In the IOCCG Report Series:
1. Minimum requirements for an operational ocean-colour sensor for the open ocean
2. Status and plans for satellite ocean-colour missions: considerations for complementary missions
3. Remote sensing of ocean colour in coastal, and other optically-complex, waters (thisvolume)Reports of the International
Ocean-Colour Coordinating GroupAn Aﬃliated Program of the Scientiﬁc Committee on Oceanic Research (SCOR)An Associate Member of the Committee on Earth Observation Satellites (CEOS)

IOCCG Report Number 3, 2000

Remote Sensing of Ocean Colour in
Coastal, and Other Optically-Complex, Waters

Edited by:
Shubha Sathyendranath (Bedford Institute of Oceanography, Canada)

Report of IOCCG workshop held in Ispra, Italy (14-18 June, 1999) with additional contributions from mini-workshops held in Berlin, Germany (20 April, 1999) and Moss Landing, USA (17 May, 1999). Based on contributions from (in alphabetical order) RobertArnone, Marcel Babin, Jean-François Berthon, Robert Bukata, Janet Campbell, Curtiss
Davis, Roland Doerﬀer, Mark Dowell, Howard Gordon, Nicolas Hoepﬀner, Chuanmin Hu,
John Kirk, Motoaki Kishino, Oleg Kopelevich, Harald Krawczyk, Andreas Neumann, John
Parslow, Shubha Sathyendranath, Peter Schlittenhardt and Akihiko Tanaka.

Series Editor: Venetia Stuart

Correct citation for this publication:
IOCCG (2000). Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex,
Waters. Sathyendranath, S. (ed.), Reports of the International Ocean-Colour CoordinatingGroup, No. 3, IOCCG, Dartmouth, Canada.

The International Ocean-Colour Coordinating Group (IOCCG) is an international groupof experts in the ﬁeld of satellite ocean colour, acting as a liaison and communication
channel between users, managers and agencies in the ocean-colour arena.
The IOCCG is sponsored by NASA (National Aeronautics and Space Administration),
NASDA (National Space Development Agency of Japan), ESA (European Space Agency),
CNES (Centre National d’Etudes Spatiales), JRC (Joint Research Centre, EC), IOC (Intergovernmental Oceanographic Commission) and SCOR (Scientiﬁc Committee on Oceanic
Research).

http://www.ioccg.org

Published by the International Ocean-Colour Coordinating Group,
P. O. Box 1006, Dartmouth, Nova Scotia, B2Y 4A2, Canada.
ISSN: 1098-6030 ISBN: 978-1-896246-54-3c
IOCCG
2000

Printed by MacNab Print, Dartmouth, Canada.

Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1. General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1 Passive Remote Sensing of Ocean Colour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Case 1 and Case 2 Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3 Utility of the Classiﬁcation Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4 Complexity of Case 2 Waters Relative to Case 1 Waters . . . . . . . . . . . . . . . . . . . . . . . . 17
1.5 Need for New Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.6 Advantages of New Ocean-Colour Sensors over the CZCS in Case 2 Waters . . . . 19
1.7 Need for a Fresh Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
2. Colour of Case 2 Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Ocean Colour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Inherent and Apparent Optical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Ocean-Colour Models for Deep Waters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30
2.5 Inﬂuence of the Bottom on Ocean Colour. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33
2.6 Relationship between Bulk Optical Properties and Water Constituents . . . . . . . . 34
2.7 Inherent Optical Properties of Aquatic Substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.8 Properties of Inelastic Scattering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44
2.9 Issues Related to Atmospheric Correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3. Algorithms for Case 2 Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Desired Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3 Empirical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4 Model-based Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 Bathymetry and Bottom Characterisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73
3.6 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4. Measurement Requirements for Case 2 Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Remote-Sensing System Design Requirements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78
4.3 In situ Data for Model Parameterization and Validation . . . . . . . . . . . . . . . . . . . . . . . 83
4.4 Validation Procedures and Error Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5. Case 2 Ocean-Colour Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.2 Case 2 Ocean-Colour Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.3 Applications by Issue or Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.4 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6. Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.1 Applications of Results in Case 1 Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.2 Can we have a Single Case 2 Algorithm Applicable Globally? . . . . . . . . . . . . . . . . . 116
6.3 How well do we Understand Regional Variations in the Inherent Optical
Properties of In-Water Constituents that Contribute to Ocean Colour? . . . . . . 117
6.4 What Accuracy and Precision can we Expect for Quantities Retrieved
from Remotely-Sensed Data on Ocean-Colour in Case 2 Waters? . . . . . . . . . . . . . 118
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .119
Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Mathematical Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Appendix: Participants at Case 2 Workshops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

1

Executive Summary

Remote sensing of ocean colour is going through a quiet revolution. This IOCCG reportaims to bring the news of these developments to a broader community.
To understand the magnitude and scope of the revolution, we have to go back to thegenesis of ocean-colour remote sensing from space. The Coastal Zone Color Scanner(CZCS), the ﬁrst satellite sensor to monitor ocean colour, was launched by NASA in 1978.
At that time, the goals of the mission were modest: to measure water-leaving radianceat a limited number of wavebands in the visible domain, and then to use the signal toinfer concentrations of phytoplankton pigments in the near-surface layers of the ocean.
The algorithms in routine use made the assumption that the atmospheric and oceaniccomponents of the total signal reaching the satellite sensor could be decoupled using
theoretical models of radiative transfer in the atmosphere. The signals corrected foratmospheric noise were then used in empirical algorithms to retrieve concentrations ofphytoplankton pigments. Notwithstanding the name of the sensor, it was understood andaccepted that the methods in use might not work in coastal, and other optically-complex
waters, where the presence of other substances might mask or modify the signal fromphytoplankton. The quantitative applications of the retrieved data were therefore largely
conﬁned to open-ocean waters.
All these ground rules are changing now. Building on the CZCS experience, and learning from theoretical studies and observations from in situ platforms and aircraft, thescope and requirements of remote sensing of ocean colour from space have grown dramatically over the years. As we understood more about the optical properties of aquatic
substances and their inﬂuences on ocean colour, it became possible to envisage the useof ocean-colour data to retrieve information on substances other than phytoplankton, oreven to distinguish between some types of phytoplankton under favourable conditions.
Exploring these possibilities required sensors with better spectral resolution, improved
calibration, and higher signal-to-noise ratio than the CZCS. The ocean-colour sensors that
followed the CZCS were therefore not replicas, but a diﬀerent breed of instruments with
diﬀerent capabilities. New algorithms have emerged in parallel, to address the new challenges. These algorithms are also a new breed, and not minor variants of old algorithms.
For example, there has been a move towards treating the ocean-atmosphere system as acoupled system, and solving simultaneously for oceanic and atmospheric properties; theempirical algorithms for retrieval of aquatic properties are being abandoned in favourof algorithms that are soundly based on theoretical considerations; and new and powerful mathematical and statistical approaches capable of dealing with a multi-variable,
non-linear system are being brought to bear on the problem.

2• Ocean Colour in Optically-Complex Waters

Most of these developments have focused on improving remote sensing of ocean
colour in coastal, and other optically-complex, bodies of water, and this report has thesame focus. The report attempts to review the progress made so far in this ﬁeld, to evaluate the limitations that exist, and to identify promising avenues for further improvement. The report has an introductory chapter that provides the general background to
the problem, and a second chapter that outlines the theoretical concept of ocean colour.
The third chapter focuses on the various approaches that have been used to address
the problem of quantitative remote sensing in optically-complex waters and the fourth
chapter discusses the requirements for successful remote-sensing experiments in thesewaters. The major, potential applications of ocean-colour data are reviewed in the ﬁfth
chapter. The report then closes with a summary and conclusions. The contributors tothis report have attempted to make the report accessible and useful for the specialist
and the non-specialist alike.

