3.2.2

GLI in-water algorithms (OTSK2/5/6/7)• Outline of the GLI in-water algorithm
GLI ocean algorithms are listed below (Table A1). Summary table and algorithms flow chart of the GLI in-wateralgorithms are shown in Table A2 and Figure A1.

Table A1 GLI ocean algorithms
Algorithm code
OTSK1
OTSK2
OTSK5
OTSK6
OTSK7
OTSK14
OTSK13OTSK3
OTSK4aOTSK4bOTSK8OTSK9OTSK10OTSK11OTSK12
OTSK16
OTSK17
OTSK18OTSK19targetStandard algorithms (Dec 2003)
Atmospheric correction
Chlorophyll-a concentration
Attenuation at 490nm
Suspended solid concentration
Colored dissolved organic matter
Photosynthetically available radiation
Bulk sea surface temperature
Research algorithms
Accessory pigment (Carotenoid, Phycobilin)
Primary production by ecosystem model
Primary production by natural fluorescence
In-water optical measurements
Study of in-water optical parameters
Measurements for atmospheric correction
In-situ SST measurements
Vicarious calibration measurements
Skin sea surface temperature
Phytoplankton species (Trichodesmium)Phytoplankton species (Coccolithus)
AP and APH

Investigator
Fukushima, Frouin
MitchellMitchellKishinoMitchellFrouinKawamura

Table A2. Formulations of the GLI in-water algorithms
Variable

Algorithm source

CHLA

OC4-GLIv3
Modified from
O’Reilly et al.,
1998

Max Band CHLA = 10^(a0 + a1*R + a2*R + a3*R ) +a4
Ratio,
a=[0.531, −3.559,4.488, −2.169, −0.230]
Modified Cubic
Polynomial R = log10(NWLR443 > NWLR460 > NWLR520) / NLWR545)

K490

GLI-K490
Modified from
Mitchell andKahru, 1998

K490 = 10^(a0 + a1*R + a2*R + a3*R )Cubicpolynomial in a=[ −0.825, −1.362, 1.094, −0.777]
log-log
R = log10(NWLR460/NLWR545)

CDOM

In preparation

REDTIDE

Kahru and
Mitchell, 1998

PIGMENTO’Reilly et al.,1998

GLI Algorithm Description

Type

Equation, Coefficients (a), Band Ratio (R)
2

2

3

3

CDOM440 = 10^(a0 + a1*R)
Linear band
ratio in log-log a = [−1.493, −1.618]
R = log10(NWLR443/NLWR520)
Threshold of REDTIDE index > 0, if R < 0.8 and CHLA ( 1.0band ratio R = NWLR380/NLWR412

Powerfunction

PIGMENT = 1.34*CHLA

3.2.2-1

0.98

CAROT

Derived from
Aiken et al., 1998

SS

KishinoTurbidCase2 waterPark

Linearregression

CAROT = 0.135 + 0.912*CHLA

SS=IOSS+OSS
IOSS←Neural network2
OSS=10^(0.074*(log10(CHLA)) +0.8411*log10(CHLA) −0.3273)
Threshold of Turbid case 2 water flag =1, if NWLR545 > threshold
NLWR545 Threshold is determined with CHLA (see text)
Neuralnetwork

Start
Outputof
OTSK1,
bathymetry,geolocation

SST

IInput ALL NWLR Ocean Bands and
Mask

Masked pixel?

Y

N
CHLA calculation
K490 calculation
SS calculation

CHLA = 0,
K490 = 0,
SS = 0,
CDOM =0,
REDTIDE FLAG = 0

CDOM calculation
Red Tide Flag calculation

CHLA
Write outputs

K490
SS

End

CDOM
REDTIDE FLAG

Figure A1. Algorithm flow chart of OTSK2, 5, 6 and 7

GLI Algorithm Description

3.2.2-2

• Chlorophyll-a, colored dissolved organic matter, and K490 algorithms (OTSK2/6/7)
A. Algorithm Outline(1)

Algorithm Code: OTSK2, 5, 7, 3, 4a/b, 8, 9(2)

Product Code: CHLA, K490, CDOM (Status: standard level), CAROT (Status: Research level)(3)

PI names: G-0020 B.Greg Mitchell for OTSK2,5,7(4)

Overview of algorithm
The goals of this project are to develop algorithms for GLI using our large, global bio-optical database(including CalCOFI, Indian Ocean, sub-tropical Atlantic Ocean, Western Pacific, Japan Sea, East China Sea,
Southern Ocean, and ACE-Asia). Other regional and global bio-optical data sets will be merged with our
database.

