•1

Reports of the International
Ocean-Colour Coordinating Group

IOCCG Report Number 1, 1998

Minimum Requirements
for an Operational,
Ocean-Colour Sensor
for the Open Ocean
Report of an IOCCG working group held inVillefranche-sur-Mer, France, October 6-7, 1997.
Chaired by Prof. André Morel.

Published by the International Ocean-Colour Coordinating Group(IOCCG), an Affiliated Program of the Scientific Committee on
Oceanic Research (SCOR).

ISSN: 1098-6030 ISBN: 978-1-896246-52-9

2 • Minimum Requirements for an Operational, Ocean-Colour Sensor

The IOCCG is an international group of experts in the field ofsatellite ocean colour, which acts as a liaison and communication
channel between users, managers and agencies in the ocean
colour arena.
The IOCCG is sponsored by NASA (National Aeronautics andSpace Administration), NASDA (National Space Development
Agency of Japan), ESA (European Space Agency), CNES
(Centre National D’Etudes Spatiales), JRC (Joint ResearchCentre, EC), CSA (Canadian Space Agency), AMRS (Allianceof Marine Remote Sensing), and SCOR (Scientific Committee
on Oceanic Research).
IOCCG Project Office: Bedford Institute of Oceanography, P.O.
Box 1006, Dartmouth, Nova Scotia, Canada, B2Y 4A2

www.ioccg.org

•3Contents
1.

Introduction ..................................................................................................................

1

2.

Inventory of ocean-colour sensors (past, present, future);
spectral bands, bandpass, noise equivalent radiances ......................................................

3

Scientific background: Ocean spectral reflectance interpretation, information versus
number of channels .......................................................................................................

63.

3.1 Information content and spectral resolution .......................................................... 6
3.2 Phytoplankton pigments and their influence on absorption andreflectance spectra .................................................................................................. 7
3.3 The need for additional wavelengths ...................................................................... 9
3.4 Do the same wavelengths always carry the same information? ................................ 10
3.5 Problem solving in a high-noise environment ........................................................ 10
3.6 Loss of information when wavelengths are dropped ............................................... 11
4.

Practical aspects: Number of channels versus objectives.................................................. 13
4.1 Atmospheric correction requirement ...................................................................... 13
4.2 Pigment index (chlorophyll) retrieval ..................................................................... 14

5.

Other requirements........................................................................................................ 18
5.1 Radiometric calibration, solar calibration, vicarious calibration .............................. 18
5.2 Dynamic range ...................................................................................................... 20
5.3 Sun glint avoidance ................................................................................................ 21

6.

Other operational aspects ............................................................................................... 22
6.1
6.2
6.3
6.4

7.

Ground resolution, temporal resolution, coverage ..................................................
Data distribution, accessibility ...............................................................................
Sensors in geostationary position ...........................................................................
Other orbital options .............................................................................................

22
23
24
26

Conclusions ................................................................................................................... 27

Appendices ............................................................................................................................. 29
I. Channel positions of various ocean-colour sensors, 1978 - 2000 .............................
II. Detailed information about the radiometric characteristics of various
ocean-colour sensors. .............................................................................................
a) CZCS ........................................................................................................................
b) OCTS .......................................................................................................................
c) POLDER ...................................................................................................................
d) SeaWiFS ....................................................................................................................
e) MODIS .....................................................................................................................

29
30
30
31
32
33
34

4 • Minimum Requirements for an Operational, Ocean-Colour Sensore) MODIS (continued) ..................................................................................................
f ) MERIS ......................................................................................................................
g) GLI............................................................................................................................
g) GLI (continued) ........................................................................................................
h) S-GLI ........................................................................................................................
h) S-GLI (continued) .....................................................................................................

35
36
37
38
39
40

References ............................................................................................................................... 41
List of Acronyms and Abbreviations ...................................................................................... 45

Introduction • 5

1. Introduction
The forerunner of all ocean-colour satellite
sensors, the CZCS (1978-1986), has led toa series of increasingly-sophisticatedinstruments: MOS, OCTS, POLDER, andSeaWiFS launched in 1996-1997, and
MODIS, MISR, OCM, GLI, OCI, OSMI,MERIS, and POLDER-2 scheduled to belaunched in 1998-2000. The sophistication,and expected improvements, have mainly
consisted in better radiometric performances(in terms of dynamical range and signal-tonoise ratio), and in increased number ofspectral channels (from 5 for CZCS up to
36 for MODIS and GLI). Many additional
channels are actually devoted to terrestrial
vegetation and atmospheric features, sincethese new instruments are designed to fulfill
multipurpose missions. Other kinds ofsophistication were also introduced to allow
multi-angle viewing capability (POLDER,
MISR), and, in addition, to determine thestate of polarization of the reflected radiation(POLDER 1 and 2). Such increased
complexities in the sensor’s design were a
response to the demands of the scientificcommunity, and were essentially based on
new research objectives or exploratory
projects. This goal implies the developmentof new experimental algorithms, and thedefinition of new products for the user
community.
The views presented in this document aredeliberately oriented in the opposite
direction, as they deal with a question that
could be formulated as follows: Is it possible
to satisfy the requirements for an operational
ocean-colour mission at low cost based on
simple sensors? In particular, is there any
possibility of providing useful information
if the sensor is operated with a reduced
number of spectral channels? If such aminimal set of bands could be identified, acorollary would be to recommend it forinclusion in all sensors, regardless of theirother capabilities and of the larger numberof channels they may possess. A
commonality in the spectral acquisition
provides important practical, as well as
scientific, advantages. Indeed, it would allow:❖ easy intercomparison between sensors,and even radiometric intercalibration inwell-defined conditions;❖ afull compatibility of operationalalgorithms for atmospheric correction andderivation of end products;❖ a meaningful data merging, at the level ofgeophysical products (pigment index,
aerosol optical thickness) or at the level ofthe initial quantities (e.g., spectral
normalized radiances);❖ a long-term continuity of ocean-colour

observations, based on stable, entirelycomparable, parameters; and therefore❖ the building up of a coherent data basefor biogeochemical studies and related
modeling activities, for physical studies
and models (heating rate, mixed layer
dynamics), and for climatological
purposes involving the radiative budget
and the effect of aerosol loading.
Several limitations on, and assumptions
about, the subject matter were adopted forthe present discussion. It has been postulated
that an environmental monitoring program
based on ocean-colour observation, and in
essence, similar to those in operation formeteorology and for some oceanographic
variables (sea-level, for instance), is needed,
and must cover the entire open ocean andthe coastal zones. Such a monitoring of themarine algal biomass distribution, activity,and impact, is a crucial aim of core programsof the IGBP (JGOFS, GLOBEC, LOICZ,
and their successors). It could be performed6 • Minimum Requirements for an Operational, Ocean-Colour Sensor

at low cost by using simple, small andinexpensive sensors. Such an operational
system is supposed to rely on dedicatedsensors for ocean colour, so that there will
be no need for compromises with otherkinds of missions when designing the
instrument. It would require several identical
instruments operating simultaneously inorbit. The deployment of such simple
sensors is in no way contradictory to theconception, development and use of moresophisticated instruments, designed foradvanced research purposes. Indeed, several
sensors will be in simultaneous operation in
the near future, with differing capabilities,some complementarity, and a partial planned
redundancy. Finally, the issue of algorithms,
beyond the scope of the present document,
need not be discussed. It has been admitted
that basic algorithms already exist at least
for routine products (e.g., see SeaBAM
Algorithm Evaluation, John E. O’Reilly andStéphane Maritorena, NOAA, NASA,
personal communication, 1997), and thatalgorithms for advanced products are still
under development, although less essential
for global monitoring.
At the beginning of the meeting inVillefranche-sur-Mer on the 6th October,
1997, it was realized and stated that the aimof the workshop was not to establish an“ideal” sensor, but to rely on the current stateof knowledge, and on well-tried andaccepted methodologies, so that one could

progress by successive consensus towards theidentification of “minimal” requirements for
future ocean-colour instruments. In the samevein, the opinions expressed hereafter are
those of the authors; in no way do they
represent the policy of Agencies. The authors
are the members of a working group set upby the IOCCG for addressing the questions
outlined above. The membership of theworking group is❖ André Morel, Chairman❖ Vittorio Barale❖ Annick Bricaud❖ Janet Campbell❖ Nicolas Hoepffner❖ Motoaki Kishino❖ Marlon Lewis❖ Shubha Sathyendranath❖ James Yoder

Howard Gordon has been involved inreviewing successive drafts, and otherinformation and help was provided by David
Antoine, Paul Bissett, Chris Brown, Curtiss
Davis, Mark Dowell, Wayne Esaias, Frank
Mueller-Karger, and Venetia Stuart.The
various Space Agencies with IOCCG
representation were asked to provide theinformation presented in this document,
and in particular, the information for thetables and appendices. The members of theworking group apologise for any inadvertentomissions or inaccuracies.

Inventory of ocean-colour sensors • 7

2. Inventory of ocean-colour sensors (past, present, future);
spectral bands, bandpass, noise equivalent radiancesThis section provides basic information
for past and present ocean-colour sensors(Table 1a), and for future sensors (Table 1b).
The spectral bands of each of these sensors,
together with their nominal bandwidths andnoise equivalent radiances are summarizedin Table 2. The band positions can bevisualized in Appendix I. Note that the bands(in Tables and Figures) are only thosededicated to ocean-colour observation andassociated atmospheric correction. Most of the
future instruments also include other channels
for other purposes (e.g. oxygen bands,
vegetation, water vapor), not considered here.
More detailed information on theradiometric characteristics of the various
sensors, and the way of deriving them, areprovided in Appendix II.

Table 1a. Characteristics of past and present ocean-colour sensors.
SensorCZCSOCTS

POLDER

MOS

SeaWiFS

Platform

Nimbus-7

ADEOS-1

ADEOS-1

IRS-P3

OrbView-2

Agency

NASA

NASDA

CNES

DLR

OSC/NASACountry

USA

Japan

FranceGermany/India

USA

Operation Start

Oct. 1978

Aug. 1996

Aug. 1996

Mar. 1996

Sep. 1997

Operation End

Jun. 1986

Jun. 1997

Jun. 1997

Mar. 2001

Sep. 2002

Orbital Inclination

99.3

98.6

98.6

98.7

98.2

Equatorial Crossing Time (h) 12:00

10:41

10:41

10:30

12:00

Altitude (km)

955

804.6

804.6

817

705

Resolution at Nadir (km)

0.825

0.7

6x7

0.5

1.1

Swath (km)

1566

1400

2400

200

2800

Tilt (degrees)

±20

±20

Variable

No±20

Direct Link

No

UHF/X-band

X-bandS-bandL-bandRecordedYes

X-bandX-bandNone

S-bandSolar Calibration

No

Yes

No

Yes

Yes

Lunar Calibration

No

No

No

No

Yes

Lamp Calibration

Yes

Yes

No

Yes

No

2330

No

ISRO

India

Nov. 1998

Nov. 2003

98.3

12:00

720

0.36

1420

± 20

X-band

Yes

Yes

No

Yes

AgencyCountry

Operation Start

Operation End

Orbital Inclination

Eq. Crossing Time (h)Altitude (km)Resolution at Nadir (km)Swath (km)Tilt (degrees)

Direct Link

RecordedSolar Calibration

Lunar Calibration

Lamp Calibration

Yes

Yes

Yes

X-bandX-band

1

705

10:30

98.2

Jun. 2003

1999

USA

NASA

EOS-AM1

IRS-P4

Platform

MODIS-AM

OCM

Sensor

No

Yes

No

X-bandNo

Variable

360

0.25

705

10:30

98.2

Jun. 2003

1999

USA

NASA

EOS-AM1

MISR

Table 1b. Characteristics of future ocean-colour sensors.

