ,(6)where w(λ) is weight of absorption. It is given empirically fromscenes with an absorptive aerosol.

Initial values ofchl, ism, cdom

1-4 Atmospheric correction with iterationNIR ρw model

We developed an iterative procedure that corrects

NIR ρw correction

atmospheric effect to avoid the black pixel assumption wherenear-infrared (NIR) water-leaving radiances are assumed to
be zero or negligible (Siegel et al., 2000). As shown in Figure

Atmospheric correction

3-1, the water reflectance at NIR bands are first estimated byneural network
in-water algorithm

using in-water model assuming initial values for chlorophyll a(chl) and inorganic suspended matter (ism) concentrations asNew chl, ism, cdom, and ρw in VIS

well as absorption coefficient of colored dissolved organic
matter (cdom). First atmospheric correction is executed, andConverged?

new chl, ism and cdom are estimated by using neural network
in-water algorithm. After the first atmospheric correction, new

Outputwater-leaving reflectance is estimated from obtained chl, andthe second stage atmospheric correction is conducted. This
process is repeated until chl, ism and cdom estimatesconverge.
1-4-1.

Fig. 3-1
A simplified flow diagram of thepixel-wise GLI atmospheric correction with
iteration. In the diagram, NIR and VIS stand forthe near-infrared and visible bands, respectively.

In-water model at near infrared region

The in-water model for NIR water-leaving radiance is defined as follows(7)[ ρ w (λ )]N = 0.533πR(λ ) /Qby Lee et al. (1994) where λ is wavelength, and Q = 4.5 (Morel and Gentili, 1991). [ ρ w (λ )]N is normalized waterreflectance (Gordon, 1997) given by[ ρ w (λ )]N = ρ w (λ ) / t 0 ,(8)where t0 is the transmittance between sun and ocean surface.

R is the reflectance just below surface and is definedby Joseph (1950) asR(λ ) =1+ 2bb (λ ) / a(λ ) − 1(9)1+ 2bb (λ ) / a(λ ) + 1

where a and bb is absorption and backward scattering coefficient, respectively. a(λ) and bb(λ) are defined bya (λ ) = a w (λ ) + a c (λ ,chl ) + a s (λ ,ism ) + a y (λ ) ,(10)bb (λ ) = bbw (λ ) + bbc (λ ,chl) + 2.7 ⋅bbs (λ ,ism)(11)

andGLI Algorithm Description

3.2.1-3

where subscript w, c, s, y represents water, chlorophyll-a, inorganic suspended matter and yellow substance (cdom)
respectively.

Absorption coefficient values are given as follows.

a w (678) = 0.42829 , a w (865) = 4.6416(12)

by Pope and Fry (1997)

a c (λ ) = a cs (λ ) ⋅chl

(13)

where chl is chlorophyll a concentration in mg/m3

a cs (678) = 0.01968 , a cs (865) = 0

by Kishino (personal ٛ comm.)(14)

a y (λ ) = a y (440) ⋅ exp{−0.014 ⋅ (λ − 440)}

by Bricaud et al. (1981)(15)

The back scattering coefficients are given as follows: λ −4.32
bbw (λ ) = 0.5× 0.00288 ⋅ 
 550 by Morel (1974)(16)

bbc (λ ) = 0.081⋅bc (λ )by Oishi et al. (2002)(17)

 λ −0.2933
bc (λ ) = 0.27.chl 0.698 .
 550 by Kishino (personal ٛ comm..)(18)

bbs (λ ) = bs (λ ) ⋅ 0.01478

by Babin and Doerffer (1996)(19)

 λ −0.812
bs (λ ) = 0.125⋅ism ⋅ 
 550 by Kronfeld (1988)(20)

3-2. Neural network-based in-water algorithm
A neural network is used for the iteration process to derive estimates of the chlorophyll-a concentration (chl), inorganic
suspended solid concentration (ism) and absorption coefficient of colored dissolved organic matter (cdom) at 440nm(Tanaka et al., 1998)11. The inputs for the neural net are the normalized water-leaving radiances at 412, 443, 460,520, and 545 nm bands.

These bands are chosen because they have high saturation radiances.

Figure 3-2 Neural network-based in-water algorithm

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3-3. Alternative bands
Due to saturation problems of the GLI nominal band16 (749nm) and band18 (865nm) even in non-cloudy ocean areas,
GLI band13 (678nm) and band19 (865nm), were used for atmospheric correction. An iteration scheme is needed sincethese bands may be influenced by upward radiance from the sea. These near-infrared (NIR) water-leaving radianceshould be corrected before atmospheric correction by using specific in-water model.
(2) Physical and Mathematical aspects of the algorithm
This algorithm is originated from the OCTS atmospheric correction algorithm. An extension of the algorithm was madeto improve the processing accuracies by taking use of many additional and new GLI bands. The OCTS algorithm, inturn, was initially developed based on an atmospheric correction method used to handle SeaWiFS data (Gordon andWang, 1994), in taking the following effects into consideration:
・Polarized Rayleigh scattering (including the multiple scattering)・Aerosol scattering
・Aerosol absorption
・Scattering among aerosol particles and gas molecules
・Reflection of sky light from sea surface
・Absorption effect by ozone
・Transmittance along the path sun-to-sea surface-to-satellite・Sun glint
・Influence of white cap
・Absorbing effect of water vapor
・Absorbing effects of carbon dioxide gas and others
Corrections of the light components due to Rayleigh scattering, aerosol scattering, aerosol transmittance etc. are madeby using lookup tables prepared beforehand. In addition to the atmospheric pressure, ozone concentration, wind speedetc., the atmospheric correction requires also other kinds of analysis data made available by Meteorological Agency.
C. References
1.