3

PrefaceThis work belongs to a series of reports commissioned by the International OceanColour Coordinating Group (IOCCG) on issues facing the ocean-colour community. The
ﬁrst of these reports dealt with the choice of a minimum set of common wavebands
for ocean-colour sensors (IOCCG, 1998). The second report in the series dealt with thecomplementarity of satellite ocean-colour missions (IOCCG, 1999). This report, which is
the third in the series, focuses on remote sensing of optically-complex waters.
The subject matter under discussion here is a diﬃcult one, and an area of intense
investigation. Therefore, in preparing this report on remote sensing of ocean colour incoastal, and other optically-complex, waters, the IOCCG gathered information from alarge pool of experts actively involved in ocean-colour research. The essential elementsof the report emerged from a ﬁve-day workshop in June 1999 that was sponsored by the
IOCCG and hosted by Peter Schlittenhardt and colleagues at the Joint Research Centre(JRC) in Ispra, Italy. The Ispra workshop was preceded by two other mini-workshops on
Case 2 waters: one in Berlin (April 1999) and the other in Moss Landing, California (May1999). The Berlin meeting was held at the German Aerospace Centre. The California
meeting was held at the Marine Biological Research Institute at Moss Landing and wassponsored by National Aeronautical and Space Administration (NASA). These mini workshops were used as vehicles to sound a larger pool of experts than those who were able
to attend the workshop in Ispra. Janet Campbell and Peter Schlittenhardt took the leadin organising these workshops (see Appendix 1 for a list of workshop participants).
The Ispra workshop began with a formal session at which participants presented papers on various aspects of remote sensing in Case 2 waters. After this, the participants
split into working groups, each group taking responsibility for writing the ﬁrst draft of achapter in this report. IOCCG members and outside experts commented on, and provided additional inputs to, a ﬁrst draft. The principal contributors are identiﬁed as authorsof individual chapters. We wish to acknowledge all who provided helpful comments, inparticular Trevor Platt, André Morel, Tom Malone and Zong Ping Lee. The IOCCG and
the editors of the report wish to thank all the experts who contributed, either by airing
their ideas at the workshops or by participating in the writing of the report. S.S. wishes
to acknowledge, with thanks, the considerable input made by the series editor, V.S.
The editor and authors have tried to do justice to the many approaches that arefavoured by experts who study Case 2 waters, and to present opposing points of view
when they exist. If we have failed in any way to achieve these goals, we apologise. The
opinions expressed in this report are those of the authors, and do not reﬂect the policyof any Space Agencies.

4• Ocean Colour in Optically-Complex Waters

The report is aimed at a wide audience, ranging from the graduate student and theworking scientist to policy makers and managers. In the interests of clarity and brevity,
we have tried to highlight general principles rather than speciﬁc applications. Thosewishing to delve deeper into the ﬁeld will ﬁnd a detailed bibliography at the end of thereport, which we hope is representative, if not exhaustive.

General Introduction•

5

Chapter1
General Introduction
S. Sathyendranath

This report deals with issues related to remote sensing of optically-complex waters
such as are frequently encountered, for example, in coastal regions and in lakes. Water
masses of this kind are often designated as “Case 2” waters. The nomenclature arises
from a commonly-used method of classifying oceanic (or fresh) waters into one of two
types: Case 1 or Case 2. It is generally recognized that Case 2 waters are more complex than Case 1 waters in their composition and optical properties. Interpretation ofan optical signal from Case 2 waters can therefore be rather diﬃcult. To date, remotesensing of ocean colour has focussed largely on the relatively-simple Case 1 waters, and
it is well recognized that the standard algorithms in use today for chlorophyll retrieval
from satellite data break down in Case 2 waters.
At least to some extent, the limitations of the algorithms that were developed initiallyfor interpretation of ocean-colour data reﬂected limitations of the satellite technology
itself: the ﬁrst ocean-colour sensor to be launched on a satellite, the Coastal Zone Color
Scanner (CZCS), had only a small number of wavebands. The CZCS years and the postCZCS years have taught us that remote sensing of ocean colour is much more complex
than was conceived initially, and the new generation of ocean-colour sensors has been
designed with improved capabilities (with respect to both spectral and radiometric resolution). With the advent of the new sensors, the prospects of better algorithms for Case
2 waters have improved vastly. These technological advances have been matched by scientiﬁc endeavours to improve the interpretation of ocean colour in Case 2 waters. The
objectives of the report are ﬁve-fold:❖ to examine how various substances and optical processes inﬂuence the colour of Case
2 waters;❖ to review progress in developing algorithms for remote sensing of Case 2 waters andidentify residual problems;❖ to identify the requirements of successful ocean-colour missions in Case 2 waters,
bearing in mind the need to complement remote observations with in situ experiments
whenever the situation demands it;6• Ocean Colour in Optically-Complex Waters❖ to summarise the potential applications; and❖ to identify issues that merit further action.
Background material relevant to the discussion is presented in this introductory chapterof the report. Before we examine in detail the concept of Case 1 and Case 2 waters, we
begin with a brief introduction to remote sensing of ocean colour.

1.1 Passive Remote Sensing of Ocean Colour
A passive system for remote sensing of ocean colour makes use of a sensor with anarrow ﬁeld of view, capable of monitoring the radiometric ﬂux reaching the sensor atseveral selected wavelengths in the visible and near-infrared domains of the electromagnetic spectrum. The sensor, mounted on a satellite, an aircraft or some other remoteplatform, is aimed at a point on the surface of the earth. Scanning devices on the sensor,
and the movement of the platform itself combine to acquire data from diﬀerent points
on the earth. The sensors operate during the daylight hours, and the ultimate source ofthe light reaching the sensor is the sun. However, the photons from the sun can follow
diﬀerent pathways before they reach the remote detector (Fig. 1.1). We can think of thefollowing main contributors to the remotely-sensed signal:❖ light reaching the sensor after scattering of photons by the atmosphere;❖ light reaching the sensor after specular reﬂection of direct sunlight at the sea surface;and❖ light upwelling from the sea surface after back-scattering in water. Note that thelight upwelling from the sea surface would be attenuated on its journey from the sea
surface to the sensor, due to absorption and scattering by the intervening atmosphere.
It is only the upwelling light from the sea surface that carries any useful information on
the water body. The atmospheric contributions and specular reﬂection at the sea surface
constitute noise in this context, and have to be corrected for. Often, satellite sensors for
ocean colour have the capability to orient the detector to avoid specular reﬂection fromthe sun, but there are no ways to prevent some of the ﬂux scattered by the atmospherefrom reaching the sensor. In fact, at the altitude of a satellite sensor, more than 80%of the light reaching the detector may have an atmospheric origin (Morel, 1980), andsmall errors in estimating the atmospheric contribution can cause signiﬁcant biases inthe estimation of the water component. Furthermore, the upwelling component fromthe water has to be processed to evaluate what the water-leaving component would havebeen, if there were no atmosphere in between the sensor and the water body. Techniques

General Introduction

Figure 1.1•

7

Pathways of light reaching the remote sensor. Adapted from Sathyendranath(1986). (a) Light scattered by atmosphere. Multiple scattering is possible. It is also possiblethat some of the light that reaches the sensor is reﬂected at the sea surface before or after
atmospheric scattering. (b) Specular reﬂection of direct sunlight at the sea surface. (c) Upwelling
light leaving the water surface and travelling in the direction of the sensor. This is the component
that carries useful information about the water body and is examined in more detail in Figure
1.2. Note that the ﬂux upwelling from within the sea is modiﬁed by the intervening atmosphere.
The geometry of the sensor, its altitude and its viewing angle determine the instantaneous ﬁeldof view (IFOV) or pixel size of the sensor on the surface of the water body.