—

—

—

None

S-band

No

704

0.8

600

09:00/15:00

35

Feb. 2003

Feb. 1999

TaiwanTaiwanROCSAT

OCI

No

No

Yes

Yes

X-Band

No

800

0.85

685

10:50

98.13

Jul. 2002

Jul. 1999

Korea

KARI

KOMPSAT

OSMI

No

No

Yes

X-bandX-bandNo

1150

1.2/0.3

800

10:00

98.5

Feb. 2004

Mar. 2000

Europe

ESA

Envisat

MERIS

Yes

No

Yes

X-bandUHF/X-band

±20

1600

1/0.25

803

10:30

98.6

Jun. 2005

Jun. 2000

Japan

NASDA

ADEOS-2

GLI

No

No

No

X-bandX-bandVariable

2400

6x7

803

10:30

98.6

Jun. 2005

Jun. 2000

FranceCNES

ADEOS-2

POLDER-2

Yes

Yes

Yes

X-bandX-band

No

2330

1

705

13:30

98.2

Dec. 2005

Dec. 2000

USA

NASA

EOS-PM1

MODIS-PM

8 • Minimum Requirements for an Operational, Ocean-Colour Sensor

NE∆L
0.208
0.173
0.166
0.094
0.040

NE∆L
0.063
0.061
0.050
0.031

NE∆L
0.076
0.051
0.045
0.054
0.055
0.027
0.044
0.044
0.018
0.017
0.015
0.014
0.012
0.010
0.007

CZCS
Center λ FWHM
443
20
520
20
550
20
670
20
750
100

MISR
Center λ FWHM
446
41
557
27
672
20
867
39

GLI
Center λ FWHM
380
10
400
10
412
10
443
10
460
10
490
10
520
10
545
10
565
10
625
10
666
10
680
10
710
10
749
10
865
20

20
20
50
35
0.145
0.127
0.170
0.123

OCM
Center λ FWHM NE∆L
412
20
0.26
443
20
0.22
490
20
0.17
510
20
0.17
555
20
0.15
670
20
0.10
765
40
0.05
865
40
0.08

469 *
555 *
645 **
858 **

MODIS
Center λ FWHM NE∆L
412
15
0.048
443
10
0.032
488
10
0.025
531
10
0.018
551
10
0.019
667
10
0.008
678
10
0.007
748
10
0.009
870
15
0.006

OCTS
Center λ FWHM NE∆L
412
20
0.186
443
20
0.109
490
20
0.089
520
20
0.121
565
20
0.091
670
20
0.037
765
40
0.057
865
40
0.031

NE∆L
0.026
0.025
0.022
0.019
0.016
0.014
0.013
0.014
0.011
0.008
0.007
0.011
0.010

NE∆L
0.138
0.147
0.088
0.063
0.090
0.086
0.034
0.045

POLDER-2
Center λ FWHM
NE∆L
443
20
0.138
490
20
0.147
565
20
0.088
670
20
0.063
763
10
0.090
765
40
0.086
865
40
0.034
910
20
0.045

MERIS
Center λ FWHM
412.5
10
442.5
10
490
10
510
10
560
10
620
10
665
10
681
7.5
709
9
779
14
870
20
890
10
900
10

POLDER
Center λ FWHM
443
20
490
20
565
20
670
20
763
10
765
40
865
40
910
20

S-GLI
Center λ FWHM
412
10
443
10
490
10
520
20
565
20
625
20
680
20
710
20
749
20
865
20

OCI
Center λ FWHM
443
20
490
20
510
20
555
20
670
20
865
40

MOS
Center λ FWHM
408
10
443
10
485
10
520
10
570
10
615
10
650
10
685
10
750
10
815
10
870
10

NE∆L
0.081
0.067
0.054
0.052
0.029
0.021
0.012
0.007
0.009
0.006

NE∆L
—
—
—
—
—
—

NE∆L
0.192
0.124
0.148
0.141
0.150
0.105
0.141
0.088
0.102
0.034
0.033
NE∆L
—
—
—
—
—
—

NE∆L
0.092
0.077
0.056
0.049
0.043
0.031
0.019
0.015

(others: 1km)** Spatial resolution 0.25 km** Spatial resolution 0.5 km

OSMI
Center λ FWHM
443
20
490
20
510
20
555
20
670
20
865
40

SeaWiFS
Center λ FWHM
412
20
443
20
490
20
510
20
555
20
670
20
765
40
865
40

Table 2. Summary of the spectral bands used for ocean-colour applications (see Appendix for detailed characteristics of these sensors).
Center λ (nm), Bandpass (FWHM, nm), NE∆L (W m-2 sr-1 µm-1 )Inventory of ocean-colour sensors • 9

10 • Minimum Requirements for an Operational, Ocean-Colour Sensor

3. Scientific background: Ocean spectral reflectance interpretation,
information versus number of channels
A general overview of this problem is given
under the assumption that the atmosphericcorrection is “perfectly” made. The
constraints imposed by the need for an
accurate atmospheric correction, combinedwith those of deriving a useful ocean-colour
signal are examined in section 4.

3.1 Information content and spectral
resolution
Until 1996, all our practical experience ofsatellite ocean-colour techniques came froma single, experimental, satellite sensor: theCoastal Zone Colour Scanner (CZCS),
launched by NASA in late 1978, andoperated until the first half of 1986. New
and improved ocean-colour sensors haverecently been launched by Japan, France,
Indo-Germany and the USA, but thepotentials of these new experiments are still
being explored. Various countries plan evenmore sophisticated satellite sensors for the
next few years (see Table 1b). The CZCS hadvery modest spectral resolution and a modest
goal of estimating a phytoplankton pigment
index, whereas the new generation of present
and future satellite sensors have improved
spectral resolution, aimed at retrieving more
information from ocean colour, or at
improving the accuracy and precision of
retrievals of pigment concentration. The
success of these more ambitious goals still
remains to be evaluated.
In such a context, it is difficult, if not
impossible, either to draw solely on practicalsatellite experience to evaluate theinformation content in the ocean-colour
signal, or to discuss the choice of wavelengths
for ocean-colour missions. Instead, in thissection, we shall base our discussions on
theoretical grounds, on results of optical insitu determinations, and on practicalexperiences of remote sensing from aircraft.
For the conclusions drawn from suchanalyses to be valid in the context of satellite
remote sensing, the atmospheric correction
has to be implemented “perfectly” (seesection 4). With this proviso, we give, in thissection, a general overview of the potentials
and problems of remote sensing of ocean
colour.
In the theoretical discussions, we recognizethat ocean colour, or spectrally-resolved
reflectance at the sea surface, is generally
modeled as simple functions of theabsorption coefficient and the backscattering coefficient of the water body (e.g.,
Gordon et al., 1975; Morel and Prieur, 1977;
Kirk, 1981; Åas, 1987; Sathyendranath andPlatt, 1997), with additional influence frominelastic processes such as Raman scattering(Sugihara et al., 1984), fluorescence bychlorophyll-a (Neville and Gower, 1977;
Morel and Prieur, 1977; Gordon, 1979), andfluorescence by organic dissolved materials(Hawes et al., 1992; Lee et al., 1994). We
also recognize that phytoplankton pigments
influence ocean colour for the most partthrough their influence on the absorption
spectrum, except when fluorescence bychlorophyll-a (around 685 nm), or bybiliproteins (Yentsch and Phinney, 1985)
alters significantly the signal in specificwavebands. The reflectance spectra ofoceanic (Case 1) waters could be split into
two spectral regions (see e.g. Haltrin et al.,1997): a blue part (λ < 590 nm) notably
affected by elastic-scattering effects, and a redpart (> 590 nm) notably affected by inelastic
processes. Red fluorescence near 685 nm,
caused by chlorophyll, plays a dominant role
when the algal concentration is large, whereas

Scientific background• 11

in oligotrophic and mesotrophic waters,
Raman emission is the main inelastic process,which actually affects the whole spectrum(Stavn, 1990, 1992). For those Case 2 waters
where the dissolved organic matter isabundant (yellow-substance-dominated Case
2 waters), the blue fluorescence by thiscoloured substance may become an
important contributor to the reflectance(Hawes et al., 1992; Haltrin et al., 1997;
Dowell and Hoepffner, 1997). With thisbackground, we can explore the usefulnessof wavelengths in a remote-sensing context.

3.2 Phytoplankton pigments andtheir influence on absorption andreflectance spectra
As a first step in identifying the
wavelengths that carry useful information in
an ocean-colour, remote-sensing context, we
can examine the relationship between
phytoplankton pigments and theirabsorption spectra. At an elementary level,
it may be argued that the single mostimportant phytoplankton pigment ischlorophyll-a, and that, if we are onlyinterested in retrieving that one variable, thenit should be possible to achieve the goal if
we know phytoplankton absorption at one
suitable wavelength. To a large extent, suchan approach is feasible, for Case 1 waters, inparticular. In fact, simple optical models forsuch open ocean waters rely on theobservation that it is possible to parameterizeentire, spectrally-resolved, optical propertiesof the water body if we know theconcentration of chlorophyll-a in the water(Gordon et al., 1988; Morel, 1988). Bricaud
et al. (1995) have recently shown that thephytoplankton absorption at every single
wavelength in the visible domain can beparameterized as a function of thechlorophyll-a concentration (see also

Cleveland, 1995). Implicit in suchparameterizations is the assumption that thepigment composition of algae evolves in a
regular (and predictable) way fromoligotrophic to mesotrophic, and eutrophic
Case 1 waters.
However, in such parameterizations, there
is always a considerable amount of
unexplained variability, and at the next levelof complexity we can ask whether the
residual variability carries any useful
information. One potential source of thisvariability is fluctuations in theconcentrations of auxiliary pigments relative
to that of chlorophyll-a. From classical plant
physiology, we know that the exact
composition of phytoplankton pigments
varies with changes in the phytoplankton
species composition, or in the physiologicalstate of a given phytoplankton species. In
recent years, several studies have shown thatsome variability in the shapes ofphytoplankton absorption spectra is related
to changes in the packaging effect (Moreland Bricaud, 1981; Roesler et al., 1989; Sosik
et al., 1989), as well as in the pigmentcomposition (Mitchell and Kiefer., 1988;
Bricaud and Stramski, 1990; Hoepffner andSathyendranath, 1992). The inverse problem
of retrieval of pigment composition fromabsorption spectra is not trivial, for various
reasons: a large number of phytoplankton
pigments are present in natural
phytoplankton populations (a typical High
Performance Liquid Chromatography
analysis would probe a phytoplankton
sample for some 15 pigments and theirdegradation products, not including
biliproteins); the absorption bands ofindividual pigments overlap each other; andvariations in pigment packaging inphytoplankton cells can influence theirabsorption efficiencies.