Our bio-optical data set includes spectral reflectance (various channels depending on the

instrument), pigments (fluorometric and HPLC) and absorption coefficients for particulate and soluble material.
Our current database includes more than 1000 high-quality sets of reflectance and chlorophyll data in nonpolar oceans areas and over 100 data points from the Southern Ocean. Including multiple casts per station the
total number of radiometric profiles is over 3500. Our radiometric data does not have exact spectral band
center and bandwidth match for all the GLI ocean color bands. Therefore, we use spline interpolation toestimate remote sensing reflectance (Rrs) at any wavelength and then convert Rrs to normalized water leaving
radiances (NWLR) at GLI bands using F0bar weighted over the response function of each GLI band. In the
future we may use optical models to generate high spectral resolution synthesis of our actual observations andestimate NWLR in the GLI bands from these models but at present the spline interpolation procedure seems tobe sufficiently accurate considering other sources of errors and the widths of the GLI bands. We present ourcurrent versions of GLI standard algorithms for chlorophyll, pigments, K490, CDOM and red tide detection. Our
preliminary semi-analytical algorithm is capable of deriving CHLA, CDOM, spectral absorption andbackscattering coefficients for particulate matter but needs refinement as it is currently inferior to empiricalalgorithms for standard products.

B. Theoretical Description(1)

Methodology and Logic flow
As discussed elsewhere (Mitchell and Kahru 1998, O'Reilly et al. 1998, Kahru and Mitchell 1999), simple 2band empirical algorithms perform well in Case 1 waters for deriving standard products of CHLA and K490 anda novel indicator of red tides. Our preliminary version of a semianalytic algorithm as well as other semianalytic
algorithms (Garver and Siegel 1997, Carder et al. 1999) are capable of deriving more products (e.g. thespectral absorption and backscattering coefficients) but due to atmospheric correction uncertainty at shortwavelengths and parameterization problems they are currently inferior to empirical algorithms for standard
products.

Our previous CHLA algorithm was based on the CAL-P6 algorithm (Kahru and Mitchell 1999) and used
NWLR490/NWLR565. Due to the possible saturation problem at both of these bends we changed the CHLA
algorithm during 2000.

Our analysis shows that normalizing to NWLR545 may actually improve CHLA

retrieval compared to normalizing to NWLR565. The GLI NWLR545 does not have the saturation problem
either. Among the band ratios with NWLR545 the sequence of preference is as follows: NWLR490, NWLR460,
NWLR443. Unfortunately we cannot use the best band ratio NWLR490/NWLR545 (see Table 1) due to thesaturation problem at the GLI 490 nm band. Our second choice is a maximum band ratio (MBR) algorithm

GLI Algorithm Description

3.2.2-3

using 3 bands (the maximum of NWLR443, NWLR460 and NWLR520) with NWLR545 in the denominator.
This MBR algorithm is slightly superior to other band ratio algorithms not using NWLR490. In satellite-derived
NWLR data the use of MBR has additional advantages. A well-known problem with the SeaWiFS ocean color
sensor is the underestimation of NWLR at shorter wavelengths that is quite drastic at 412 nm but commonly
evident even at 443 nm. As the MBR algorithm picks the maximum ratio, it will reduce the effects of biased
NWLR by switching to longer wavelengths in such a case. The MBR concept was introduced by O’Reilly et al.
(1998) and applies a single set of coefficients to a band ratio selected as maximum among a set of band ratios.
The band actually used in the numerator changes from shorter wavelengths to longer wavelengths withincrease in CHLA. We modified the scheme of O’Reilly et al. (1998) by selecting the maximum among the
NWLR ratios and not Rrs ratios. This improves CHLA retrieval significantly as it shifts the actually used band
towards longer wavelengths with better correlation with CHLA. The algorithm covers the full range of CHLA
from 0.01 to 100. This report provides updated coefficients in Table 2 based on work in 2001 to merge newdata sets.

Table 1. Comparison of different CHLA algorithms using modified cubic polynomial formulation(a0 + a1*R + a2*R2 + a3*R3)+a4. Due to the saturation problem of NWLR490 we are currently
CHLA = 10
recommending to use algorithm 3 with the coefficients in Table A2.