Research Report: On the development of a retrieval algorithm for ADEOS-II/GLI ocean color data – Chapter 3: AnAlgorithm for GLI Ocean Color Atmospheric Correction, FY1996.

2.

Research Report: Advanced Earth Observing Satellite (ADEOS) OCTS Data Processing Algorithm Description
Ver.2.0 (NASDA) – Chapter 8.6: OCTS Type Algorithm.

3.

Babin M. and R. Doerffer (1996), Specifications for case II coastal water reference model., Algorithm Theoretical
Basis Document for MERIS (ATBD 2.12), ESA-ESTEC, pp.61-73.

4.

Bricaud, A.,A. Morel and L. Prieur(1981), Absorption by dissolved organic matter of the sea (yellow substance) inthe UV and visible domains, Limnol. Oceanogr., Vol.26, No.1, pp.43-53.

5.

Ding, K. Y., & Gordon, H. R. (1994). Atmospheric correction of ocean color sensors: Effects of the earth'scurvature. Applied Optics, 33 (30), 7096-7106.

6.

Fukushima, H. A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani (1998): Correction ofatmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance, J.
Oceanography, 54, 417-430.

GLI Algorithm Description

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7.

Fukushima, H. and M. Toratani (1997): Asian dust aerosol: optical effect on satellite ocean color signal and ascheme of its correction. J. Geophys. Res., 102(D14), 17,119-17,130

8.

Gordon, H.R.(1997),Atmospheric correction of ocean color imagery in the Earth Observation System era, J.
Geophys. Res., 102 (D14), 17,081-17,106.

9.

Gordon, H.R., and M. Wang.(1994), Retrieval of water-leaving radiance and aerosol optical thickness over theoceans with SeaWiFS: a preliminary algorithm, Appl. Opt., 33 (3), 443-452.

10. Joseph, J. (1950), Untersuchungen über ober- und unterlichtmessungen im Meere und über ihren zusammenhang
mit durchsichtigkeitsmessungen, Deut. Hydorograph. Z., Vol.3, pp.324-335.
11. Kronfeld, U. (1988),Die optishen eignenschatten der ozea nischen shwebstoffe und ihre bedentung furfernerkundung von phytoplankton., GKSS Forschungszentrum, D-2054 Geesthacht, Germany, p.153.
12. Lee, Z., K. Carder, S. Hawes, R. Steward, T. Peacock and C. Davis (1944), Model for the interpretation ofhyperspectral remote-sensing reflectance, Applied Optics, Vol.33, pp.5721-5732.
13. Morel A. and B. Gentili (1991), Diffuse reflectance of oceanic waters: its dependence on sun angle as influenced
by the molecular scattering contribution, Applied optics, 30, 4427-4438.
14. Morel, A. (1974), Optical properties of pure water and pure sea water, chapter 1 in Optical Aspects ofOceanography, edited by N. G. Jerrov and E.S. Nielsen, Academic Press, New York, 1-24.
15. Nakajima, T., M. Tanaka, M. Yamamoto, M. Shiobara, K. Arao, and Y. Nakanishi (1989), Aerosol opticalcharacteristics in the yellow sand events observed in May, 1982 at Nagasaki - part II models, J. Meteor. Soc.
Japan, 67, 279-291.
16. Oishi, T., Y. Takahashi, A. Tanaka, M. Kishino, and A. Tsuchiya (2002), Relation between the backwordas well as
total scattering coefficients and the volume scattering functions by cultured phytoplankton. J. School Mar. Sci.
Technol. Tokai Univ., Vol.53, pp.1-15 (in Japanese with English abstract).
17. Pope, R. M. and E. S. Fry (1997), Absorption spectrum (380-700) of pure water, II: Integrating cavity measurement,Applied Optics, Vol.36, pp.8710-8723.
18. Shettle, E.P., and R. W. Fenn. (1979), Models for the aerosols of the lower atmosphere and the effects of humidity
variations on their optical properties, AFGL-TR-79-0214, 675, 94pp,.
19. Siegel D. A., M. Wang, S. Maritorena, and W. Robinson (2000): Atmospheric correction of satellite ocean color
imagery: the black pixel assumption, Appl. Opt., 30, 21, 3582-3591.
20. Tanaka, A., T. Oishi, M. Kishino, and R. Doerffer (1998), “Application of the neural network to OCTS data”, Proc.
Ocean Optics XIV, S. G. Ackleson and J. Campbell (eds.), Office of Naval Research, Washington, D.C..

GLI Algorithm Description

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