12 • Minimum Requirements for an Operational, Ocean-Colour Sensor

However, promising techniques exist forrecovering some pigment information fromabsorption spectra (Bidigare et al., 1989).
Hoepffner and Sathyendranath (1991,
1993), for example, have developed atechnique that has the potential todecompose individual absorption spectra
into about a dozen or so Gaussian bands
associated with chlorophylls-a, b, and c, andcarotenoids. Such analyses suggest that some
information on phytoplankton pigmentcomposition may be retrievable, provided
that data on absorption are available at tenor so wavelengths, selected to match theabsorption maxima of these individual
pigments.
Thus, the goal of retrieving some
information on pigment composition rather
than just on the main pigment,
chlorophyll-a, increases the wavelength
requirement by an order of magnitude. New
results (Sathyendranath, unpublished results)
also suggest that small changes in the positionof the Gaussian bands, or in theirbandwidths, may be associated with yet morechanges in pigment composition, such aschanges in divinyl-chlorophyll-a relative tonormal chlorophyll-a, when thephytoplanktonic assemblage includes
prochlorophytes (Chisholm et al., 1988;
Morel et al., 1993). But obtaining
information on band widths of individual
Gaussian bands or on small changes (1~3nm) in the band positions requires that wehave absorption spectra resolved to about one
nm, within the spectral domain of 400-700
nm, thus increasing the resolution
requirement by another order ofmagnitude.
Another point to bear in mind when
discussing the retrieval of algal concentrationin the near surface layer is that a possibility
exists for passive remote sensing ofchlorophyll-a, through the sun-induced

fluorescence signal (Neville and Gower,
1977; Gower and Borstad, 1981). Even
though the effectiveness of such a method
has not been demonstrated using satellitedata, there is every evidence that thistechnique could be useful in Case 2 waters,
and even in oceanic Case 1 waters withmoderate chlorophyll concentrations (Babin
et al., 1996). The MODIS and MERIS
instruments are designed for such an
application (Letelier and Abbott, 1996).
Besides a low noise-equivalent radiance, thefluorescence detection requires additional
channels in the red part of the spectrum; atleast three dedicated channels are necessary
to monitor the height of the fluorescencesignal above a baseline determined using twochannels on either side of the emission peak.
Up to this point, we have discussed theinformation content in only the opticalproperties of phytoplankton. When dealing
with reflectance spectra of natural sea water
samples, additional issues have to beaddressed: in particular, the high absorption
coefficient of the water itself at wavelengths
larger than about 590 nm overwhelms theabsorption signatures of other substances. In
addition, reflectances are governed by thespectral dependency both of absorption andof backscattering. Even in Case 1 waters,
where most of the suspended materials often
originate from biological activity, the spectral
dependency and the magnitude of thebackscattering coefficients are not fully
understood (nor predictable). As a result,reflectance spectra are non-linear functionsof absorption, and strong signals inabsorption may be reduced to a small signal
in reflectance. Thus, the problem of
retrieving pigment information from optical
data is more complicated for sea surface
reflectance than for phytoplankton
absorption spectra.

Scientific background• 13

But Gege (1997) has analyzed high-resolution (512 channels in the 400-800 nm
range) reflectance spectra measured from aboat on Lake Constance, and shown that these
spectra can be used to quantify the concentrations of four phytoplankton species thatare known to occur at high abundances in
the lake. His analysis is based on exploiting
the differences in the absorption spectra ofthese phytoplankton species, which in turnare related to differences in their pigmentcompositions. Information on spectral absorption or reflectance properties of unusual
algal blooms, such as coccolithophores(Viollier and Sturm, 1984; Gordon et al.,
1988; Ackelson et al., 1994; Balch et al.,1996), Trichodesmium (Subramaniam andCarpenter, 1994), and Mesodinium rubrum(Doerffer, unpublished data) also show thatsome bloom-forming algae have spectral signatures that are very distinct from the morecommonly-encountered phytoplankton communities, again suggesting that it may be possible to identify such blooms from remotelysensed data.
In fact, methods already exist for identifying
coccolithophore or Trichodesmium blooms
using CZCS data (Brown and Yoder, 1994;
Subramaniam and Carpenter, 1994), thoughquantifying the pigment concentrations
within the blooms remains more problematic.
These results are encouraging (but see Garver
et al., 1994); at least under some favorable
conditions, they support the idea that
additional information on pigmentcomposition or on species composition ofphytoplankton can be recovered fromreflectance spectra, provided high-resolution
spectral information is available.

3.3 The need for additional
wavelengths
Up to now, we have considered mainly how
the wavelength requirements increase when
we explore the possibility of retrieving, in Case

1 waters, more than one pigment index
from ocean-colour data. In coastal Case 2
waters, the influence of other substances,such as dissolved organic (coloured) matterand of suspended sediments may be very
important, and may vary independently of
the chlorophyll concentration. Dissolved
coloured substances affect only theabsorption coefficient, whereas suspended
minerals may strongly enhance thescattering coefficient, with a minor, albeit
non-negligible, impact on absorption.
Similar arguments as above for Case 1
waters can be made for application of oceancolour data in these more complex waters,
where the need for additional wavelengths
emerges from the requirement to separate
the various dissolved and particulatematerials. In this context also, it quickly
becomes apparent that the possibility of
retrieval often increases with increasing
spectral resolution.
The goal here is to identify and, ideally,
quantify three variables: a phytoplankton
pigment “index”, concentration of dissolved
organic matter and concentration ofsuspended sediments. So it may be argued
that the problem should be solvable if we
had information on ocean colour at threesuitable wavelengths, and if we could relyon stable optical (specific absorption andbackscattering) coefficients for the threecomponents. But typically, addressing the
problem with some degree of confidence
requires information at more than three
wavelengths. This requirement arises froma number of factors:❖ The spectral features of interest are broad,they overlap each other, and, in certain
spectral windows, they resemble eachother.
❖ No single wavelength can be treated as aunique signal carrier for any of thecomponents of interest.

14 • Minimum Requirements for an Operational, Ocean-Colour Sensor❖ The relationships between ocean colour

and optical properties of the componentof interest are non-linear.
Under these circumstances, sophisticatedoptimization techniques are envisaged tosolve the problem. When such techniques
are used in a context where the data are noterror-free, redundancy is often essential toconstrain the solutions.

3.4 Do the same wavelengths always
carry the same information?
The oceans are complex and diverse, and
it is possible that wavelengths often used as
signal carriers for a given variable may
become contaminated by the influence ofother variables, such that the interpretationof the signal at those wavelengths becomesdifficult. For example, in the time-honored
algorithm for retrieval of a phytoplankton
pigment index, changes in the blue-green
ratio of reflectance are interpreted as changes
in chlorophyll-a.
However, we know that substances otherthan chlorophyll-a can change the blue-green
ratio. Most important of these is dissolved
organic matter or yellow substance, which
has a strong influence on the blue-green ratio.
In Case 2 waters with large quantities ofyellow substances, it would be impossible todistinguish them from phytoplankton
pigments, if the blue-green-ratio algorithm
were used (Fischer et al., 1986).
Changes in species composition can also
modify the blue-green ratio without
any change in the concentration ofchlorophyll-a. For example, variations in the
proportion of fucoxanthin relative tochlorophyll-a can change the blue-green
ratio of phytoplankton absorption by a factor
of up to six (Hoepffner and Sathyendranath1992). Morel (1997) has shown that theblue-green ratio of waters dominated by a
Synechococcus bloom may be different fromthat of waters with a “normal”
phytoplankton population having the sameamount of chlorophyll-a. Such a difference
may overestimate chlorophyll-a values by asmuch as a factor of three, if routine
algorithms are used. It therefore becomes
essential to use additional wavelengths toidentify such special situations and, if
possible, apply correction factors.
Thus, high spectral resolution in oceancolour data is desirable, not just in thecontext of retrieving additional informationfrom ocean colour, but also in the context
of making the estimates of pigment index
more rigorous and robust. If the number ofchannels is deliberately reduced (see section4), it must be accepted that someuncertainties and ambiguities will remain inroutine estimates.

3.5 Problem solving in a high-noise
environment
It is important to point out here that theusefulness of multiple wavelengths in aremote-sensing context is linked to theprecision in the data. Many of the moreadvanced applications, such as deriving
information on pigment composition, rely
on retrieving signals of very small
magnitudes, and so the usefulness of multiple
wavelengths is likely to be compromised if
the ocean-colour information is not available
at the necessary precision and accuracy. For
example, Fischer (1985) has shown that, fora given number of wavelengths, the numberof independent pieces of information thatcan be retrieved will drastically decrease withincreasing noise in the data. Thus, to meetthe more demanding requirements of oceancolour missions, the increased spectral
resolution of the sensors must be matchedby increased precision in the retrieved oceancolour signal, which in turn demands

Scientific background • 15

improvements in both instrument design
and atmospheric correction techniques.
All these analyses show that the wavelength
resolution required, and the precision neededin the retrieval of ocean colour, are linked tothe objectives of the remote-sensing
experiment. The more ambitious goals, andthe more stringent requirements, can be met
only through very high spectral resolution,and very high precision in the retrieval ofocean-colour spectra. Any choice of a small
number of wavelengths for a satellite mission
must therefore be recognized as acompromise, and it would be unreasonable
to expect that any subset of potentially usefulbands would meet all contingencies.

3.6 Loss of information when
wavelengths are dropped
In the preceding sections we saw how thenumber of wavelengths required in a remotesensing study increased as the goals become
more demanding. Conversely, we can explore
whether full spectral information is always
needed, or whether wavelengths can be
dropped while retaining all useful
information contained in high-resolution
data. This complex problem can be studied
only in specific contexts. The technique thathas been followed with some success involves
starting with high-resolution spectral
information, and evaluating the information
content in the data set. Then, wavelengths
are dropped successively from the data set,
and the change in retrieved information, if
any, is evaluated. The quality of thetechnique used for information retrieval
can be evaluated on the basis of the tightnessof the relationship between the ocean-colour
signal (if necessar y after sometransformations) and an oceanic property ofinterest; on the basis of the stability of therelationship when perturbations are
introduced; or on the basis of the linearityof the relationship.

Principal component analysis has beenused in some studies to address this problem.
Sathyendranath et al. (1989) examined theretrieval, in case 2 waters, of three optically
active components, namely algal pigments,
suspended material and dissolved organic
matter (see also Dowell and Hoepffner,1997). The choice of wavelengths to retain
can be based on a number of criteria. If, incertain spectral domains, the characteristic
spectra of the variables are similar to eachother, we can then conclude that those
spectral domains cannot be used to
distinguish between the variables of interest.
The corresponding bands may be dropped
from the sensor without loss of information.
Conversely, we could select those wavebands
in which the characteristic spectra of theindividual variables appeared to be mostdifferent from each other. Or, one could
examine the correlation between
wavelengths, and if two wavelengths were
perfectly correlated, one of them could be
dropped. Sathyendranath et al. (1989)
concluded that one could reduce the numberof wavelengths from 30 to 5, without
significant loss of information, provided thewavelengths retained were selectedjudiciously. By using a five-band approach,it has been shown that a full spectrum
reconstruction would be possible (Wernand
et al., 1997). In a similar study,
Sathyendranath et al. (1994) examined thewavelength requirements for distinguishingchlorophyll-a from phycoerythrin (abiliprotein), and again found that the task
could be accomplished successfully using five
wavelengths, whereas using only three
wavelengths led to a significant decrease in
the performance.
In interpreting the results from theseanalyses, it is also important to recognize thelimitations of the techniques used. The
problem is inherently non-linear, andprincipal component analysis is a linear16 • Minimum Requirements for an Operational, Ocean-Colour Sensor

technique. The approach is a purely statisticalone, and gives no indication of how theresults might be extrapolated to othersituations. The analyses may use observations
or modeled reflectance spectra as inputs, andthe results will be compromised if the inputs
are not sufficiently representative. Withinthese limitations, the studies cited above

suggest that, if the goal is to estimate two or
three independent variables from oceancolour data, then we need at least five
wavebands, whereas three CZCS-type
wavebands may be insufficient for the task.
This guideline will be used in the next
section.