1

Band RatioR

2

RMS Error

R = log10(NWLR490 / NWLR545)

0.944

0.12172

R = log10( (NWLR443 > NWLR460 > NWLR520) / NWLR545)

0.941

0.12493

R = log10( (NWLR443 > NWLR460 > NWLR520 ) / NWLR545)

0.938

0.1270In March 2002 the K490 algorithm was modified from our previous version due to the possible saturation
problem of the GLI 490 and 565 bands.

The current K490 product is derived as a cubic function of

NWLR460/NWLR545 in the log-log space (Table 2, Fig. 3). It provides better retrieval compared to the original
approach of SeaWiFS using NWLR443/NWLR555.

Due to measurement error in the K490 estimates in veryclear waters, some K490 estimates that were below the estimated pure water values were excluded in thecoefficient optimization.

As we have previously reported (Mitchell and Holm-Hansen, 1991; Mitchell 1992), the Southern Ocean has
significantly different bio-optical relationships compared to lower latitudes. Figure 4 shows our SPG ANT MBR
algorithm fitted to our updated JGOFS and AMLR data sets compared to the standard NASA OC4v4 (note at
this time SPG ANT is for SeaWiFS wavelengths, not GLI alternative bands). Figure 5 shows the error of the-3

NASA OC4v4 as a function of CHL concentration. In the range of 0.2 – 2.0 mg m , OC4v4 will underestimate
CHL in the Southern Ocean by about 30-50%. This is the range of CHL that predominates in the Southern
Ocean and therefore it is essential to determine alternative algorithms for these regions. In Figure 6 we show
match ups for CHL between ship surveys and SeaWiFS. The underestimates that are indicated in our in situ
algorithm data set (Figure 4) indeed are confirmed in the satellite - ship comparison of Figure 6 underscoring aneed to resolve the issues related to Southern Ocean algorithms. We note that there should also be special
attention to Arctic algorithms (Mitchell 1992) but we do not have data with new instruments to confirm our earlyobservations.

GLI Algorithm Description

3.2.2-4

For the CDOM product all combinations of GLI band ratios were tested versus in situ measurements of as(300),
2i.e. absorption coefficient of dissolved material at 300 nm. Several band ratios provided similar values of r and2

2

RMS error. The best band ratio with the highest r and lowest RMSE appeared to be NLWN380/NWLN545 (r= 0.802, RMSE = 0.059). Band ratios NLWN412/NWLN520, NLWN412/NWLN545 and NLWN443/NWLN520
were slightly but not significantly inferior. A large proportion of the scatter is actually due to measurement
errors and not due to algorithm errors. Our experience with SeaWiFS (Kahru and Mitchell 1999) shows that
atmospheric correction at short wavelengths in the coastal zone may have large errors. Therefore we did notselect band ratios including short wavelength bands (380, 412 nm) for the CDOM algorithm. As similar results
can be achieved with other bands for which atmospheric correction should be more robust, we selected
NWLR443/NWLN520. This ratio appears to be well correlated with CDOM absorption due to the fact that asignificant part of phytoplankton influence on this ratio tends to cancel out. Additional advantage of this band
ratio (compared to band ratios using bands 380 and 400 nm) is that 443 and 520 nm bands are common onother sensors and the same algorithm can therefore be used on these sensors, provided that accurate NWLR
values are available. The operational CDOM definition is the amount of absorption by the dissolved organic
component at a certain wavelength.

We originally used the wavelength of as(300) as a proxy to theconcentration of CDOM due to a better signal to noise ratio at this wavelength. At 440 nm wavelength the2

relative amount of measurement error is significantly higher which results in the reduction in r of the estimate.
Per request of Dr. Kishino and according to original NASDA plans, we are now providing algorithm for as(440).
A novel algorithm for the detection of red tides has been developed using the unique 380 nm spectral band
available on GLI that is not available on other planned ocean color satellites. The algorithm is based on theapproach described in Kahru and Mitchell (1998) and should allow detection of blooms of common red tideorganisms with high intracellular concentrations of mycosporin-like amino acids (MAAs). For chlorophyll
concentrations greater than 1-2 mg m

-3

the ratio NWLR380/NWLR412 diverges significantly for the red tidedata set compared to comparable chlorophyll concentrations observed for routine CalCOFI cruises.