8• Ocean Colour in Optically-Complex Waters

for atmospheric correction therefore form a very important component of remote sensingof ocean colour (Gordon and Morel, 1983; Gordon, 1993; Stürm, 1993; Gordon and Wang,
1994; Gordon, 1997). However, this report focuses on problems related to opticallycomplex water bodies, and issues related to atmospheric correction are mentioned, only
in so far as they are speciﬁc to Case 2 waters.
Examining the water-leaving signal in some more detail, we see that several factors
inﬂuence this signal (Fig. 1.2). Direct sun light and scattered sky light that penetrate
the sea surface may be absorbed or scattered by the water molecules, or by the various suspended and dissolved materials present in the water. In shallow, clear waters, asigniﬁcant part of the light from the sun may reach the bottom, and be reﬂected from
it. Some of the scattered and reﬂected photons eventually ﬁnd their way to the remotesensor. Remote sensing involves analyses of the variations in magnitude and spectral
quality of the water-leaving radiation to derive quantitative information on the type of
substances present in the water and their concentrations. Clearly, this has to be basedon a sound understanding of the optical properties of the medium, and of the opticalprocesses in the medium.
When we analyse the optical properties of this medium, we need to distinguish theeﬀect of water itself on the light ﬁeld from the eﬀects of the dissolved and suspended
matter that are present in the water. It is customary, in this context, to use the term purewater to indicate a hypothetical medium devoid of substances other than water molecules
themselves and the inorganic salts dissolved in the water. When necessary, we distinguishbetween pure freshwater (with no dissolved salts), and pure seawater (with dissolved
salts typically found in oceanic waters). It is recognised that the optical properties ofnatural bodies of water are inﬂuenced by several substances. From a practical, optical,
perspective, we can recognise three main components, in addition to pure water itself:❖ Phytoplankton. This component is taken to include phytoplankton and other microscopic organisms. But for convenience, we call this the “phytoplankton” component,
in recognition of their major inﬂuence on optical properties.
❖ Suspended material (inorganic). Even though microscopic organisms are also “suspended” material, we use this term here to represent only suspended material of
inorganic nature.
❖ Yellow substances. These are coloured, dissolved, organic substances. We take thiscomponent to also include “detrital” particulate material, which generally has absorption characteristics similar to yellow substances.

General Introduction

Figure 1.2•

9Factors that inﬂuence upwelling light leaving the sea surface. (a) upward scatter-ing by inorganic suspended material; (b) upward scattering from water molecules; (c) absorption
by the yellow-substances component (Yellow substances, per se, are not considered to be important scatterers. However, in this report we have included the detrital component with theyellow-substance component, and detritus may scatter.); (d) reﬂection oﬀ the bottom; and (e)upward scattering from the phytoplankton component. Note that absorption by any of thesecomponents or by the bottom will serve to decrease the water-leaving signal. Light from the sun
may be scattered by atmospheric constituents before it reaches the sea surface. Similarly, lightleaving the water may be scattered away from, or towards the remote sensor by the atmosphere.

It is important to recognise that this somewhat unorthodox partitioning is adopted forconvenience from an optical point of view. The nomenclature identiﬁes only the major
components in each category, but each category includes other minor players. One canthink of other ways for partitioning, which would involve assigning independent categories to some of the minor players, or regrouping the components diﬀerently. But theimportant thing is to be consistent, and to ensure that nothing that plays an opticallysigniﬁcant role is left out, and nothing is accounted for twice. Some salient features ofeach of the three main categories are examined below.
The Phytoplankton Component: These are ubiquitous, microscopic, free-ﬂoating organisms found in the illuminated surface layers of the ocean. They are the single-celledplants that form the base of the aquatic food web, and they are an important componentof the global carbon cycle. According to current estimates, the global carbon ﬁxation by10 • Ocean Colour in Optically-Complex Waters
oceanic phytoplankton roughly matches terrestrial carbon ﬁxation over an annual scale.
Thousands of phytoplankton species, with characteristic sizes, shapes and physiologicalproperties, are known to exist in the aquatic environment, and their species compositionand concentration can change with time and space. The concentration of the main phytoplankton pigment, chlorophyll-a, is often taken as an index of phytoplankton biomass.
However, it is important to recognise that chlorophyll-a is accompanied by a number ofauxiliary pigments in the phytoplankton cells (pigment analyses using High Performance
Liquid Chromatography, HPLC, technique show the presence of ﬁfteen or so pigments
in a typical phytoplankton sample from the marine environment). The pigment composition of a water sample can vary with the community structure of the phytoplankton
population in the sample, as well as with the physiological state of the cells (e.g., photoadaptation and nutritional status).
If a single group of substances were to be identiﬁed as being the most important
agent responsible for variations in the optical properties of aquatic environments, thenit would be phytoplankton. But even in the most pristine of aquatic environments, theseorganisms co-exist with other microscopic organisms such as zooplankton, heterotrophic bacteria and viruses. Non-living degradation products of these organisms would also
be present in the milieu as detritus. For practical experimental reasons when we quantify
the optical properties of phytoplankton from natural aquatic environments, the contributions from such other microscopic particles are often not distinguished from those ofphytoplankton. For example, the size spectra of several of these organisms overlap, making it diﬃcult, if not impossible, to separate them by ﬁltration. Furthermore, in analysesof optical data from the natural environment, it is often problematic to distinguish thephytoplankton signal from those of other substances that covary with it. Therefore, in
the remote-sensing context, the “phytoplankton component” incorporates other microscopic organisms as well, unless otherwise stated. This is in recognition of the fact that,from an optical point of view, the highly-pigmented phytoplankton typically dominate
the signal from microscopic organisms. Organic detrital particles are not included in thiscategory.
1.1.1 Suspended material
In this somewhat loose category we include all inorganic particulate material that is
not included in the phytoplankton component. In shallow coastal and inland water bodies, wave and current action can bring bottom sediments into suspension, modifying
signiﬁcantly the colour of the oceans. Muddy rivers and estuaries, oceanic waters inﬂuenced by the outﬂow of rivers, and areas of large tidal excursions are examples of regions
where one might anticipate particulate material in suspension to play an important rolein determining the optical properties of the water. Unlike that of the phytoplankton
component, their inﬂuence is conﬁned typically to certain coastal and inland water bodies. It is important to recognise that the term “suspended material” does not apply toa single type of material, but to a whole family of materials with their own individualGeneral Introduction•

11

characteristics. For example, the reﬂective white sands of a coral beach, when brought
into suspension, will have a very diﬀerent inﬂuence on water colour from, say, red clay insuspension in a river outﬂow. They may also include suspended particles of other origin,such as continental dust deposited on the water by winds or volcanic deposits.
1.1.2 Yellow substances
Yellow substances, variously called “gelbstoﬀ”, “coloured, dissolved organic matter”(CDOM) or “gilvin”, are a group of organic, dissolved substances, consisting of humic andfulvic acids. They may have a local origin, for example from the degradation of phytoplankton cells and other organic particles, or they may be advected to a locality from a
distant source. For example, rivers that ﬂow through heavily-wooded regions and overorganic-rich soils accumulate a load of yellow substances along their ﬂow path. Localities
where yellow substances from distant sources accumulate may have much higher concentrations than regions where only locally-derived yellow substances are present. Yellow
substances are known to undergo photodegradation, such that locally-derived yellow
substances are likely to accumulate more at depth than in the surface layers of water
bodies. The absorption properties of yellow substances are also known to be somewhat
variable.
As we shall see in some detail in Chapter 2, absorption spectra of the so-called nonpigmented, “detrital” particulate material resemble the absorption spectra of yellow substances very closely. Hence, for practical reasons in remote sensing, the detrital component is often combined with the yellow-substances component. We follow this practice
here, even though there are arguments for including it in the phytoplankton component
or the suspended-material component.
1.1.3 Bottom eﬀect
In addition to these three types of substances present in the water column, lightreﬂected oﬀ the bottom of a water body can also inﬂuence ocean colour, provided thewater is suﬃciently shallow, and the water suﬃciently clear. Again, the inﬂuence of thebottom on the colour of water can vary with the depth of the water body, the clarity of thewater, the type of substances present in the water, and the type of bottom. The bottom
may be rocky or sandy, and may or may not be covered, partially or fully, by a variety ofbenthic organisms (e.g., algae, molluscs). All of these factors will inﬂuence the mannerin which bottom eﬀects are manifested in the colour of the water, as seen by a remotesensor.
It is recognized that these three categories of substances listed above, and perhaps thebottom characteristics, can inﬂuence ocean colour. However, the most well-establisheduse of ocean-colour data is in the study of phytoplankton distribution in oceanic waters.
Typically, algorithms for interpretation of satellite data are constructed by calibrating12 • Ocean Colour in Optically-Complex Waterschanges in ocean colour against changes in the concentration of chlorophyll-a in the surface layers of the ocean. Simple algorithms designed for such applications function best
if substances other than phytoplankton are either insigniﬁcant, or correlated with phytoplankton. Fortunately, large tracts of oceanic waters and even of inland water bodies
satisfy one of these criteria. As a consequence, simple algorithms have been remarkably
successful in chlorophyll retrieval from satellite data. However, we also recognise thatsuch algorithms have a higher probability of failure in waters in which particulate matter(other than the phytoplankton component) and yellow substances or bottom eﬀects exert
an important inﬂuence. Furthermore, it would be useful to know how, when and wherethese algorithms may fail, and how we can improve the performance of ocean-colour algorithms, and extend their domain of applicability. It is in this context that the conceptof Case 1 and Case 2 waters becomes useful.