Practical aspects: number of channels versus objectives • 17

4. Practical aspects: Number of channels versus objectives
4.1 Atmospheric correction
requirement
Before any interpretation of the marine
signals can be made, the crucial problem in
detecting ocean colour from space is to makean accurate “atmospheric correction”. As iswell known, backscattered radiation by air
molecules and aerosols is predominant (80%or more) in forming the radiance detected
at the top of the atmosphere (TOA) in thevisible part of the spectrum. The aerosol
nature and optical thickness are additional
unknowns in the ocean-colour remotesensing problem. Whatever the techniqueemployed for removing the atmosphericcontribution (not discussed here), specificinformation about aerosols is needed. This
information is available in the near infraredpart of the spectrum (NIR), where theatmospheric contribution to the TOA
radiance becomes 100%, to the extent thatthe marine signal, at least for case 1 waters,
vanishes in this spectral domain. Because
aerosol scattering varies spectrally, andpresumably in a smooth manner, at least two
measurements at two wavelengths arenecessary and thus represent a minimal
requirement. These two wavelengths, orchannels, must be sufficiently distant fromeach other in the NIR to capture the spectral
trend with some accuracy; in addition, theymust be not too far from the visible domain,
to ensure a safe extrapolation toward thisdomain. The last consideration when
making the NIR channel selection is to avoid
prominent atmospheric absorption bands(water vapor and oxygen), which exist in thisspectral domain. Safe and “clear” windows,
with sufficient width (reference HITRAN,
Rothman et al., 1987, 1992), are as follows:(0) Between 1024 and 1064 nm, transmittance above 0.998(1) Between 855 and 890 nm,

transmittance above 0.998(2) Between 772 and 786 nm,

transmittance above 0.998(3) Between 744 and 757 nm,

transmittance above 0.995(4) Between 704 and 713 nm,

transmittance above 0.978 ** for 2 cm precipitable water

The adoption of channel (1), actually used
for almost all instruments, is obviously agood choice. The available width of thisatmospheric window and thus of the
bandpass, can warrant a favorable S/N ratio;in addition, this wavelength is far enoughaway from the visible range, therefore a good
assessment of the spectral scattering
behaviour can be achieved.
A combination of channel (1) and channel(3) seems the most favorable. Indeed,channel (4), closer to the visible domain, andfarther from (1), could appear even moreconvenient than channel (3). Information inthis channel, however, is occasionallycontaminated by a residual marine signal, invery high chlorophyll waters, or in transition
zones between Case 1 and turbid-Case 2
waters. Therefore channel (3) is safer thanchannel (4) from this point of view, and is
wider.
A combination of channels (1) and (2)(instead of (3)), was adopted for MERIS.
Such a choice originates from a compromisewith another mission of MERIS, namely thedetection of the cloud top height. Thisdetection requires a narrow reference band(no oxygen absorption), close to the oxygen
absorption band (at λ = 760 nm); thisreference band was located around 754
nm. As a consequence, the position
corresponding to channel (3) is no longer

18 • Minimum Requirements for an Operational, Ocean-Colour Sensor

available for atmospheric correction, whence
the resort to the channel (2) in combination
with (1) for the MERIS instrument.
Using two channels, as (1) and (3), may
result in significant errors in the atmosphericcorrection, in presence of whitecaps (Frouin
et al., 1996; Gordon, 1997); the addition ofchannel (0) has been proposed to reduce theerrors, by exploiting differences in the
spectral properties of aerosol scattering andwhitecap reflectance in this NIR domain.
As a preliminary conclusion, a minimum
minimorum set could be the couple (1) and(3). If flexibility exists, adding channels (4)and/or (0) could offer advantages. In mostsituations, with no appreciable marine signal,
the extrapolation toward the visible domain
can be made more robust if channel (4) isavailable. Possibly, an additional piece of
information could also be extracted from thischannel concerning the detection ofabsorbing aerosols, when present. The
problem of whitecaps could lead to a
recommendation for the further adoptionof channel (0).

4.2 Pigment index (chlorophyll)
retrieval
The “colour” of oceanic Case 1 waters
shifts from deep blue in oligotrophic waters(very low chlorophyll concentration) to darkgreen in eutrophic waters (highconcentration). This shift results from thestrong absorption by algal pigments (notonly chlorophyll, but also various
carotenoids) in the blue band, with amaximum around 445 nm, compared withthe weak absorption in the green-yellow band
(550-580 nm). The reflectances, R, for these
two bands, or the normalized water-leaving
radiances, (Lw )N , are combined through theirratio. The rationale for using a ratio
technique, rather than another kind of
combination (a difference, for instance), liesin the variability and the uncertainty
affecting the absolute values of R or (Lw )N .

Natural as well as methodological sources ofvariations and errors inevitably interfere. The
quantities R and (Lw )N are directly sensitive
to the scattering and backscattering
coefficients, in such a way that they may varyby a factor 2 or more, for a given chlorophyll
con-centration, whereas spectral ratios
remain practically unaffected. Also, theimpact of the bidirectional character of theocean reflectance is greatly reduced if a ratio
is used. After the atmospheric correction
process, residual errors may persist and are
added to the marine signals at the two
wavelengths. Their impact is considerably
diminished for the ratio.
As an obvious preliminary conclusion, a
couple of wavelengths constitutes theminimum minimorum. The position of theneeded channel in the green region ofminimal absorption is constrained by theexistence of an atmospheric absorption band(due to water vapor, from 567 to 637 nm).
Therefore, depending on the bandwidth, thewavelengths 555 nm (20 nm wide), or 560
nm (10 nm wide) are convenient. The othermandatory channel must be located within
the blue absorption band of algae. Ideally,
the most appropriate channel corresponds
to the phytoplankton absorption maximum,
occurring on average at about 443 nm.
Indeed, the ratio R (443)/R (555) spans a wide
range, and may vary from about 10, down
to 0.3, when chlorophyll varies from 0.02
to 20 mg m-3. An objection has been raised
against this blue wavelength, because of its
great distance from the NIR channels,
rendering the extrapolation, and thus theatmospheric correction, somewhat
problematic. Another objection was thevanishingly small marine signal at 443
nm, for pigment concentrations above

2 mg m-3.
An alternative solution has been
proposed (e.g., see the presently used
SeaWiFS algorithms), in which the above
ratio is replaced by the ratio R (490)/R (555).

Practical aspects: number of channels versus objectives • 19

It is, however, notably less sensitive, as it
varies from about 5 down to 0.5 for the samechlorophyll concentration range as envisaged
above. It is nevertheless believed that thisdisadvantage would be compensated by thebetter accuracy of the atmospheric correction
at the wavelength 490 nm, compared withthat achievable at 443 nm, as well as by amore substantial marine signal at 490 nm,
when the chlorophyll content becomes high.
Admittedly, this question is still open andwill remain so until experience is gained in
the near future (particularly with the
SeaWiFS sensor). Nevertheless, it can benoticed that in the range of low (say, <0.30
mg m -3) chlorophyll concentration (typicalof the open ocean), there is a definite
advantage in using the first colour ratio, as
it decreases by a factor of three when thechlorophyll concentration increases from0.03 up to 0.3 mg m-3, whereas the second
ratio decreases only by a factor of 1.3 overthe same concentration range. A switchingprocedure, from the first to the second ratio,
has already been proposed, to be operated assoon as the signal at 443 nm becomes lower
than that at 490 nm, and such a procedure
seems very reasonable.
Therefore, at this stage, and with theprincipal aim of estimating the chlorophyll
concentration in Case 1 waters, theconclusion could be i) to select the 490-560
nm couple, as a first priority, in conformity
with previous choices, and ii) to advocate
strongly for an additional capability, as
provided by the adoption of the triplet 443490-560 nm.
Experience of the past (CZCS), leads us
to think that with this set of NIR and VISchannels, discriminating between Case 1
waters and turbid-Case 2 waters is feasible,by using a threshold technique applied tothe marine signals, all enhanced in turbid
waters (Bricaud and Morel, 1987). Based on
the reflectance values, a rough estimate of
the sediment load can be made at 555 nm,

for instance. Nonetheless, a more accuratequantification of this load will necessarily
require specific field studies, aiming at
relating the mass concentration to thescattering properties of the local sediments.
Some interferences seem inevitable; highly
loaded waters exhibit a non-zero signal in
the infrared domain and therefore may
induce an overestimate of the aerosol
contribution. The differing spectral behaviorof the scattering by aerosols and by hydrosols
could help in removing ambiguities; othertechniques, based on the “bright pixel”(Moore et al., personal communication,1998) are under development.
Yellow-substance-dominated Case 2
waters cannot be detected with this minimal
set of two or three wavelengths. Generally,
they will be interpreted as being Case 1 waters,with rather high chlorophyll concentration. The
divergence between the optical signatures of algae
and of yellow substances takes place at shorter
wavelengths, when phytoplankton absorption
tends to decrease (between 440 and 380 nm),whereas yellow substance absorption continues toincrease in an exponential fashion. Beside
instrumental limitations, the extreme difficulty of
performing atmospheric corrections in the near
UV has led to the proposal of a “violet” channel at
about 410 nm. Disposing of such a channel
appears to be the unique solution to get
information about yellow substances, at least for a
simple instrument and with simple algorithms
based on reflectance ratio techniques. In sucha perspective, a comparison of ratios likeR (410)/R (555) and R (443)/R (555)(or otherpossible combinations as well) could provide
a clue to identifying zones where yellow
substances becomes the dominant optically
active substance. It must be acknowledged
that, if algorithms have been put forward
tentatively for the detection andquantification of yellow substances, aconvincing demonstration of their efficiency
has not yet been made. This situation should
improve rapidly, thanks to experience to be20 • Minimum Requirements for an Operational, Ocean-Colour Sensor

gained with the new sensors.
As soon as a combination of four visible
channels (410, 443, 490, and 555 nm) canbe accepted, even for a simple instrument,
other approaches, more complex than simple
ratio techniques, are potentially conceivable.
Among them, non-linear inversion or neuralnetwork techniques could be employed. In
principle, the capabilities of such advanced
methods increase when the number ofchannels and spectral information increases,
and thus when the problem becomes moreconstrained (see section 3). But, with morethan 4 channels in the visible part of thespectrum, we would leave the domain of
simple sensors, and enter that ofsophisticated sensors, outside the scope of
this work.
In summary, after having catalogued thechannels as follows:# 0 1024 – 1064 nm width

30 nm(possible width 40 nm)#1855 – 890 nm

width

20 nm

(possible width 35 nm)#2

744 – 757 nm

width

14 nm

#3

704 – 713 nm

width

10 nm

#4

550 – 565 nm

width

10 nm

#5

485 – 495 nm

width

10 nm

#6

438 – 448 nm

width

10 nm

#7

407 – 417 nm

width

10 nm

the following combinations (“Cn”) can beenvisaged:
C1 = Channels

1, 2,

4, 5

C2 = Channels

1, 2,

4, 5, 6

C3 = Channels

1, 2,

4, 5, 6, 7

C4 = Channels

1, 2, 3, 4, 5, 6

C5 = Channels
C6 = Channels
C7 = Channels

0, 1, 2,4, 5, 6

1, 2, 3, 4, 5, 6, 7
0, 1, 2, 3, + n channels inthe visible part of the spectrumwith the following comments:❖ The combination C1 (restricted to 4channels) represents a minimum
minimorum in terms of atmosphericcorrection and of ocean-colour
information, providing a single pigment(“chlorophyll”) estimate within Case 1
waters, and a crude delineation of theturbid-Case 2 waters.
❖ The

combination C2 (5 channels)
provides a better assessment of thechlorophyll concentration over its whole
range, including that of low to very low
concentrations, and a safer discriminationof turbid waters, including a quantitative
information about their reflectance.

❖ The combination C3 (6 channels) has thesame capacity as C2, and, in addition,
implementation of the specific channel
centered on 412 nm can remove
ambiguities between the effect of algal
pigments and that of yellow substance; in
addition, this channel can help in thedetection of absorbing aerosols above open
ocean.
❖ The

combination C4, also with 6
channels, has the same capacity as C2, interms of marine information, but theatmospheric correction could be improved
thanks to the availability of three channels
in the near infra-red domain, allowing
more accurate products to be expected.