Theavailability of the 380 nm band on GLI allows the potential for red tide detection while concentrations are still
moderate. It is critical for this algorithm that the NWLR380 and NWLR412 values are accurately derived. This
may not be an easy task considering the problems with SeaWiFS at 412 and 443 nm (Kahru and Mitchell 1999).

(2)

Physical and Mathematical aspects of the algorithm
The algorithms are simple, empirical relationships based on in-water NWLR and measurements of the
products of interest. There are no physical assumptions and the equations are derived from the best fits ofmeasurements with a few constraints.

C. Practical Considerations(1)

Programming, Procedural, Running Considerations
Program Requirements: Table 2 shows information about the expected software generated from this algorithm
Table 2.
Program Memory
Program Size
Required Channels
Necessary/Ancillary Data

GLI Algorithm Description

N/A
Each algorithm less than 100 lines of C code
All GLI Ocean Bands
Top-of-Atmosphere radiances in bands with saturation problem, SST,
Bathymetry, Geo-location (latitude, longitude)

3.2.2-5

Expected Disk Volume

2 bytes per pixel per algorithm per productSpecial Programs or Subroutines

NONE

D. Publications and Papers

1.

Kahru, M. and B.G. Mitchell, Bio-optics and remote sensing of a massive red tide off Southern California, ASLO
Aquatic Sciences Meeting, Santa Fe, New Mexico, February 10-14, 1997, p.203-204.

2.

Kahru, M., and B.G. Mitchell, 1998, Spectral reflectance and absorption of a massive red tide off southern

3.

Mitchell, B.G. and M. Kahru, Algorithms for SeaWiFS standard products developed with the CalCOFI bio-optical

California, Journal of Geophysical Research, 103(C), 21601-21610, 1998.

data set, Cal. Coop. Ocean. Fish. Invest. R., 39:133-147, 1998.
4.

O’Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, and C.R. McClain,
Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res., 103(C):24,937-24,953, 1998.

5.

Kahru, M. and B. G. Mitchell. Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the
California Current, Int. J. of Remote Sensing, 20: 3423-3429, 1999.

6.

Kahru, M., B.G. Mitchell. Influence of the 1997-98 El Niño on the surface chlorophyll in the California Current,
Geophysical Research Letters, Vol. 27, No. 18, 2937-2940, 2000.

7.

Kahru, M. and B. G. Mitchell. Seasonal and non-seasonal variability of satellite-derived chlorophyll and CDOM
concentration in the California Current. J. Geophys. Res, 106(C2): 2517, 2001.

E. References
1.

Aiken, J., G.F. Moore, C.C. Trees, S.B. Hooker, and D.K. Clark, The SeaWiFS CZCS-type pigment algorithm,

2.

Carder, K.L, F.R. Chen, Z.P. Lee, and S.K. Hawes, Semianalytic Moderate-Resolution Imaging SpectrometerNASA Tech. Memo. 104566, vol. 29, 34 pp., 1995.

algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures. J.
Geophys. Res., 104(C3):5403-5421, 1999.
3.

Garver, S.A., and D.A. Siegel, Inherent optical property inversion of ocean color spectra and its biogeochemicalinterpretation. 1. Time series from the Sargasso Sea. J. Geophys. Res., 102(C8):18,607-18,625, 1997.

4.

Kahru, M., and B.G. Mitchell, Spectral reflectance and absorption of a massive red tide off southern California,
Journal of Geophysical Research, 103(C), 21601-21610, 1998.

5.

Mitchell, B.G. and M. Kahru, Algorithms for SeaWiFS standard products developed with the CalCOFI bio-optical
data set, Cal. Coop. Ocean. Fish. Invest. R., 39:133-147, 1998.

6.

O’Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, and C.R. McClain,
Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res., 103(C):24,937-24,953, 1998.

7.

Kahru, M. and B. G. Mitchell. Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the
California Current, Int. J. of Remote Sensing, in press, 1999.

GLI Algorithm Description

3.2.2-6

Figure 1. Top, GLI CHLA algorithm (OC4-GLI version 3) using the maximum band ratio of NWLR443, NWLR460,
NWLR520 to NWLR545 shown as the blue line. Symbols are the non-polar algorithm data set used for the fitting.
Bottom, Ship-measured chl compared to OC4 GLIv3 estimate of chl for our algorithm data set.