1.2 Case 1 and Case 2 Waters
A bipartite classiﬁcation scheme, according to which oceanic waters are partitioned
into Case 1 or Case 2 waters, was introduced by Morel and Prieur (1977), and reﬁned later
by Gordon and Morel (1983) (see also Sathyendranath and Morel, 1983). By deﬁnition,
Case 1 waters are those waters in which phytoplankton (with their accompanying andcovarying retinue of material of biological origin) are the principal agents responsible
for variations in optical properties of the water. On the other hand, Case 2 waters areinﬂuenced not just by phytoplankton and related particles, but also by other substances,that vary independently of phytoplankton, notably inorganic particles in suspension andyellow substances.
This classiﬁcation scheme does not preclude the possibility that substances otherthan phytoplankton may contribute to the optical properties of Case 1 waters. In fact, it
is recognised (Sathyendranath and Morel, 1983) that a number of substances, including
biological debris generated by grazing and natural decay of phytoplankton organisms,
and dissolved organic matter (yellow substances) liberated from biological particles, can
all contribute to the optical properties of Case 1 waters, especially by modifying theirabsorption characteristics. Recent years have also seen an increasing awareness that
small organisms other than phytoplankton, such as ﬂagellates, heterotrophic bacteria
and viruses, which co-exist with phytoplankton, play an important role in determiningsome optical properties of Case 1 waters (Morel and Ahn, 1991; Stramski and Kiefer,
1991; Ulloa et al., 1992). Thus, the idea is not that phytoplankton are the only agents
responsible for the colour of Case 1 waters. Instead, it is merely stipulated that thecontribution from other substances, if present, is relatively small in Case 1 waters, andcan be modelled as a function of phytoplankton concentration. On the other hand, inCase 2 waters, other particulate matter or yellow substances (or both these types ofsubstances) may make a signiﬁcant contribution to the optical properties. Furthermore,General Introduction•

13

these contributions are not linked to the concentration of phytoplankton; instead, they
have to be treated as independent variables.
A pictorial representation of the two Cases is presented in Fig. 1.3 (adapted from
Prieur and Sathyendranath 1981; see also Morel and Antoine, 1997; Dowell, 1998). The
idea is to take an optical property of interest (say absorption or reﬂectance) at a certain
wavelength, and at a particular location and time, and compute the fractional contributions due to phytoplankton (P ), yellow substances (Y ) and suspended material (S). The
sample can then be characterised optically by a single point on a triangular plot, in which
the axes are the fractional contributions due to each of the three components (an example of the use of the triangular diagram for representing a sample is shown in Fig. 1.4). If
the contribution due to any one of the components dominates the signal, then the point
representing the sample will move towards the corresponding apex (see Fig. 1.4). On theother hand, if the contributions from the three types of substances are more equivalent,
then the point will move towards the centre of the equilateral triangle. It is clear from
Fig. 1.3 that, in the domain of all possible cases, several distinct types can be identiﬁed,
as listed below:❖ Cases where only one component (P , S or Y ) dominates. Waters belonging to thesecases would fall into one of the three smaller triangles in Fig. 1.3 demarcating areas
close to the three apices. Within these smaller triangles, one of the components would
contribute in excess of, say, two-thirds of the total.
❖ Cases where contributions from any two of the substances dominate, while the third
component plays a minor role. These cases are represented by the three trapezoids
that lie along the three axes.
❖ Cases where all the components (P , S and Y ) play important roles. This case is represented by the central, inner triangle in Fig. 1.3. In principle, this would be the mostcomplex water type, from an optical point of view.
Case 1 waters represent the phytoplankton-dominant cases, whereas Case 2 waters represent all other possible cases (Fig. 1.3). Note that the classiﬁcation is based on therelative contributions of the three types of substances, and does not depend on themagnitude of the individual contributions. For example, Case 1 waters can range fromoligotrophic (phytoplankton-poor) to eutrophic (phytoplankton-rich) conditions, provided the contributions from the other components remain relatively small. Note also thatthe contribution from pure water, which forms a constant background optical property,
does not enter into the classiﬁcation; only the three variable components are relevant.
It is worthwhile to reiterate that the location of a sample on the graph would dependon the wavelength used. When multi-wavelength applications are envisaged, it would be
prudent to ensure that the classiﬁcation holds for all wavelengths used in the application.
Another point to bear in mind is that the triangular representation of Figures 1.3 and 1.4

14 • Ocean Colour in Optically-Complex Waters

Figure 1.3

Diagrammatic representation of Case 1 and Case 2 waters, adapted from Prieur

and Sathyendranath (1981) (see also Morel and Antoine, 1997, and Dowell, 1998).

is only valid for optically-deep water columns. When the optical depth of the water issmall, we also have to bear in mind that the bottom of the water body can also exert animportant inﬂuence on the water-leaving signal.
We have used Fig. 1.3 to go from a qualitative deﬁnition of Case 1 and Case 2 waters
to a more quantitative deﬁnition. Yet, it has to be stressed that the classiﬁcation, by itsvery nature, cannot be implemented in a very strict quantitative fashion. For example,
it is clear from Fig. 1.3 that even in the upper triangle designated as Case 1 waters,
there is room for some small variability in sediments and yellow substances, relative
to phytoplankton concentration. The value of this classiﬁcation scheme lies most in
its use as a simple device to diﬀerentiate waters where phytoplankton-related signals
dominate the signal from more optically-complex water bodies where such simplifying
assumptions would not hold.

General Introduction

Figure 1.4•

15

An illustration of the triangular diagram (also known as the trilinear graph) in use to classify

waters. The classiﬁcation is based on the relative contributions to an optical property from three components: phytoplankton, yellow substances and suspended material. The optical properties of pure water
itself do not inﬂuence the classiﬁcation. Let us say that we wish to classify the waters based on the absorption coeﬃcient at 440 nm (Prieur and Sathyendranath, 1981). The scale of the diagram is then selected suchthat the altitude of each of the apices from the opposite base represents 100% absorption by an individualcomponent. That is to say, the total height of apex P from base YS should represent 100% absorption by thephytoplankton component at 440 nm. Similar rules apply for the other two components. Furthermore, let
us assume that a Station X is characterised by the following values for absorption at 440 nm for each of the
major constituents: the absorption by yellow substances, ay (440) = 0.7m−1 ; absorption by phytoplankton,
ap (440) = 0.2m−1 ; and absorption by suspended material, as (440) = 0.1m−1 . Then the proportional contribution from each of these constituents would be 70, 20 and 10%, for yellow substances, phytoplankton andsuspended material respectively. Station X will then be represented on the triangle by point X, such thatthe yellow drop line through X drawn parallel to the side PS intersects the yellow-substances axis at 70%;
the green drop line parallel to YS intersects the phytoplankton axis at 20%; and the red drop line indicates
10% on the axis representing suspended material. Note that another station having diﬀerent magnitudes ofabsorption for the individual components, but maintaining the same proportions of absorption as station
X, would appear superimposed on Station X in the triangular ﬁgure, since this representation is based onrelative values of absorption rather than the absolute magnitudes. Station X appears close to the apex Y,
which shows that X is a yellow-substance-dominated Case 2 water. If the values of the component absorption coeﬃcients were such that the station appeared close to apex S, then we would classify it as belonging
to Case 2 waters dominated by suspended material. Note also that all Case 1 waters would appear close to
the apex P. More information on the use and interpretation of trilinear graphs can be found in Harris (1996).