❖ The

combination C5, again with 6
channels, differs from C4 only for theatmospheric correction procedure, andcould offer a capacity in distinguishing
between whitecaps and aerosols.

❖ The

combination C6 (7 channels),
combines the advantages of C3 and C4,
namely an improved atmosphericcorrection and a yellow substancedetection capability.

Practical aspects: number of channels versus objectives • 21
❖ The combination C7 (n >7 channels) rep-resents any advanced, sophisticated sensor, with added capacities (cf. section 3),
and thus falls outside the scope of thepresent document.
For the visible channels, the proposed 10
nm bandwidth is desirable to maximise thedifferences between channels 4 to 7, and toincrease the potential for retrieving some
information on algal pigment composition.
Though it is acknowledged that retrieval ofpigment composition by remote sensing has
yet to be demonstrated irrefutably, bandwidths of 10 nm in the visible should nevertheless be seen as a desirable configuration
for future sensors.

22 • Minimum Requirements for an Operational, Ocean-Colour Sensor

5. Other requirements
5.1 Radiometric calibration, solar
calibration, vicarious calibration
As a prerequisite, ocean-colour applications need an accurate radiometriccalibration of the sensor to reach the required
accuracy of the remotely sensed signals. The
CZCS experience, which was affected bysevere malfunctioning of internal calibrationand had to rely most of the time uponvicarious calibration, has drawn attention to
the importance of setting up well-definedand reliable calibration procedures. The
radiometric accuracy requirements set for
SeaWiFS and MODIS, for instance, havebeen (for the visible range) 5% for absolute
radiance values, and 2% for relative values(reflectances). These specifications must be
met for the whole spectral range and thewhole field-of-view of the sensor. These goals
are usually reached by combining internal
calibration procedures and vicarious
calibration based on in situ measurements.
5.1.1 Internal calibration:
To satisfy the high calibration standards
mentioned above, the basic requirement is
to make use of an on-board, in-flightcalibration system. Generally, the newsensors rely on a solar diffuser plate viewing
the sun (instead of internal calibration lamps,
which proved to be unreliable for CZCS).
Such a diffuser, illuminated by the sun,provides a calibration relative to the solar
irradiance. Knowing the solar constant (andits slight variations with the solar cycle), it isthen possible, under the assumption of the
diffuser stability, to monitor a possible driftof the instrument. Solar observations areusually made over the Pole so as to minimize
the loss of oceanic measurements, and canbe as frequent as once per orbit.

Obviously, the reflectance andbidirectional properties of the solar diffuser
have to be characterized precisely during the
pre-launch phase using laboratory calibration
devices. However, as this diffuser ispermanently exposed to sunlight and UV
radiation after launch, it is expected todegrade with time. It is therefore useful, as
it is the case for SeaWiFS, to add anattenuator plate that attenuates the flux
incident on the diffuser plate and limits its
degradation. However, even in this case, itremains necessary to track the actual
degradation of the solar plate.
The concept used for SeaWiFS and
MODIS is to use the surface of the moon asa secondary diffuser, i.e., to compare thesunlight scattered by the surface of the moonwith that scattered by the solar diffuser. The
basic assumption is that the surface of themoon is perfectly stable with time. However,
the reflectance of this surface is not spatiallyhomogeneous, so that the correction
procedure must take into account these
spatial variations and the observationgeometry. With this aim, a database ofradiometric observations of the moon has
been developed for SeaWiFS. The frequencyof lunar observations (once per month at themost for full illumination) must, in practice,
be decided according to the rate ofdegradation of the solar diffuser.
In addition to the lunar observations, the
MODIS instrument controls the behaviorof the diffuser with an additional design, the“solar diffuser stability monitor”. It consistsof a spherical integrating source with nine
filtered detectors, viewing, alternatively,
direct sunlight and the illuminated solar
diffuser, so that changes in the reflectance ofthe diffuser can be monitored at nine

Other requirements • 23

wavelengths between 0.4 µm and 1 µm.
Another solution, developed for MERIS, is
to use two diffuser plates mounted on a“calibration wheel”, the first one
permanently exposed to sun irradiance,
whereas the secondary one, much lessfrequently exposed, is used to monitor the
degradation of the primary diffuser.
Whatever the system adopted, suchmonitoring remains critical to an accurate
calibration of measured radiances.
5.1.2 Vicarious calibration:
Vicarious calibrations consist of comparisons
between water-leaving radiances at the sea
level and those derived from satellite
measurements, after proper atmosphericcorrections. Such vicarious calibrations may
be useful as additional tests to check thefunctioning of the sensor. The difficulty,
however, lies in that not only the sensor
calibration, but also the quality of
atmospheric corrections, is tested using thisapproach. Therefore, vicarious calibrationmust be considered only as an additional
check of the whole system (sensor +atmospheric algorithms) function, with thecalibration of the sensor relying primarily
upon the in-flight diffuser system. The
analysis of differences between vicarious
calibration and on-board calibration may be
useful to determine the sources of errors.
Vicarious calibrations are usually
performed in oceanic sites characterized byweak variability in optical properties, suchas very oligotrophic waters. Even in such acase, spatial and temporal variations of waterleaving radiances have to be taken into
account (particularly at 443 nm, where thevariability of radiance is high at low
chlorophyll content). To minimize errors in
atmospheric corrections, atmosphericproperties (e.g. optical thickness of aerosols,

aerosol phase function, ozone content) havealso to be determined accurately. Therefore,
an extended set of in situ oceanic andatmospheric measurements must be
performed at the time of the satellite
overpass, in the same way as in “validation
experiments” (see Mueller and Austin, 1995).
Such measurements can be performed fromship. The CZCS experience, however, has
shown that the monitoring of the system ismore reliable if it is made at high frequency.
Therefore it is useful to include in such
experiments not only ship measurements,
but also measurements from fixed opticalmoorings providing time-series for adequate
parameters.
In conclusion, whatever the degree ofsimplification that is envisaged for a low-cost
sensor, the high radiometric stability andcontrol capabilities cannot be less than thoseanticipated for the future sophisticatedsensors. In other words, there is no possible
simplification or relaxation of constraints inthis domain.
5.1.3 Noise Equivalent Radiance:
The same conclusion holds true for thenoise-equivalent-radiance in all channels,
including those devoted to the atmosphericcorrection. Studies made in different agencies
and scientific teams lead to specifications,actually very similar, as recommended for the
next generation of sensors (see Figure 1), now
achievable with modern technology. Evenfor simple sensors, such requirements, say
NE∆L below 0.05, or better, below
0.035 W m -2 sr-1 µm-1 in the visible and nearinfrared, and below 0.025 in the NIR, must
be kept. Such noise-equivalent-radiancerequirements, actually met by future
instruments such as MODIS, MERIS and
GLI, must be aimed at for less sophisticatedinstruments as well.

24 • Minimum Requirements for an Operational, Ocean-Colour Sensor

Noise equivalent radiance (W m-2 sr-1 µm-1)

0.25
—
—
—
—
—
—
—

0.2

CZCS (PFT)OCTS (PFT)
SeaWiFS (PFT)MODIS (PFT)MERIS (PFT)GLI (EM)S-GLI (SPC)

0.15

0.1

0.05

0
400

600

500

700

800

Wavelength (nm)Figure 1. Noise equivalent radiance of various satellite ocean-colour sensors

5.2 Dynamic range
The dynamic range for each channel has tobe adjusted in such a way that a maximal
sensitivity is achieved when the sensor ispointed at the ocean. This requirement(optimization for dark targets) for oceancolour observations can be a major factor in
instrument design. One approach taken has
been to allow the sensor to saturate overbright targets, such as clouds or possibly
many terrestrial sites, and instead, toaccommodate only radiances expected overcloudless ocean pixels. This precludes
knowing how bright the brightest pixels
were, if corrections are needed.
As aerosol optical thickness and nature
constitute extremely valuable products, andare, in principle, determined as accurately
as the atmospheric correction has beenachieved, the required dynamic range has tobe specified accordingly. In such aperspective, even if the marine signal isdrowned, and can no longer be extracted

from a too high aerosol signal, the aerosol
signal itself can be successfully exploited.
This possibility exists if the sensor is not
saturating in the presence of significant
aerosol loads. The following table provides,
for the main channels, the radiances thatwould exit an atmosphere with an aerosol
optical thickness (τa= 2, at 550 nm, in ageometry leading to the maximal signal(although outside of the sun glint zone)). In
this calculation the aerosol model is ofmaritime type, with a relative humidity of70%.
This maximal signal is found for a solar
zenith angle of about 41° and when theviewing angle is also about 41° in the solar
plane, so that the backward lobe of theaerosol phase function is directly involvedin forming the (multiply scattered) exiting
radiance. Note that, in such conditions, therelative contribution of the marine signal to
the TOA signal is essentially zero (the ocean
is not “seen” by the sensor), except forOther requirements • 25

oligotrophic waters and in the blue and violet
channels only (where the relative
contribution does not exceed 2%, however).
The following numbers are not intended to
be definitive numbers, only a reasonableestimate of expected radiances in thepresence of high aerosol loads.
Channels (nm)

410 443560 710 750 865

W m-2 sr-1 µm-1

205 189

138

92

82

58

The above values are increased with respect
to the maximum values (to be observed when
the horizontal visibility is 23 km, i.e. in goodviewing conditions), by a factor of
approximately 1.4 (at 410 nm), or of 2.9 (at865 µ m); therefore the impact of
implementing such a monitoring aerosol
capability is more important in the NIR
channels,than for the visible channels. Verylikely, a bi-linear gain (such as used with
SeaWiFS) or an equivalent capability will benecessar y to accommodate such highradiances, without cutting down the highsensitivity required when favorable
atmospheric conditions allow observation of
the ocean. More recently, improved
detectors, and use of multiple gains, or dual
digitization, have enabled bright and darktargets to be accommodated with only aslight sacrifice of performance. This allows
the sensor to be more useful for other earthscience disciplines.

5.3 Sun glint avoidance
The illumination geometry of an Earthscene by the sun is critical for an opticalremote sensor: when the sunlight reflectedfrom the Earth’s surface is too close to themain instrument axis direction, the sensor
can be saturated by the received energy, sothat the signal recorded becomes useless. Thisphenomenon, called sunglint effect, isobserved primarily over water bodies, due
to wave motions on the surface, which mayalign water facets to reflect sunlight directly
toward the sensor and yield an extended
sunglint pattern. The brightness of thesunglint is related to the texture of thesurface, so that its changes correlate withlocal roughness and wave steepness, andultimately with the local wind.
The occurrence of sunglint patterns may
prevent a remote sensor from providing thecomplete coverage of the Earth surface
required. Several solutions have been devised,
to avoid or minimize the loss of data when
the instrument is saturated by sunglint.
Possible mechanisms for glint avoidance
include tilting of the instrument away fromthe direct reflection of sunlight, or in general
the adoption of a multi-angle viewing mode.
However, because of the need to modify thesensor set-up, such mechanisms may rendera certain data loss unavoidable (e.g., whenswitching tilt direction during the equatorcrossings). Other dynamical avoidancemechanisms call for the use of particularscanning (e.g., conical) techniques orspinning of the sensor/satellite system.
Alternatively, a proper choice of orbitalparameters can minimize glint recurrence
and ensure coverage of contaminated areas
within a reasonable time (e.g., a few days).
Finally, the availability of a constellation of
sensors can also provide glint-free scenes,
when at least two instruments can look
simultaneously at the same target fromdifferent angles.