GLI Algorithm Description

3.2.2-7

Figure 2. Distribution histogram of CHLA in the bio-optical database (top) and the switching of bands used in the
maximum-band-ratio algorithm OC4-GLI (bottom). The distribution histograms are shown for NWLR443 (black),NWLR460 (red) and NWLR520 (green) as a function of CHLA. NWLR443 is used at low CHLA, NWLR460 in the
middle range and NWLR520 at high CHLA.

GLI Algorithm Description

3.2.2-8

Figure 3. The GLI K490 algorithm using band ratio of NWLR460 and NWLR545 (top). The pure water value is shownwith a horizontal dashed line, estimated pure water reflectance and K490 are shown as a big red diamond. The
quantile-quantile plot is shown in bottom panel.

GLI Algorithm Description

3.2.2-9

Figure 4. GLI Southern Ocean CHLA algorithm (GLI-ANT) using the maximum band ratio of NWLR443, NWLR460,
NWLR520 to NWLR545 (blue line) compared to current global GLI CHLA algorithm OC4-GLIv3 (green line).

GLI Algorithm Description

3.2.2-10

Figure 5. Percent error that results from using the current GLI CHLA algorithm OC4-GLIv3 compared to GLI-ANT.
The OC4-GLIv3 algorithm would underestimate CHLA at moderate concentrations (from about 0.1 to 5 mg m-3) but
would overestimate at both low and high concentrations.

GLI Algorithm Description

3.2.2-11

SeaWiFS median 3x3 chl

10

AMLR1998-2001 1-day composites

9
8
7
6

y = 0.5421x
R2 = 0.6839

These two stations were on the edge of a"superbloom" blown out from the Bransfield
Strait and therefore data is reflecting
patchiness

5
4

D084 and D085

3

1998

2

y = 0.5318x
R2 = 0.9176

1

1999
2000

0

2001

0

2

4

6

8

10

In situ surface Chl, mg/m3

Figure 6. Evidence for the underestimation by SeaWiFS of Southern Ocean CHLA at medium CHLA concentration.
As evidenced by the slope of the regression, the OC4v4 algorithm shows only 54% of in situ CHLA. Evidence for the
possible overestimation at very low CHLA is not conclusive but possible.

GLI Algorithm Description

3.2.2-12

• Suspended solid algorithm (OTSK6)
A. Algorithm Outline(1)

Algorithm Code: OTSK6(2)

Product Code: SS (Status: standard level)(3)

PI names: G-0059 Motoaki Kishino for OTSK6

B. Theoretical Description(1)

introduction
The substances that determine the turbidity of the ocean are phytoplankton, its degradation organic matter(suspended or dissolved), and inorganic suspended matter such as clay particles. In general, in open oceans,
phytoplankton and organic matter exist but inorganic matter does not in most cases (Case 1 waters). However,in coastal waters, a lot of organic and inorganic suspended matter is transported by river from land.
Eutrophication of water also occurs by land-originated nutrient, which brings phytoplankton bloom andsometimes red tide. Under this condition, light distribution is quite different from that in open oceans (Case 2
waters).
Traditional chlorophyll algorithm developed for case 1 waters gives in many cases too high value of chlorophyll
if applied to coastal waters. In open oceans, phytoplankton and its byproducts control the radiation transfer, andthe data (remote sensing reflectance, or normalized water leaving radiance) after atmospheric correction ofsatellite ocean color shows the spectra that is determined almost by chlorophyll concentration. On the contrary,in coastal waters, dissolved organic matter or suspended inorganic matter affect the underwater light field.
Therefore, the traditional algorithms that do not take into account this effect cause significant error since theytend to attribute the spectra entirely to chlorophyll concentration.
Ocean color algorithm for coastal zone need to be constructed taking into account all of phytoplankton, organic
or inorganic suspended matter and dissolved organic matter. It is extremely difficult to use traditional empiricalmethod (e.g. band ratio) for that purpose. The concentration of various materials should be considered. As acandidate, there is inversion method that inverses remote sensing reflectance or normalize water leaving
radiance. In inversion technique are the direct solution, the matrix inversion technique and so on. However, its
disadvantage is to take significant time to find solution. While neural network needs time to be educated, it canprocess data within a short period.