16 • Ocean Colour in Optically-Complex Waters
1.3 Utility of the Classiﬁcation Scheme
Simple as the distinction between Case 1 and Case 2 waters is, it has some importantconsequences, from the point of view of modelling and interpretation of optical data.
For example, the classiﬁcation implies that all the optical properties of Case 1 waters
can be modelled as a function of chlorophyll-a concentration. This follows from theassumptions that phytoplankton and covarying substances determine the optical properties in Case 1 waters, and that chlorophyll-a can be taken as an index of phytoplankton
concentration. The single-variable approach to modelling also implies that the opticalproperties at any wavelength can be predicted if we know the value of an optical property at any other wavelength, and that these relationships are universal. Often, in themarine environment, there is a shift in the species and pigment composition and in thecharacteristic optical properties of phytoplankton with a change in chlorophyll-a concentration. Modelling Case 1 waters as a function of chlorophyll-a concentration alone does
not ignore totally such shifts in the properties of phytoplankton populations; it merely
assumes that they follow a predictable path. Deviations from these assumptions, when
they occur, are treated simply as noise around the general trend.
It is fortunate that the sweeping assumptions underlying the deﬁnition of Case 1
waters do hold over vast tracts of oceanic waters. This has made it possible to implement large-scale optical models that rely simply on chlorophyll-a concentration as inputs.
From a purely remote-sensing perspective, this has led to algorithms for phytoplankton
pigment retrieval designed for Case 1 waters. Such algorithms make use of additional
properties of Case 1 waters. For example, the atmospheric correction in Case 1 waters
exploits the fact that the contribution to ocean colour from such waters is negligible (orpredictable) in the near infra-red part of the spectrum (see Sections 2.9 and 3.1.2 forfurther details).
It would be diﬃcult to over-emphasise the importance of the classiﬁcation scheme,
especially in the context of remote sensing. It is particularly easy to appreciate the valueof this classiﬁcation when we look back on the CZCS experience: we were able to deriveuseful information on phytoplankton distribution from CZCS data primarily because the
CZCS algorithms exploited several simpliﬁcations that would hold in Case 1 waters. If
one had insisted on only using algorithms that would work in all possible conditions, thenthe unfortunate, and erroneous conclusion would have been drawn that the CZCS was
useless, because of its limited spectral resolution. In fact, the CZCS algorithm for pigment
retrieval makes use of a simple ratio of two wavebands, one in the blue and the otherin the green. The idea of compromising, and developing algorithms that would work atleast in Case 1 waters, was a judicious one, given that Case 1 waters are considered toencompass over 90% of the world oceans, and some inland water bodies as well. Thus,
even though Case 1 waters only occupy a small fraction of all possible cases (Fig. 1.3),
they dominate in geographic area. In fact, the success of the CZCS has been so striking
that the ocean-colour community has come to the conclusion that there will be a place

General Introduction•

17

in the future for simple CZCS-like instruments, which would function side-by-side withthe more sophisticated ocean-colour sensors now available (IOCCG, 1998).

1.4 Complexity of Case 2 Waters Relative to Case 1 Waters
The simplicity of single-variable models has to be abandoned when dealing with Case 2
waters, since we admit that at least three relevant quantities (phytoplankton, suspended
material and yellow substances, and perhaps even bottom reﬂectance) can vary independently of each other. It is clear from Fig. 1.3 that Case 1 waters encompass only a small
subset of all possible cases, whereas “Case 2 waters” is a catch-all phrase for everythingthat is not Case 1. From a modelling perspective, this implies that we can no longer assume a one-to-one relationship between optical properties at any given wavelength andthe pigment concentration.
As we shall see in greater detail in Chapters 3 and 4, development of algorithms forCase 2 augments the technical requirements of ocean-colour sensors, with respect to theirspectral resolution, as well as their radiometric accuracy and precision. Furthermore,
the algorithms for retrieval of the three variables become more complex, given that therequirement now is to unravel non-linear, spectrally-varying interactions among at least
three variables and the remotely-sensed signals. The changes in signal with changes in theconcentrations of the constituents are often very small, making it diﬃcult to guarantee
the quality and reliability of the extracted information. At some wavelengths, two or
more substances may inﬂuence the optical signal in a similar manner, making it diﬃcult
to distinguish one type of material from another. Therefore, in dealing with Case 2 waters,
one has to pay particular attention to the choice of wavelengths, and to the number ofwavelengths used in an algorithm. Algorithms for Case 2 waters have to address several
issues that are conveniently side-stepped when dealing with Case 1 waters. The questions
that arise solely in conjunction with algorithms for Case 2 waters include:❖ Do the atmospheric correction algorithms developed for Case 1 waters hold in Case 2
waters? If not, how can they be reﬁned, either by incorporating new wavebands into
the algorithm, or by modifying the mathematical approach?
❖ What additional wavebands do we require of an ocean-colour satellite to be able todistinguish among phytoplankton, yellow substances and suspended material?
❖ Given a complex, non-linear system with three or more independent variables, how
can we ensure that the retrieved variables always satisfy minimum requirements for
precision and accuracy?
❖ How can the algorithms be made to account for the diﬀerent optical signals from
diﬀerent types of suspended material and yellow substances?
❖ How do we determine whether or not the signal is inﬂuenced by bottom reﬂectance?

18 • Ocean Colour in Optically-Complex Waters❖ How do we account for the natural variability in the types of bottom that are present
in shallow waters (e.g., white sand, red mud, rocky, algae-covered) and the resultant
variations in their optical properties?
❖ Is it reasonable to expect that we can develop universal algorithms to deal with the
myriad of possible conditions and combinations, or would it be more reasonable todevelop regional algorithms, optimised for local conditions?
These questions are addressed in some detail in the following chapters.

1.5 Need for New Algorithms
The geographic distribution of Case 2 waters is variable: waters of a given locality may
drift between Case 1 and Case 2 conditions, depending on environmental forcing. At amost elementary level, we need an algorithm to distinguish between Case 1 and Case 2
waters. This would allow masking of regions where Case 1 algorithms may not hold, andclassifying these waters as belonging to the more complex Case 2. At a more advanced
level, we need algorithms that can derive more information about Case 2 waters.
Even though only a small fraction of total area of the water-covered parts of the earth
falls into the Case 2 category, it is an important fraction. It includes many coastal andinland water bodies whose economic, social and ecological signiﬁcance cannot be denied(see Chapter 5). Remote sensing provides us with the only tool for viewing the pelagic
environment at synoptic time scales. The science of ocean-colour remote sensing has
been steadily growing in the years since the launch of the CZCS. With this growth hascome new ideas and possibilities for dealing with Case 2 waters. The ocean-colour community therefore has a responsibility to pursue these opportunities further, and to learn
to extract all possible information from remote sensing.
There are yet other reasons why we need to pay more attention to dealing with Case
2 waters. For example, emerging results from oligotrophic, tropical areas (Siegel andMichaels, 1996; Bouman et al., 2000) suggest that the contribution from yellow substances to optical properties may be more important, and more variable, than hitherto
imagined. One might argue that these waters should be classiﬁed as belonging to the Case
1 category, since the yellow substances in these waters have a local origin. However, it
would no longer be reasonable to assume that the contribution from yellow substances
to optical properties would be a small fraction of the chlorophyll-a concentration, or that
all optical properties could be modelled as a function of chlorophyll-a alone. Hence, froma practical modelling point of view, it becomes untenable to treat these waters as being
Case 1, raising the question whether such open-ocean areas as have traditionally beentreated as Case 1 waters indeed always belong to Case 1, or whether sporadic or periodic
switching to Case 2 can occur.

General Introduction•

19

Algorithm development for Case 2 waters may eventually turn out to be beneﬁcial for
Case 1 waters as well. Typically, algorithms have always taken the contribution from other substances to optical properties of Case 1 waters to be negligible, or to covary with phytoplankton. Developing algorithms that are applicable in both Case 1 and Case 2 waters
would allow this assumption to be relaxed even in Case 1 waters, with a possible increasein the retrieval accuracy of phytoplankton concentration. In fact, there is some evidence
that algorithms accounting for the inﬂuence of components other than chlorophyll-a improve the accuracy of chlorophyll-a retrieval itself (see for example Sathyendranath etal., 1994).