26 • Minimum Requirements for an Operational, Ocean-Colour Sensor

6. Other operational aspects
6.1 Ground resolution, temporal
resolution, coverage
The extent of an area that can be detectedby a given sensor is prescribed by its
instantaneous field of view (IFOV), whereas
both the spatial and temporal coverage arecontrolled by the orbit characteristics of thesatellite, as well as the scanning capability ofthe sensor. In other words, the satellite andsensor properties in terms of ground
resolution and temporal coverage have to be
designed at an early stage of the mission
configuration after agreement on the mission
objectives.
Past, present and expected “low-medium
resolution” ocean-colour sensors on polarorbiting platforms have ground resolution
at nadir ranging from c.a. 250 m to 7 km,
and global coverage from 1 to 3 days (seeTable 3), except for CZCS which was limited
to two hours of operation each day, and
MOS, not systematically operated. The
absence of an on-board recording system
reduces the coverage of MOS to available
receiving stations. Note that thecharacteristics of each sensor have been

selected for the monitoring of a large rangeof surface water phenomena, andapplications in different fields, from global
climate modeling to small-scale operational
management in the coastal areas.
Therefore, any “standardization” orrecommendation that would ensure
continuity of the spatial and temporal
sampling characteristics for future satellitebased ocean-colour sensors may require a
trade-off between different levels of
application and the technical achievement
on board the satellite. As an example, global
coverage at a ground resolution of 300 m istechnically too demanding (high number ofreceiving stations or excessive storage units
on board the satellite), whereas 7 km pixel
resolution (e.g., POLDER) may not besuitable for coastal applications.
As a compromise, recommendations for
future ocean-colour sensors may be as
follows:❖ On-board

storage of 4 km ground
resolution data at global scale;

Table 3. Spatial and temporal coverage of various ocean-colour sensors.
SensorGround resolution(at nadir)

Recurrent periodof satellite

Coverage

CZCS

825 m

6 daysNo global coverage

MOS

520 m

24 days

No global coverage

OCTS

700 m

41 days

3 days global coverage

SeaWiFS

1.13 km

16 days

2 days global coverage

POLDER6 x 7 km

41 days

1 day quasi-global coverage

MODIS

1.0 km16 days

1-2 days global coverage

MERIS

1.2 km / 300 m

35 days

3 days global coverage

GLI

1.0 km / 250 m

4 days3 days global coverageOther operational aspects • 27
❖ Direct downlink of higher resolution data

(1 km) on specific request for regional
studies;❖ Global daily coverage would be justified

for coastal studies and for a sufficient
representation of the cloud-covered areas;❖ Even though the present experience, andthe plans for immediate future, areconfined to the use of polar orbiting
ocean-colour sensors, there are strong
arguments for a deployment of similarsensors from geostationary positions, orfrom low-inclination orbiting platforms,
able to provide a higher frequency
monitoring together with a better coverageof tropical zones (see below).

6.2 Data distribution, accessibility
Data acquisition and archiving are best
accomplished by dedicated, specialized
facilities, usually operated (either directly or
under contract) by Space Agencies. As thishas been the trend for quite some time, acertain degree of standardization, extended
to end-users interested in having direct dataacquisition capabilities, has already beenachieved in this field.
In general, though, users are anticipating
long-term continuity of data as inputs
to scientific and (quasi) operational
applications. There is a need for operational
systems that provide data of proven quality
and accessibility for a number of years. The
experience acquired in this field has shownthat the vast quantities of data generated byremote sensors, and the scientificcomplexities inherent in data processing (inparticular for optical data) to derive
environmental parameters, represent themost serious obstacles to their use. Managing
and exploiting the information potential of
integrated, remotely-sensed data requiresthat substantial efforts be made in thegeneration of value-added information
products, and in their analysis, using specificscientific tools.
The development of specific (quasi) realtime data product lines, and of
corresponding historical time series – as well
as dedicated algorithms and models – insupport of marine environmental research
and applications, are seen as pre-requisites
to ensure data accessibility. Contemporary
data, generated by new sensors, should bescreened, selected, assembled, and banked as
quality-controlled sets of derived parameters,
at processing levels to be defined. The same
tools developed for ingesting new data, as
they become available, should be used to
update the time series of historical data
(possibly coupled to suitable auxiliary data,e.g., atmospheric and meteo-climaticparameters), to enhance the statisticalsignificance of the series or to open new
information lines. These activities should also
be accompanied by the collection and/or
development of data management,
processing and analysis tools, to be madeavailable together with the data setsthemselves. This is to ensure that algorithms
and models (e.g., site or time specific) arealways available for the exploitation of thebanked data.
Suitable data access and distributionmeans, based on electronic publishingtechniques, should also be identified andimplemented. Formats (for data archiving,
retrieval and distribution) represent a criticalissue that should be clarified by thecompetent organizations (e.g., theCommittee on Earth Observation Satellites,
CEOS, ad hoc working group). In general,
it is envisioned that such implementations
should comply with the guidelines identified
and/or established by current research efforts
in this field (e.g., the Center for Earth
Observation, CEO, program at the Joint
Research Center, Ispra, Italy).

28 • Minimum Requirements for an Operational, Ocean-Colour Sensor

6.3 Sensors in Geostationary
Position
Ocean-colour sensors on sun-synchronous,
polar orbiting satellites are capable ofsupplying water-leaving radiances with highspectral and spatial resolution and a revisit
period of approximately two to three days.
This relatively low frequency coverage,
further reduced in the presence of clouds, isinadequate to study oceanic processes that
occur at shorter time scales. Ocean-colour
observations from geostationary platforms
could be used to study processes that require
several ocean-colour observations during asingle day. From a geostationary position, it
can be envisaged that the sensor is aimed at
a restricted zone for a specific monitoring athigh spatial resolution, or as well, has an allembracing view of the planet; this second
option is more adapted for a global
monitoring along the line of the present
document.
Geostationary satellites provide highfrequency observations of the environment
over large geographic regions (high latitude
zones excluded) and permit the resolutionof dynamic processes with time scales ofhours to days. The “fixed view” of theseplatforms offers additional capabilities, suchas providing a consistent viewing geometry
to any given earth location, monitoring
features that can only be detected byinstruments capable of “staring”, andincreasing daily image coverage bycompositing cloud-free areas of individual
images collected during the same day.
Furthermore, augmenting the continuous
ocean-colour observations of geostationary
platforms with the high spectral and spatial
resolution data from polar orbiters will allow
the investigation of oceanic processes notpossible with either platform separately.
Unfortunately, no current geostationary
platform possesses the ability to measureocean colour, nor is it presently considered
to be a requirement by government Agencies.
The current Geostationary Operational
Environmental Satellite (GOES) system,
operated by the National Oceanic andAtmospheric Administration (NOAA),
provides half-hourly observations of Earthemitted and reflected radiation. Each GOES
comprises two instruments: an Imager andSounder. Together, these sensors acquire
high-resolution visible and infrared data, as
well as temperature and moisture profiles of
the atmosphere, from which atmospherictemperature, winds, moisture, and cloud
cover data can be derived. Addition of an
ocean-colour capability to GOES has been
discussed in the past, but with little progress.
We are broaching this issue again, proposingthat ocean-colour capability be added asa requirement for the next generationof geostationary platforms, GOES N-R.
The European meteorological satellite(EUMETSAT) program consists of a seriesof geostationary spacecraft (Meteosat)
continuously operating since 1977. Thesesensors provide images (2500 lines of 5000
pixels) in three spectral bands (thermal
infrared, water vapor channel, and “VIS”);
the latter channel, centered on 0.7 µm,
extends from 0.3 up to 1 µm. India has also
been operating a series of geostationary
satellites (Insat series): the data are not, atpresent, available to the international
community.
Adding ocean-colour bands to such
instruments is technically feasible. For
instance, the GOES Advanced Imager Study
Review (Silver Springs, MD on August 28,1997), in which the results of a conceptual
design study of the advanced Imager basedon present NOAA requirements were
presented, indicated that the sensor had been
under utilized. Under its current
configuration, the sensor could easilyOther operational aspects • 29

incorporate a limited number of oceancolour bands. This sentiment was echoedlater in the review when it was suggested that
ocean colour be added to the baseline
requirements of the sensor. In addition
to its technical feasibility, including
ocean colour as a requirement on GOES,
or Meteosat, must be justified
programmatically.
There are numerous scientific reasons topromote geostationary ocean-colour
observations. It must be kept in mind,
however, that maintaining a rather
demanding noise-equivalent radiance froma geostationary orbit results in a considerable
increase in pixel size, to the extent that (for agiven detector technology) the instantaneous
field of view cannot be reduced considerably.
Therefore the achievable geometricresolution limits the nature and spatial scaleof investigation and monitoring to beenvisaged, whereas the possibility of frequentacquisitions provides unquestionableadvantages. In this context, such sensors
in geostationar y positions can becomplementary of, but in no way substitutefor, sensors in sun-synchronous orbits. These
data will aid in addressing the following
topics:❖ Detecting, monitoring, and predicting
noxious or toxic algal blooms of notable
extension;❖ Initializing

and validating coastal
circulation models, at mesoscale;❖ Assessing the geological and biologicalresponse to storm and other short-term
events (dust or smoke plumes, forinstance);❖ Monitoring biotic and abiotic material intransient surface features, such as extended
river plumes and tidal fronts;❖ Tracking hazardous materials, such as oil

spills (narrow instantaneous field of view
required).
Several NOAA Line Offices and agencies
have stated an interest or requirement forgeostationary ocean colour. The US Navy,for instance, wishes to obtain ocean-colour
imagery at two-hour intervals. The Tropical
Prediction Center of the National Hurricane
Center may be interested in using these data
to improve estimates of hurricane
intensification and predict landfall. Similarinterests were expressed by scientists in othercountries.
If ocean colour monitoring fromgeostationary positions becomes arequirement, various studies will be needed
to weigh tradeoffs and arrive at an acceptable
design. Several potential designs exist. For
instance, measurements could be made withthe main GOES imager with the additionof two or three bands, or with separate focal
planes, but sharing the scan mirror andtelescope. Alternatively, a separate sensor
could be constructed. The former appears
to be the most cost-effective approach.
Minimal specifications of spectral band
placement, width, and signal-to-noise-ratio
necessary to perform ocean-colour
observations for GOES would also have to
be ascertained. Calibration and radiometricstability control are also other issues to beaddressed.
Geostationary ocean colour will not be
available for at least another decade. NOAA
or Eumetsat require approximately 10 years,
from design to launch, to complete ageostationary platform. Little or no chanceexists of adding ocean-colour bands on
GOES-P, which is planned for launch in2007 (launch readiness: 2006). Time isavailable, however, to justify and incorporate30 • Minimum Requirements for an Operational, Ocean-Colour Sensor

an ocean-colour requirement on the next
GOES platform, Q, which is planned forlaunch in 2010 (launch readiness: 2008).
In summary, ocean-colour observations
from a geostationary platform would
complement concurrent observations frompolar orbiters and permit the investigationof dynamic oceanic processes, not presently
possible. Adding an ocean-colour capabilityon the next generation of GOES platforms
appears to be scientifically tenable andtechnically feasible (in the authors' opinion).
Inclusion of this capability as a requirement
and its financial support, however, await
approvals by NOAA and other involved
management agencies.