(2)

Database
Ocean color database obtained by Japanese scientists includes in-water radiance, chlorophyll concentrationand so on, measured by the groups from Hokkaido University, Tokyo University of Fisheries, National Polar
Research Institute, RIKEN, Tokai University, Nagasaki University and local Fisheries Institutes. This database
covers wide area from Bering Sea in the north to Southern Ocean in the south, and includes a lot of data fromnear-Japan seas, coastal waters and inland waters. But the number of suspended solid data is very limited.
In this study, the database was built from the data measured by Tokyo University of Fisheries, National Polar
Research Institute, RIKEN and Nagasaki University. The total data were 306. PRR-600, MER-2040, PRR-800(Biospherical Inc.) were used to measure in-water radiance/irradiance. When the instrument does not include
any GLI band, interpolation was performed as follows.
1) For PRR-800 data, the target band was estimated from several bands in both sides by Spline interpolation.
400 nm: interpolated using 4 bands, 380 nm, 395 nm, 412 nm, 443 nm

GLI Algorithm Description

3.2.2-13

460 nm: interpolated using 4 bands, 412 nm, 443 nm, 465 nm, 490 nm
545 nm: interpolated using 4 bands, 520 nm, 532 nm, 555 nm, 565 nm
2) In other cases, the target band was obtained using the relationship between interpolated value and fourbands3) When one band of nearest two bands does not exist, the relationship of the two bands is obtained from thedata of two bands that measured at the same time (Interpolation (Rrs)-2 in Original.xls)
520 nm: from 510 nm
565 nm: from 555 nm
From the database, the relationship between chlorophyll-a concentration and suspended solid was found. In
Case 1 waters, a certain relationship probably exists between two parameters. First, we removed the data ofextremely small SS concentration, which may come from measurement error. Second, we also excluded thedata of extremely high SS concentration compared to other measurements with nearly same chlorophyll
concentration, because it may include significant amount of inorganic SS. As a result, we found the relationshipbetween chlorophyll concentration and SS concentration (Fig.SS-1). After such data quality control, 74 dataremained. These data can be considered those measured in Case 1 waters.

Figure SS-1 The relationship between chlorophyll a (Chl) and suspended solid (SS) concentrations.
Black line shows regression line.
SS= 10^(-0.074(log10(Chl))2 + 0.8411(log10(Chl))-0.3273)(3)

r =0.930

Algorithm
SS can be split into organic suspended solid (OSS) and inorganic suspended solid (IOSS).

GLI Algorithm Description

3.2.2-14

The OSS can be estimated using chlorophyll a concentration (Fig. SS-1). To estimate IOSS, an neural network(NN) algorithm which inversed bio-optical model was developed. The bio-optical model based on Joseph’s twostream equations (Joseph, 1950) is to simulate remote sensing reflectance (Rrs) at 412, 443, 460, 520 and
545nm from concentrations of chlorophyll a, IOSS and CDOM. Although the Joseph’s two-stream equation
includes assumptions such as it is realized under the total diffuse light, advantage of using the equation can be
expressed by two parameters that are absorption coefficient and backscattering coefficient. Using the Joseph’s
two-stream equation, Doerffer (1979) shows a good result from a laboratory experiment.
The NN algorithm require a education in order to determine biases of each neuron and weight among neurons.
To make data set for education of the NN, 100,000 pairs of Rrs at 412, 443, 460, 520 and 545nm were
simulated by bio-optical model from random concentrations of chlorophyll a, IOSS and CDOM, respectively.
The Stuttgart Neural Network Simulator (SNNS) was used for the NN construction and education.
A result of the NN algorithm applied to in situ data is shown in Fig. SS-2. We propose the NN algorithm shownin the figure to be the algorithm for SS.

Figure SS-2 Relation between in situ data and retrieved by neural network (NN)(4)

Conclusion in this subsection
SS concentration measurements may include considerable error from the spots in suspended solid itself andthe foreign dusts, which differs from other measurements. As a result, the correlation is not so high. Algorithm
should be improved with more truth data in future.
For Case 2 waters, with the advanced model of optical process, the algorithm for estimating both SS anddissolved organic matter should be established. For that purpose, the optical property measurement of various

GLI Algorithm Description

3.2.2-15

materials, the establishment of the relationship of optical property and in-water materials in various ocean areas
are required.

D. References1.

Doerffer, R., Application of a two-flow model for remote sensing of substances in water, Boundary-Layer Meteol.,
vol. 18, 221-232, 1979.

2.

Joseph, J., Untersuchungen über Ober- und Unterlichtmessungen im Meere und über ihren Zusammenhang mit
Durchsichtigkeitsmessungen, Deut. Hydorograph. Z., vol. 3, 324-335, 1950.