1.6 Advantages of New Ocean-Colour Sensors over the CZCS in Case 2
WatersIt ought to be clear from the foregoing discussion that when we talk about algorithms
for Case 2 waters, we are not referring to minor alterations to Case 1 algorithms. Instead, what is required is a completely new family of algorithms for dealing with both
atmospheric correction and with retrieval of oceanic constituents. This is essential, because many of the simplifying assumptions that hold in Case 1 waters do not hold in
Case 2 waters. Atmospheric correction algorithms have to cope with the reality in Case 2
waters that wavebands treated as “atmosphere only” in Case 1 waters may contain a nonnegligible and variable signal from the sea as well. Many commonly-used algorithms forCase 1 waters are based on correlations between some simple function of ocean-colour
signals at two or three wavebands and chlorophyll-a concentration. Such algorithms,
which have proven to be very eﬀective in Case 1 waters, are not expected to perform wellin Case 2 waters, since the potential exists that more than one variable may inﬂuence
ocean colour. Thus, to develop eﬀective algorithms for Case 2 waters, we have to moveaway from simple algorithms that use two or three wavebands, to multi-waveband algorithms designed for retrieval of multiple variables. The small number of wavebands on
the CZCS did not allow the development of elaborate multi-waveband algorithms.
Remote sensing in Case 2 waters imposes additional demands on the sensor: as weshall see in some detail in Chapter 2, the relationship between the concentrations ofaquatic constituents and ocean colour is nonlinear, and we often have to exploit smallchanges in the signal to retrieve useful information. This implies that we need instruments with a high signal-to-noise ratio. The proximity to land and the possibility ofencountering highly-reﬂective waters call for sensors with a high dynamic range. Taken together, these are more stringent requirements than those for remote sensing overeither land or open oceans (Table 1.1). Several second-generation ocean-colour sensors
meet the requirements for Case 2 waters to a much higher degree than the CZCS did(IOCCG, 1999; see also Chapter 4). These improved prospects for dealing with Case 2 waters have led to a wave of studies designed for developing algorithms for remote sensingof Case 2 waters. This is an opportune moment to evaluate the progress achieved so far20 • Ocean Colour in Optically-Complex Waters
Land

Open Ocean Waters

Coastal and Inland Waters(Case 1 waters)(Case 2 waters)

Broad spectral bands

Narrow spectral bands

Narrow spectral bands

Wide dynamic range

Narrow dynamic range

Wide dynamic range

Low signal-to-noiseHigh signal-to-noiseHigh signal-to-noise

Atmospheric correction;

Radiative transfer;pigment algorithm

multivariate techniques

Sensor Requirements

Techniques
Clustering; classiﬁcation

Characteristics of Features to be Monitored
Spatial scales ∼10 m

Spatial scales ∼1 km

Spatial scales ∼ 30 m

Time scales ∼ 10 – 100 d

Time scales ∼1 d

Time scales ∼ 0.2 d

Crisp, ﬁxed boundaries

Fluid boundaries

Fluid boundaries

Many spectral signatures

One spectral signature

Many spectral signatures

Pigment concentration

Concentrations of three

Applications Envisaged
Land use/Land Covercomponents
Change over time

Bloom dynamics, productivity

Bloom dynamics, productivity,
sediment transport, coastal
dynamics

Disturbance/disease

Carbon cycle, harmful algal

Carbon cycle, harmful algal

bloomsblooms, water quality,
aquaculture, ﬁsheries,
leisure activities

Table 1.1 Visible remote sensing over land, open ocean and Case 2 waters (Adapted from information provided by Janet Campbell).

the ﬁeld of remote sensing of ocean colour in Case 2 waters (Chapter 3), and to outline
promising strategies for further developments (Chapter 6).

1.7 Need for a Fresh Approach
Remote sensing of Case 2 waters is almost synonymous with remote sensing of coastal
and inland water bodies, since it is here that one is most likely to encounter Case 2

General Introduction•

21

waters. Typical time and space scales of features to be monitored in coastal and inland
water bodies diﬀer from those of other environments, and the applications envisaged
are more varied (Table 1.1). What we need here is a fresh approach, rather than simple
adaptations of sensors and techniques developed for other environments. A successful
remote-sensing mission in Case 2 waters requires the use of suitable sensors with ground
resolutions and repeat cycles that are compatible with the scales of the features to bemonitored, as well as complementary in situ experiments that are carefully designed.
The requirements of a remote sensor for Case 2 waters are examined in more detail in
Chapter 4.
Remote-sensing does not exist in a vacuum. The ultimate justiﬁcation for the eﬀort
and expense involved must lie in the applications envisaged. When it comes to coastal andinland water bodies, the applications envisaged are many. Some of the main applications
are listed in Table 1.1, but the subject is given a more detailed airing in Chapter 5.
In the next chapter, we examine what we know about the optical characteristics of phytoplankton, yellow substances suspended material, and through simple models, examine
how these substances inﬂuence ocean colour.

22 • Ocean Colour in Optically-Complex Waters

Colour of Case 2 Waters •23

Chapter2
Colour of Case 2 Waters
S. Sathyendranath, R. P. Bukata, R. Arnone, M. D. Dowell, C. O. Davis, M. Babin,
J.-F. Berthon, O. V. Kopelevich, J. W. Campbell

2.1 Introduction
The colour of water is determined by scattering and absorption of visible light by purewater itself, as well as by the inorganic and organic, particulate and dissolved, materialpresent in the water. We recognise that these substances can vary independently of eachother in Case 2 waters, and so we must consider each of them separately. Furthermore,
the colour of Case 2 waters may be inﬂuenced by bottom reﬂectance in shallow, clear
waters, and so we must be able to deal with an optically-ﬁnite water body (the assumptionof water bodies that are inﬁnitely deep work excellently for dealing with open-ocean,Case 1 waters). Furthermore, we have to be aware of any implications for atmosphericcorrection procedures that follow from the properties of Case 2 waters.
The ﬁrst step in learning to interpret the ocean-colour signal is to understand how to
express the remotely-detected signal as a function of the concentrations of the various
substances present in the water column (Fig. 2.1). One can think of this as the forward
modelling problem, and it is the subject matter of this chapter. The inverse problem of
retrieving the concentrations of the components from the remotely-sensed signal (Fig.
2.1) is treated in the next chapter.
This chapter and the next are the most technical parts of the report, and they contain anumber of scientiﬁc terms and notations. Each new term is deﬁned and described brieﬂy
as it is introduced. Readers interested in delving deeper into the standard terms andnotations used in the optical oceanographic literature are referred to several excellent
text books on the topic, including Jerlov (1976), Monin (1983), Shifrin (1988), Kirk (1994),
Mobley (1994) and Bukata et al. (1995).
The objective of this chapter is to outline, deﬁne and describe the processes thatinﬂuence variations in colour observed in Case 2 waters. To this end we address thefollowing questions:24 • Ocean Colour in Optically-Complex Waters

Figure 2.1

Schematic diagram showing the interrelationships between apparent optical prop-

erties such as ocean colour, in-water constituents and inherent optical properties.

❖ What are the optical processes that determine ocean colour, and how can they be
represented mathematically?
❖ How does reﬂection of light from the bottom of the water body aﬀect ocean colour?
❖ What do we know of the optical properties of important water constituents, and how
can they be quantiﬁed?
❖ What are the special considerations for atmospheric correction in Case 2 waters?
We deal here primarily with in-water processes and modelling, with special reference
to Case 2 waters. Issues related to atmospheric correction are not the focus here. But
particular problems related to atmospheric correction in Case 2 waters are mentioned,for completeness.
We begin by examining the principal concepts that underlie the study of ocean colour.
This is not meant to be an exhaustive treatise on the subject; only the basic concepts
necessary to understand ocean-colour models are presented here. General concepts arepresented ﬁrst, before examining problems that are speciﬁc to Case 2 waters.