6.4 Other Orbital Options
Other orbital options exist that can
improve frequency of temporal coverage. The
Taiwanese ROCSAT, for example, will beplaced in a low-inclination orbit much likethat of the Space Shuttle. The result is higher
temporal coverage in the tropics, where
coverage with sun-synchronous polar orbits
is worst. This is potentially valuable, formany of the same reasons given to support
geostationary observations; in particular, thecoverage avoids bias because of diurnal cloudformation and sun glint in tropical regions.
The disadvantage of course, shared to a lesser
degree by the geostationary approach, is lackof coverage in the productive high latitude
regions.
A third approach to achieving higher
temporal coverage, but which preserves thehigh latitude view, is placement of aconstellation of polar-orbiting ocean-colour
sensors, offset in their respective orbital
planes. A constellation of, say, eight
SeaWiFS-class sensors in sun-synchronous
orbits, but with differing equator-crossing
times, would dramatically improve the global
spatial and temporal coverage, assuming thatthe intercalibration issues could be
adequately addressed. The approach towards
low-cost, minimal sensors taken in thisdocument would reduce the costs associatedwith multiple builds of identical sensors,
improve the full global ocean coverage inboth space and time, and provide a viable
alternative to an array of geostationary
observing platforms. International
coordination is highly desirable in view of
optimizing the capabilities of any
multisensor assemblage.

Conclusions • 31

Conclusions
1. It is assumed that global and permanentmonitoring of ocean colour will beconsidered as necessary in the near future,similar to meteorological and otheroceanographic monitoring requirements.
Such an operational ocean-colour program
must be based on a minimum number ofdedicated sensors for a complete andrepetitive coverage. These sensors must be
as simple and cheap as possible, albeit
fulfilling the scientific requirements thatallow the most important properties to be
retrieved accurately, according to the present
state-of-art. The minimum set of properties,
important for biogeochemistry (carbon
cycle), for dynamics of the upper oceanic
layer, and for earth radiative budget(aerosols), are algal biomass distribution andevolution, sediment distribution andtransport, and aerosol optical thickness andnature (above the oceans).
2. A constellation of sensors would be anadequate solution, operated fromgeostationary positions (four to five) toensure a complete longitudinal coverage athigh temporal frequency, and simultaneously
from sun-synchronous polar orbits, withsymmetrical modes (ascending anddescending) to ensure a complete (lowfrequency) coverage within two to three days.
Other configurations, including low latitude
orbiting sensors and appropriate phasing ofpolar orbiting sensors could also beenvisaged.
3. A certain commonality, in terms ofspectral channels, already exists between thepresent and near-future instruments (cf. Fig1); it is rather well represented by the
SeaWiFS instrument (which was actually a
low-cost sensor). Therefore the effort

required to converge toward an identical (andminimal) set of channels does not seem
excessive, and a consensus could be withinreach, including an agreement about
bandwidth.
4. Restricting the aim of such a mission to
the monitoring of a pigment index in Case
1 waters (about 97% of the whole ocean),to sediment detection in coastal
environments, and assessment of aerosols,
leads to the adoption of a minimal set of fivechannels (the combination C2 on page 16),of which two are devoted to the atmosphericcorrection and aerosol monitoring (channels
744-757 and 855-890 nm), and three todetermination of oceanic variables (channels
438-448, 485-495 and 550-565 nm). The
detection of two or three independent
variables requires, in principle, at least fivechannels in the visible part of the spectrum(plus two or three in the near infrared
for the atmospheric correction); thecorresponding algorithms for the retrieval ofthese three optically active substances are notyet consolidated. Therefore a less ambitiousgoal, albeit more realistic in the present stateof knowledge, and sufficient for most of theapplications listed above, leads to a minimal
set of five to six channels (combination C2,
as mentioned above, or better, C3, which
includes an additional channel at 407-417
nm).
5. Even if simplified with respect to thenumber of channels, these sensors cannot be
simplified in terms of several sensitive points
and crucial capabilities such as:❖ the radiometric accuracy, implying highsignal-to-noise ratios (low noise equivalentradiances in all channels, including thosein the near infra-red);32 • Minimum Requirements for an Operational, Ocean-Colour Sensor❖ thecapability of controlling theradiometric stability (in-flight calibration
involving the whole optical arrangement,
and inter-calibration between sensors);❖

an adequate dynamic range, able to accommodate the low oceanic signals in thecase of clear atmosphere, and those, about
twice higher, to be observed in the presence of relatively high aerosol loading.

6. With such an observational system, basedon similar, possibly identical, instruments,

it is conceivable that a common set of basic
algorithms could be adopted, as well as astandardization of the deliverable products.
The data exchange and management, themerging of the end products, and the distribution would be highly facilitated. Before
such an operational system were decided
upon, a convergence of the characteristics ofthe near-future sensors could be sought, as
well as, if still possible, a coordination interms of orbit phasing of these sensors, for
optimized use.

Appendices • 33
APPENDIX 1. Channel positions of various ocean-colour sensors, 1978-2000

350

400

450

500

550

600

650

700

750

800

850

900

950

CZCS
443

520 550

670

750

OCTS
412 443443

490 520

565

670

765

865

490

565

670

763
765

865

POLDER
910

MOS
408 443 485 520

412 443490
510

412 443488

570

615 650 685

750

815

870

SeaWiFS
555

670

531
551

667
678

765

865

MODIS
748

870

MERIS

665
709
681

412.5
490
442.5
510

560

412 443

490
510

555

670

443

490
510

555

670

865

443

490
510

555

670

865

779

870

OCM
765

865

OCI

OSMI

400 443 490
545
380 412 460
520 565

443

620

490

565

490 520565

GLI

625 666 710 749
680

670

865

POLDER-2

763
765

865

910

S-GLI
412 443625

680 710 749

865

672

867

MISR
446

350

400

450

557

500

550

600

650 700
λ (nm)

750

800

850

900

950

34 • Minimum Requirements for an Operational, Ocean-Colour Sensor

APPENDIX II. Detailed information about the radiometric characteristics of various
mmmmmmmm ocean- color sensors.

a) CZCS

ChannelBand Width

Scene Radiancenm

nm

W m-2 sr-1µm-1

1

443

20

54.2

260

0.2085

2

520

20

45

260

0.1731

3

550

20

38.6

233

0.1657

4

670

20

13.4

143

0.0937

5

750

100

10.8

267

0.0404

µm

µm

K

K

11.5

2

270

0.25

6

where

SNR : PFTNE∆L: PFT

Wavelength

W m-2 sr-1 µm -1

NE∆T: PFT

SNR: PFT: Signal to Noise Ratio: Proto Flight Test
NE∆L: PFT: Noise Equivalent Radiance = Scene Radiance/SNR

Ref: Development of the Coastal Zone Colour Scanner for NIMBUS-7 , Volume 2 - Test and Performance Data.
xxxxxxx NASA Final Report F78-11, Rev. A, May 1979

490

520

565

670

765

865

3

4

5

6

7

8

8.525

10.85

12.05

10

11

12

1.3

1.1

0.55

0.33

µm

40

40

20

20

20

20

20

Low

K

K

300

300

300

180

180

200

250

TLW

TST
300

2

2

3

8

12

11

8

10

20

40

60

90

120

130

150

145

W m-2 sr-1 µm-1 W m-2 sr-1 µm-1

High

0.2

0.15

0.15

0.15

1.1

1.1

1.2

1.4

K

NE∆T:PFT
TLW

NE∆L:SPC
TST
K

45

25

25

40

50

40

25

30

Low

450

500

500

500

500

500

500

450

SNR: SPC
High

0.16

0.09

0.07

0.13

K

TST

637

706

1603

988

994

1453

1373

779

SNR:PFTHigh

Ref: OCTS Cal/Val Document Ver. 1, NASDA/EORC May 1995

TST: Standard input Temperature, TLW: Low level input Temperature, NE∆L: PFT: Noise equivalent radiance = Radiance/SNR (High),where High: high level, Low: low level, SNR: SPC: Signal to Noise Ratio: Specification, SNR: PFT: Signal to Noise Ratio: Proto Flight Test

3.715

9

µm

443

2

20

nm

nm

412

Band Width

Wavelength1

Channelb) OCTS

1.07

0.63

0.44

1.34

K

TLW

70

61

54

126

136

120

41

32

Low

0.03140.05670.03740.09110.12070.08950.10920.1861

NE ∆L:PFTHigh

Appendices • 35

36 • Minimum Requirements for an Operational, Ocean-Colour Sensorc) POLDER

ChannelReflectance range
High
Low

WavelengthBand Width

Polarization

nm

nm

1

443

20

no

—

0.05-0.22

2

443

20

yes (3 angles/channels)

0.05-1.1

—

3

490

20

no

—

0.034-0.17

4

565

20

no

—

0.019-0.11

5

670

20

yes (3 angles/channels)

0.1-1.1

0.013-0.25

6

763

10

no

0.07-1.1

0.008-0.25

7

765

40

no

0.07-1.1

0.008-0.25

8

865

40

yes (3 angles/channels)

0.07-1.1

0.008-0.25

9

910

20

no

0.1-1.1

0.007-0.25

Ref : ADEOS Reference Handbook (NASDA)c) POLDER (continued)
NE∆R
Channel Wavelength

Reflectance level High Dynamic Mode

Reflectance level Low Dynamic Mode

nm Reflectance level R

0.05 0.10

0.20

0.50 1.00

0.01

0.02

0.05

0.10

0.20

1

443

443NP

0.19 0.25

0.36

Sat.

Sat.

NA

NA

0.19

0.25

0.36

2

443

443P

0.44 0.53

0.68

1.00 1.38

NA

NA

0.44

0.53

0.68

3

490

490NP

0.15 0.20

0.27

Sat.

Sat.

NA

NA

0.15

0.20

0.27

4

565

565NP

0.10 0.14

Sat.

Sat.

Sat.

NA

0.07

0.10

0.14

Sat.

5

670

670P

0.42 0.51

0.66

0.97 1.30

NA

0.11

0.15

0.20

0.29

6

763

763NP

0.57 0.67

0.84

1.20 1.60

0.13

0.15

0.20

0.26

0.35

7

765

765NP

0.42 0.51

0.66

0.97 1.30

0.09

0.11

0.16

0.21

0.29

8

865

865P

0.42 0.51

0.66

0.97 1.30

0.09

0.11

0.16

0.21

0.29

9

910

910NP

0.52 0.53

0.55

0.61 0.69

0.14

0.16

0.21

0.28

0.38

NP : Non Polarized measurement

Sat. : Saturation

NE∆R: Noise Equivalent Reflectance (PFT) (x 10 3)

P : Polarized measurement

NA :Not applicableAppendices • 37d) SeaWiFS

ChannelWavelengthBand Width

MAX

nm

nm

W m-2 sr-1µm -1

1

412

20

136.3

2

443

20

3

490

4

STR

SNR: SPC

SNR: PFTNE∆L:PFT

91

499

990

0.0919

132.5

84.1

674

1091

0.0771

20

105

65.6

667

1170

0.0561

510

20

90.8

56.4

640

1152

0.0490

5

555

20

74.4

45.7

596

1069

0.0428

6

670

20

42

24.6

442

781

0.0315

7

765

40

30

16.1

455

859

0.0187

8

865

40

21.3

10.9

467

726

0.0150

where

MAX:

Saturation Radiance

STR:

Standard Radiance

SNR:SPC:

Signal to Noise Ratio: Specification

SNR: PFT:

Signal to Noise Ratio: Proto Flight Test

W m-2 sr-1µm-1NE∆L: PFT: Noise Equivalent Radiance = STR/SRN:PFT
Ref: Barnes, R.A., Barnes, W.L., Easias, W.E., and McClain, C.R.: Prelaunch Acceptance Report for the SeaWiFS Radiometer,
NASA, December 1996. SeaWiFS Technical Report Series Volume 22.