GLI Algorithm Description

3.2.2-16

• Turbid Case 2 water flag algorithm
A. Algorithm Outline(1)

Algorithm Code: OTSK9(2)

Product Code: flag

(3)

PI names: (Y. J. Park, JAXA)

B. Theoretical Description
Turbid case 2 water flag will be set on for the pixels of which water reflectance is greater than case 1 water limit.
In general, water reflectance varies with chlorophyll and other substance in water. But, to minimize theabsorption effect of colored dissolved organic matter and chlorophyll, green or red band is chosen for this flag.
For GLI, 545nm band will be used, since 565 nm band that planned to use can be easily saturated for turbid
pixels.
In this description, we use a simple model of water reflectance, R (A. Morel, JGR, 1988):

R = 0.33 ⋅

bb
,
a

(1)

where a, bb are absorption and backscattering coefficient. There is another relationship between absorption
and reflectance in case 1 waters (A. Morel, JGR, 1988):a=0.9 ⋅ K ⋅ (1 − R),
1 + 2.25 ⋅ R(2)where K is diffuse attenuation coefficient.
From these equations, we can get an expression of R as a function of K and bb:

R =  (1 − 2.25 ⋅ B) −
where B ≡(1 − 2.25 ⋅ B ) − 4B  / 2 ,
2



(3)

0.33b b.
0.9K

So, we can compute water reflectance for case 1 waters if we know bb and K, both of which are determined bychlorophyll concentration for case 1 waters. Note that we can use absorption coefficient directly than diffuse
attenuation coefficient, K. However, taking into consideration that K model (that is built from insitu
measurements) is more reliable than a for the present time (Morel and Maritorena, JGR 106, 2001), we use K
rather than a.

Recent update of models for K and bb (Morel and Maritorena, JGR 106, 2001) showsK(545nm) = 0.05212 + 0.04253 ⋅ [chl]0.656 , at 545 nm.

(4)bb(545nm) = 0.0010 + (0.002 + 0.01 (0.5-0.25 log10[chl] ) (550/545) bp(550)(5)

where b p (550) = 0.416 ⋅ [chl]

0.766

.

In the expression of bb, we don’t consider the variation of spectral dependence with chlorophyll concentration
because 545nm and 555nm are very close so that the effect is minor in computing bb at 545. We assume thatthe upper limit of particle scattering in case 1 water is 1.5 times (threshold factor) of average model:
Bp,lim(550)=0.416 [chl]

GLI Algorithm Description

0.766

×3.5

(6)

3.2.2-17

Using the upper limit of particle backscattering coefficient and the diffuse attenuation coefficient for a given
chlorophyll concentration, we get the upper limit of water reflectance, R lim (545) . The upper limit of remotesensing reflectance, can be computed using the following formula (Gordon, et. al, JGR 93, 1988):

Rrs lim (λ ) = (1 − ρ )(1 − ρ ) ⋅ R lim (λ ) /(Q ⋅ n 2 ) ,where ρ is surface reflectance upwelling radiance (0.021),(7)

ρ is surface reflectance for downward

irradiance(~0.043), Q is Q-factor (3.42) and n is refractive index of water (1.34).
This model limit is compared with the satellite derived remote sensing reflectance at 545nm, Rrs(545), which isthe normalized water leaving radiance divided by extraterrestrial solar irradiance.

Summarizing the above description, the bit of the “turbid case 2 water” flag will be set to be 1 (on), if

Rrs(545) > Rrs lim (545) ,(8)

where Rrs(545) is computed from the normalized water-leaving radiance of the pixel and Rrs lim (545) is
computed with Eq.(7). In Eq. (7), R lim (545) is computed with Eq. (3) using Eqs. (4), (5) and (6).
The present algorithm for turbid water flag uses two parameters, chlorophyll concentration and normalized
water leaving radiance at 545nm. It is expected that both parameters are not accurate in turbid region ofinterest. Conventional atmospheric correction may not give accurate result due to high value of water-leaving
radiance in near-infrared bands. Chlorophyll retrieval is also difficult if case 1 water algorithm is applied.
Furthermore, the radiance model and the bio-optical models that are exploited by present flag algorithm are not
perfect.
Therefore, it is recommended to adjust in operation phase some of parameters in remote sensing reflectancemodeling, for example, the threshold factor determining the upper limit of particle backscattering coefficient.

GLI Algorithm Description

3.2.2-18