2.2 Ocean Colour
Intrinsic colour of the ocean is deﬁned by spectral variations in reﬂectance R at thesea surface. At any depth z, R is deﬁned as:

R(λ, z) =Eu (λ, z),Ed (λ, z)(2.1)

Colour of Case 2 Waters •25

where Eu (λ, z) is the irradiance (ﬂux per unit surface area) in all the upward directions,or upwelling irradiance, at wavelength λ and depth z; and Ed (λ, z) is the irradiance inall the downward directions, or the downwelling irradiance, at the same wavelength anddepth.
The term albedo is also used instead of reﬂectance in some contexts. For example,
the bottom albedo A (see section 2.5) is deﬁned as the ratio of upwelling irradiance todownwelling irradiance at the bottom of the water column.
All optical properties of ocean waters are wavelength dependent. But in the following
discussion, the wavelength argument may be dropped from the notations in the interestof simplicity. The wavelength of light changes with the refractive index of the medium.
When speciﬁc wavelengths are mentioned in this report, they refer to wavelengths invacuum.
From a practical perspective, Eu is the ﬂux received by a ﬂat collector with a cosine
response facing downwards, divided by the surface area of the collector. Similarly, Ed ismeasured using a ﬂat collector facing directly upwards. Reﬂectance R is a dimensionlessquantity.
These irradiances measured on plane surfaces have to be distinguished from scalar
irradiance, which measures ﬂux received by a spherical collector from all directions.
Scalar irradiance usually takes the notation E0 , has the same dimensions as irradiance,and can also be partitioned into downwelling and upwelling components (E0d and E0u
respectively).
The upwelling irradiance (the numerator of reﬂectance R at the sea surface) is a measure of all the light that leaves the sea surface. However, a remote detector with a narrowﬁeld of view does not receive all the water-leaving irradiance, since the shape of the detector and its viewing geometry conﬁne the signal to a small fraction of this ﬂux. Hence,a description of the light ﬁeld would be incomplete if it did not include information on
how the ﬂuxes varied as a function of direction. This complete description is provided
by radiance, which measures ﬂux per unit area and per unit solid angle. If L(θ, φ) is
the radiance in the direction deﬁned by zenith angle θ and azimuth angle φ, then thevarious irradiances of interest here can be obtained by suitable integrations of L over theappropriate angles. For example, we have

Ed (λ, z) =

 2π  π /2
φ=0

θ=0

L(λ, z, θ, φ) cos θ sin θ dθ dφ,

(2.2)

and

E0d (λ, z) =

 2π  π /2
φ=0

θ=0

L(λ, z, θ, φ) sin θ dθ dφ.

(2.3)

26 • Ocean Colour in Optically-Complex Waters
Here the direction pointing vertically downwards is taken to represent zenith angle ofzero. The upwelling irradiances (Eu and E0u ) can be obtained similarly from the radiance distribution, by integrating radiance over all zenith angles within the range from
π /2 to π . In the context of remote sensing, it is common to deal with remote-sensing
reﬂectance (RRS ), which is closely related to the sea-surface reﬂectance R, but makes useof upwelling radiance rather than irradiance, and is deﬁned as:

RRS (θ, φ, λ, 0) =L(θ, φ, λ, 0).
Ed (λ, 0)(2.4)

The arguments θ and φ on the radiance indicate that the water-leaving radiance can varywith the viewing angle (at the surface, z = 0). Remote-sensing reﬂectance has dimensionsof [sr−1 ]. In other words, the remote-sensing reﬂectance decomposes the reﬂectance R into its component radiances as a function of the viewing angles θ and φ. A proportionality
factor, say Q(θ, φ, λ), relates RRS (θ, φ, λ, 0) to R(λ, 0):Q(θ, φ, λ) =R(λ, 0).
RRS (θ, φ, λ, 0)(2.5)

The factor Q has dimensions of [sr].
Note that the remote-sensing reﬂectance (RRS ) and reﬂectance (R) that appear in Equation (2.5) refer to signals just below the sea surface. Water-air interface eﬀects (transmittance, internal reﬂection and refraction) have to be suitably accounted for, before RRS
below the surface of the water can be compared with the remotely-sensed signal.
The goal of remote-sensing of ocean colour is to derive quantitative informationon the types of substances present in the water and on their concentrations, fromvariations in the spectral form and magnitude of the ocean-colour signal. Hence, it
is desirable to express reﬂectance and remote-sensing reﬂectance as functions of theconcentrations of the substances in the water column that inﬂuence ocean colour. This
is not a trivial task, and involves a number of steps. The ﬁrst step is to understand thevarious optical properties that appear in the problem.

2.3 Inherent and Apparent Optical Properties
Ocean colour, and remote-sensing reﬂectance, can be modiﬁed by the zenith-angularstructure of the incident light ﬁeld. In other words, it is an apparent optical property, as deﬁned by Preisendorfer (1976). Apparent optical properties are those opticalproperties that are inﬂuenced by the angular distribution of the light ﬁeld, as well as by
the nature and quantity of substances present in the medium. Another apparent optical

Colour of Case 2 Waters •27property of interest in these discussions is the diﬀuse attenuation coeﬃcient for downwelling irradiance, or Kd , which deﬁnes the rate of decrease of downwelling irradiancewith depth. We have, by deﬁnition,dEd (λ, z)= −Kd (λ)dz.
Ed (λ, z)(2.6)

The attenuation coeﬃcient Kd is used in models of light penetration, for example tocompute primary production as a function of light available at depth (see Chapter 5). It
is also often used as an index of water clarity, and is one of the geophysical variables
that can be derived from ocean-colour data (see Chapter 5). This coeﬃcient also appears
in models of ocean colour, as discussed later on in this chapter.
Two types of diﬀuse attenuation coeﬃcient for upwelling irradiance are also of interest in ocean-colour studies. The ﬁrst of these deﬁnes the rate of exponential decrease
in upwelling irradiance with increasing depth (Ku ). The equation for Ku would be similar to Equation (2.6), with the sole diﬀerence that Ku would be used to deal with the
upwelling irradiance Eu , rather than the downwelling irradiance Ed . The coeﬃcient Ku
would be the appropriate attenuation coeﬃcient to use for computation of upwelling
irradiance at some depth z, given the upwelling irradiance at some shallower depth, sayat the surface. This is the diﬀuse attenuation coeﬃcient for upwelling irradiance thatappears in classical optical oceanographic literature. However, in the last decade or so,
another attenuation coeﬃcient for upwelling irradiance has gained common usage in developing models of ocean colour. This quantity, introduced by Kirk (1989), commonly
takes the notation κ, and quantiﬁes the rate of decrease in an upwelling irradiancegenerated at a certain depth, with decreasing depth. The role of κ would be in the computation of upwelling irradiance that reaches a shallower depth (say the surface), given
an upwelling ﬂux generated at some greater depth. All these attenuation coeﬃcients
have dimensions of [L−1 ].
Quantitative interpretation of the remotely-sensed signal in terms of the constituentsof the water requires that we identify and isolate the eﬀect on the signal arising fromvariations in the incident light ﬁeld. Therefore, a necessary step in modelling ocean
colour is to express remote-sensing reﬂectance in terms of inherent optical properties(Preisendorfer, 1976), which are independent of variations in the angular distributionof the incident light ﬁeld, and are solely determined by the type and concentration ofsubstances present in the medium.
Several rigorous studies of radiative transfer in the ocean have been undertaken tounderstand the relationships between ocean colour and inherent optical properties of
the water. These studies take into account the processes of absorption and scattering,that control the manner in which impinging photons propagate through a natural water body. The process of absorption removes photons from the light ﬁeld. Scattering,28 • Ocean Colour in Optically-Complex Waters
however, inﬂuences the light budget of the medium by changing the direction of photon
propagation. Furthermore, scattering can be partitioned into elastic and inelastic processes: if elastic scattering takes place, then the scattered photon has the same wavelength as the incident photon; inelastic scattering implies a change in the wavelengthof the scattered photon. Raman scattering by water (an inelastic scattering process) is
important in a remote-sensing context. Fluorescence emission from dissolved organic
matter and from phytoplankton can also be modelled similarly to inelastic scattering inocean-colour models.
When the impinging radiation is natural sunlight in the visible domain (roughly inthe wavelength range from 400 nm to 700 nm), then the above-mentioned processes will
determine the colour of the ocean. The observed colour of a water body thus becomesa direct consequence of the interaction of incident ﬂux (downwelling, direct-solar anddiﬀuse-sky, irradiances) with water and with organic and inorganic substances present
in it at the time of observation. Case 2 water bodies display spatial and temporal variations in their organic and inorganic composition, and therefore display corresponding
variations in colour.
The inherent optical properties of relevance in this context are the absorption coefﬁcient a (which determines the exponential rate of decay of ﬂux per unit pathlength oflight in the medium, and per unit incident ﬂux, due to the process of absorption), and thescattering coeﬃcient b, which deﬁnes similarly the exponential rate of decay of the ﬂux
due to scattering. Scattering coeﬃcients have to be deﬁned separately for elastic andinelastic scattering processes. Both absorption and scattering coeﬃcients are deﬁned
for collimated, monochromatic ﬂux incident normally on the medium, and traversing an
inﬁnitesimally thin layer of the medium. Unlike the apparent optical properties, they
are not determined for natural conditions of illumination. Absorption and scattering
coeﬃcients have dimensions of [L−1 ].
Since scattering implies a change in direction of the incident photon, a descriptionof scattering is incomplete unless we also describe the distribution of the scattered ﬂux
as a function of scattering angle. The volume scattering function (β(χ)) is the inherent
property that provides this description of scattering as a function of scattering angle χ,and has dimensions of [L−1 sr−1 ]. Integration of volume scattering function over all solid
angles yields the scattering coeﬃcient. In the case of inelastic scattering, we also haveto prescribe the wavelength distribution of the scattered ﬂux.
Other inherent optical properties can be derived given this basic set. For example, theback-scattering coeﬃcient bb , which often appears in the solutions of ocean-colour models, is deﬁned as the integral of the volume scattering function β(χ) over all backward
directions (χ > 90◦ ). Note that bb is diﬀerent from the upward scattering coeﬃcientbu (sometimes referred to as diﬀuse back-scattering coeﬃcient) which is often used inocean-colour models. The upward scattering coeﬃcient refers to scattering under natural