38 • Minimum Requirements for an Operational, Ocean-Colour Sensore) MODIS
Channel

SNR: SPC*

NE ∆L

Wavelength

Band
Width

Spectral
Radiance

Primary Usenm

nm

W m-2 sr-1 µm-1

1

645

50

21.8

128

0.1703(250 m)

Land/Cloud

2

858.5

35

24.7

201

0.1229(250 m)

Boundaries

3

469

20

35.3

243

0.1453(500 m)

Land/Cloud Properties

4

555

20

29

228

0.1272(500 m)

5

1240

20

5.4

74

0.0730(500 m)

6

1640

24

7.3

275

0.0265(500 m)

7

2130

50

1

110

0.0091(500 m)

8

412.5

15

44.9

933

0.0484

Ocean colour/Phytoplankton/

9

443

10

41.9

1325

0.0317

Biogeochemistry

10

488

10

32.1

1308

0.0247

11

531

10

27.9

1385

0.0183

12

551

10

21

1114

0.0189

13

667

10

9.5

1163

0.0082

14

678

10

8.7

1265

0.0069

15

748

10

10.2

1077

0.0095

16

869.5

15

6.2

1000

0.0062

17

915

30

10

167

0.0599

18

936

10

3.6

57

0.0632

19

940

50

15

250

0.0600W m-2 sr-1 µm-1

* (Except for channels 8-16, for which SNR - PFT values are provided.)

Atmospheric Water Vapor

Appendices • 39e) MODIS (continued)
Channel Wavelength

Band
Width

Spectral
Radiance

NE∆T

nm

nm

W m-2sr-1µm-1

K

20

3.75

0.18

0.45

0.05

21

3.959

0.6

2.38

2

22

3.959

0.6

0.67

0.07

23

4.05

0.06

0.79

0.07

24

4.465

0.065

0.17

0.25

25

4.515

0.067

0.59

0.25

26

1.375

0.03

6

—

27

6.715

0.36

1.16

0.25

28

7.325

0.3

2.18

0.25

29

8.55

0.3

9.58

0.05

30

9.73

0.3

3.69

0.25

Ozone

31

11.03

0.5

9.55

0.05

Surface/ Cloud Temperature

32

12.02

0.5

8.94

0.05

33

13.335

0.3

4.52

0.25

34

13.635

0.3

3.76

0.25

35

13.935

0.3

3.11

0.25

36

14.235

0.3

2.08

0.35

Ref : Barnes et al. (1998); Esaias et al. (1998)

SNR

Primary Use

Surface/Cloud Temperature

Atmospheric temperature

150

Cirrus Cloud/Water Vapor

Cloud top altitudes/
Atmospheric profiles

40 • Minimum Requirements for an Operational, Ocean-Colour Sensorf) MERIS
Channel

SNR: PFTNE∆L

WavelengthBand Width

*LST

nm

nm

W m-2 sr-1 µm -1

1

412.5

10

47.9

1871

0.0256

2

442.5

10

41.9

1650

0.0254

3

490

10

31.2

1418

0.0220

4

510

10

23.7

1222

0.0194

5

560

10

18.5

1156

0.0160

6

620

10

12.0

863

0.0139

7

665

10

9.2

708

0.01308681.25

7.5

8.3

589

0.0141

9

709

9

6.9

631

0.011110753.75

7.5

11

760

2.5

12

779

14

4.9

628

0.0078

13

870

20

3.2

457

0.0070

14

890

10

15

900

10

* Typical L values (sun zenith angle 60°, visibility 23 km, aerosol of the maritime type, ozone 350 DU, water
vapor 2 g cm-2; the sub-satellite point is at 58.6°N, the day number is 80, the pixel is at the center of the fieldof view, and the chlorophyll concentration is 0.3 mg m-3). The NE∆L are for the reduced spatial resolution
mode (1.2 km x 1.2 km)
Channels 10 and 11 - O2 bands; Channels 14 and 15 - H2O, Vegetation

Appendices • 41g) GLI
Channel WavelengthBand Width

LST
Ocean

LMX
SNR: SPC SNR:EM
Land/AtmosphereW m-2 sr-1µm -1 W m-2sr-1 µm-1

NE∆L:EMW m -2sr-1µm-1

nm

nm

1

380

10

59

—

365

600

779

0.0757

2

400

10

70

139

—

800

1373

0.0510

3

412

10

65

130

—

800

1453

0.0447

4

443

10

54

109

560

800

994

0.0543

5

460

10

54

108

624

800

988

0.0547

6

490

10

43

86

—

800

1603

0.0268

7

520

10

31

64

539

600

706

0.0439

8

545

10

28

56

549

600

637

0.0440

9

565

10

23

47

—

800

1262

0.0182

10

625

10

17

33

—

800

996

0.0171

11

666

10

13

26

—

800

863

0.0151

12

680

10

12

24

—

800

853

0.0141

13

678

10

12

—

438

200

260

0.0462

14

710

10

10

18

—

700

826

0.0121

15

710

10

10

—

311

250

300

0.0333

16

749

10

7

14

—

550

684

0.0102

17

763

8

6

—

350

130

164

0.0366

18

865

20

5

9

—

450

739

0.0068

19

865

10

5

—

304

130

151

0.0331

20

460

70

36

—

624

200

284

0.1268

21

545

50

25

—

549

150

221

0.1131

22

660

60

14

—

150

100

202

0.0693

23

825

110

21

—

257

140

334

0.0629

24

1050

20

8

—

203

300

359

0.0223

25

1135

70

8

—

200

350

366

0.0219

26

1240

20

5.4

—

138

70

355

0.0152

27

1380

40

1.5

—

94

120

149

0.0101

28

1640

200

5

—

69

109

268

0.0187

29

2210

220

1.3

—

30

105

129

0.0101

42 • Minimum Requirements for an Operational, Ocean-Colour Sensorg) GLI (continued)

Channel Wavelength Band Width TST

TLW

NE∆T:SPC
TST

TLW

NE ∆T:EM
TST

TLW

µm

µm

K

K

K

K

K

K

30

3.715

0.33

300

250

0.15

1.8

0.15

1.4100

31

6.7

0.5

300

200

0.1

1.5

0.07

1.1500

32

7.3

0.5

300

200

0.1

1

0.07

0.8500

33

7.5

0.5

300

200

0.1

1

0.07

0.7600

34

8.6

0.5

300

180

0.1

0.5

0.03

0.4900

35

10.8

1

300

180

0.1

0.5

0.06

0.4300

36

12

1

300

180

0.1

0.5

0.09

0.4800

where

LST:

Standard Input Radiance

LMX:

Maximum Radiance

SNR: SPC: Signal to Noise Ratio : Specification
SNR: EM:

Signal to Noise Ratio : Engineering Model Test

TST:Standard Input Temperature

TLW:

Low Level Input Temperature

NE∆L: PFT: Noise equivalent radiance = LST/SNR

Ref:ADEOS-II/GLI CDR document (in Japanese) Fujitsu Lim. January, 1997

Appendices • 43h) S-GLIChannelWavelengthBand Width

LST

LMX

nm

nm

W m-2sr-1µm-1

W m -2sr-1µm-1

SNR: SPC

NE ∆L:SPC

Ocean Colour Imager (OCI)
1

412

10

65

130

800

0.0813

2

443

10

54

109

800

0.0675

3

490

10

43

86

800

0.0538

4

520

20

31

64

600

0.0517

5

565

20

23

47

800

0.0288

6

625

20

17

33

800

0.0213

7

680

20

12

24

1000

0.0120

8

710

20

7

14

1000

0.0070

9

749

20

7

14

800

0.0088

10

865

20

5

9

800

0.0063

Atmospheric and Land Imager (ALI)
1

380

10

60

400

500

0.1200

2

400

10

60

400

500

0.1200

3

443

10

50

600

800

0.0625

4

460

10

50

650

800

0.0625

5

545

20

30

550

600

0.0500

6

678

20

23

450

400

0.0575

7

710

20

20

350

400

0.0500

8

763

8

6

350

400

0.0150

9

865

20

5

304

400

0.0125

10

460

50

40

624

400

0.1000(250 m)

11

545

50

25

549

400

0.0625(250 m)

12

678

50

15

250

400

0.0375(250 m)

13

865

50

20

257

400

0.0500(250 m)

14

940

20

10

200

400

0.0250(250 m)

15

1050

20

8

203

400

0.0200(250 m)44 • Minimum Requirements for an Operational, Ocean-Colour Sensorh) S-GLI (continued)
ChannelWavelengthBand Width

µm

µm

LST

LMX

SNR: SPC

NE∆L:SPC

Infrared Imager (IRI)
1

1.24

0.02

5

138

400

0.0125

2

1.38

0.04

1.5

94

400

0.0038

3

1.64

0.2

5

69

400

0.0125(250 m)

4

2.21

0.1

1.3

30

400

0.0033(250 m)NE∆T:SPC
µm

µm

TST

TLW

TST

TLW

TMX

1

3.7

0.3

300

250

0.1

0.5

340

2

6.7

0.5

300

200

0.1

0.5

340

3

7.3

0.5

300

200

0.1

0.5

340

4

7.4

0.5

300

200

0.1

0.5

340

5

8.6

0.5

300

180

0.1

0.5

340

6

10.8

0.7

300

180

0.1

0.5

340

7

12

0.7

300

180

0.1

0.5

340

where

LST:

Standard Input Radiance

LMX:

Maximum Radiance

SNR: SPC: Signal to Noise Ratio : Specification
TST:

Standard Input Temperature

TLW:

Low Level Input Temperature

NE∆L: PFT: Noise Equivalent Radiance = LST/SNR

Ref: T. Nakajima (Private communication)

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• 49

List of Acronyms and Abbreviations
ALI
CEO
CEOS
CNES
CZCS
DLR
EM
EOS
ESA
EUMESTADFWHM
GLIGLOBECGOESHITRAN
IFOV
IGBP
IOCCG
IRI
IRS
ISRO
JGOFS
KARI
LMX
LST
LOICZ
(Lw )N
MERIS
MISR
MODIS
MOS
NASA
NASDA
NE∆L
NE∆R
NE∆T
NIR
NOAA

Atmospheric and Land Imager
Center for Earth Observation
Committee on Earth Observation Satellites
Centre National d’Etudes Spatiales
Coastal Zone Colour Scanner
German Aerospace Center
Engineering Model Test
Earth Observing System
European Space Agency
European Meteorological Satellite
Full Width at Half Maximum
Global Imager
Global Ocean Ecosystems Dynamics
Geostationary Operational Environmental Satellite
High-resolution Transmission Molecular Absorption Database
Instantaneous Field of View
International Geosphere-Biosphere ProgrammeInternational Ocean Colour Coordinating Group
Infrared Imager
Indian Remote-Sensing Satellite
Indian Space Research Organization
Joint Global Ocean Flux Study
Korean Aerospace Research InstituteMaximum RadianceStandard Input Radiance
Land-Ocean Interactions in the Coastal Zone
Normalized Water-leaving Radiance
Medium Resolution Imaging Spectrometer
Multi-angle Imaging SpectroRadiometer
Moderate Resolution Imaging Spectroradiometer
Modular Optoelectric Scanner
National Aeronautics and Space Administration
National Space Development Agency of Japan
Noise Equivalent Radiance
Noise Equivalent Reflectance
Noise Equivalent Temperature
Near Infrared part of the Spectrum
National Oceanic and Atmospheric Administration

50 • Minimum Requirements for an Operational, Ocean-Colour Sensor

OCI
OCM
OCTS
OSC
OSMI
PFT
POLDERR
SeaWiFS
SNR
SPC
STR
TLW
TOA
TST
UV

Acronyms and Abbreviations

Ocean Colour Imager
Ocean Colour Monitor
Ocean Colour and Temperature Scanner
Orbital Sciences Corporation
Ocean Scanning Multispectral Imager
Proto Flight Test
Polarization and Directionality of the Earth's Reflectances
ReflectancesSea-viewing Wide Field-of-view Sensor
Signal to Noise Ratio
Specification
Standard Radiance
Low Level Input Temperature
Top of the AtmosphereStandard Input Temperature
Ultra Violet