Colour of Case 2 Waters •29conditions of illumination, and hence is an apparent property. Furthermore, this coefﬁcient represents an integral of β(χ) over all upward directions (zenith angle θ > 90◦ )rather than over all backward directions. Another inherent optical property, the total
volume attenuation coeﬃcient, c, is deﬁned as the sum of absorption and scattering
coeﬃcients (c = a + b). Note that the total attenuation coeﬃcient c is distinct from theapparent optical properties Kd , Ku and κ, which were deﬁned earlier. For example, wehave the following relationships, which often hold in aquatic media: a ≤ Kd ≤ c, andKd  Ku , but Kd < κ.
Apparent optical properties such as Kd and R are measured using ﬂat-plate collectors facing directly upwards or downwards, with the angular distribution of the incident
ﬂux on the collectors being determined by environmental conditions. On the other hand,inherent optical properties are estimated for normal incidence. In measuring inherent optical properties, distances are measured along the direction of the incident ﬂux, whereas
apparent optical properties such as Kd are measured with reference to vertical intervals
in the water body. Therefore, the relationships between inherent and apparent opticalproperties typically depend on the angular distribution of the light ﬁeld under whichapparent properties are measured. Hence, it is usually necessary, when linking inherent
and apparent optical properties, to have a description of the angular distribution of thelight ﬁeld.
Two properties of the light ﬁeld commonly used to describe the angular distributionof the light ﬁeld, are µ d , the mean cosine for the downwelling irradiance, and µ u , themean cosine for the upwelling irradiance. The mean cosines are the weighted averagesof the cosines of the incident angles of the photon ﬂux, the weighting function being themagnitude of the radiance from each incident direction. The mean cosines are estimatedfrom the radiance ﬁeld, or from measurements of E0d and Ed . By deﬁnition,
µd =

Ed
,
E0d

(2.7)

and µ u can be estimated similarly, as the ratio of upwelling irradiance on a plane surface
to upwelling scalar irradiance.
The inverse of the mean cosine deﬁnes the mean pathlength of the ﬂux per unit vertical excursion. The mean cosine for the downwelling ﬂux incident at the sea surface isdetermined by the conditions of light transmission through the atmosphere. But scattering can modify the mean cosines within the water column.
Unlike the apparent optical properties, inherent optical properties have the advantage
of being uninﬂuenced by the illumination conditions, as mentioned previously. There isa further advantage to dealing with these quantities: in a multi-component medium, the30 • Ocean Colour in Optically-Complex Waters
bulk (or total) inherent optical properties are obtained by a simple addition of the contributions of the individual components. This strict additivity may not hold for apparent
optical properties, especially in highly scattering media.
It is recognised that the principal agent responsible for variations in the inherent
optical properties of Case 1 waters is phytoplankton populations and accompanying materials which covary with phytoplankton (see Chapter 1). In addition to phytoplankton,
suspended particulate material and dissolved organic matter of aquatic or terrestrial
origins can be present in varying concentrations in Case 2 waters, and they can vary independently of phytoplankton concentration. In fact, in Case 2 waters, it is often likelythat the optical properties of substances other than phytoplankton dominate the bulk
optical properties. Since the goal of remote sensing of ocean colour is to retrieve quantitative information on the constituents of the water body, we need information on theinherent optical properties of each of these major constituents to be able to model ocean
colour successfully.
Eﬀorts at modelling ocean colour for applications in remote sensing revolve around
two major issues:❖ Development of theoretical models for representation of ocean colour as a functionof inherent optical properties.
❖ Cataloguing the inherent optical properties of substances encountered in water bodies, and their variations with the concentrations of the substances.
The latter step is essential for the representation of the bulk inherent optical properties
as functions of the optical properties of individual components and their concentrations.
Furthermore, realistic interpretation of ocean-colour data should also take into accountthe natural variability in the optical signatures of these substances.
In the next section, we examine brieﬂy ocean-colour models intended to forge a link
between inherent and apparent optical properties.

2.4 Ocean-Colour Models for Deep Waters
Determination of relationships between apparent and inherent optical properties requires that we simulate radiative transfer processes in the ocean in a realistic fashion.
Several approaches to radiative transfer have been used in this context.
One such approach is Monte Carlo simulation of photon propagation through aquatic
media. In this technique, probability functions are used to decide the fate of photons ineach of their interactions with the medium, and ensemble averaging over many photons
is used to calculate the resultant light ﬁeld. The selection of the probability functions
is based on the inherent optical properties of the medium. Application of Monte Carloanalyses to atmospheric and oceanic photon interactions was pioneered by Plass andColour of Case 2 Waters •31

Kattawar (1972) and Kattawar and Plass (1972). Since then, a number of workers, including Gordon and Brown (1973), Gordon et al. (1975), Kirk (1981a,b; 1984), Jerome et al.
(1988), Stavn and Weidemann (1989), Bannister (1990), Morel and Gentili (1991; 1993)
and Sathyendranath and Platt (1997; 1998), have used Monte Carlo simulations to relate
the inherent optical properties of natural water bodies to apparent optical properties.
Other techniques for studying radiative transfer in complex optical media have also been brought to bear on ocean-colour studies. These include the two-ﬂow method(Kozlyaninov and Pelevin, 1965; Åas, 1987), the quasi-single scattering approximation(Gordon et al., 1975; Sathyendranath and Platt, 1997; 1998), and the method of successive orders of scattering (Morel and Prieur, 1977).
Perhaps not surprisingly, there are some small diﬀerences in the conclusions drawn
from these various studies. Some of these may be attributed to diﬀerences in the techniques, and others to diﬀerences in the speciﬁcations of the optical properties of themedium. But practically invariably, all these studies have led to the robust and consistent result that the reﬂectance at the sea surface, in the absence of inelastic processes,
can be expressed as a simple expression of the back-scattering coeﬃcient (bb ) and theabsorption coeﬃcient (a). In other words:

R(0) = fbb,(a + bb )(2.8)

where f is a parameter that is known to have some variability. Under the condition thatbb  a, which would not be unrealistic for Case 1 waters, Equation (2.8) can be replaced
by R = f bb /a, as in Morel and Prieur (1977). Note, however, that the assumption thatbb would be much smaller than a would be a poor approximation for Case 2 waters
dominated by suspended particles. Reﬂectance at the sea surface can also be expressed asa function of attenuation coeﬃcients for downwelling and upwelling ﬂuxes. For example,
we have (Åas, 1987; Kirk, 1989)
R(0) = [µd (Kd + κ)]−1 bu .

(2.9)

If we note (Sathyendranath and Platt, 1997) that bu = sbb (where s is a shape factor related
to the angle of incidence of the light and the shape of the volume scattering function),and that Kd is approximately equal to (a + bb )/µd and κ to (a + bb )/µu , and substitutethese into Equation (2.9), we recover Equation (2.8), if we set
f =

sµ u
.
(µd + µu )(2.10)

32 • Ocean Colour in Optically-Complex Waters
Thus, the models that are based on diﬀuse attenuation coeﬃcients and those based oninherent optical properties are consistent with each other. We can deduce from Equation(2.10) (see also Gordon et al., 1975; Morel and Gentili, 1991; Kirk, 1994; Sathyendranathand Platt, 1997) that the proportionality factor f depends on the shape of the volume
scattering function, and on the zenith angular distribution of the light ﬁeld. The volume
scattering functions of particles in Case 2 waters may diﬀer from those in Case 1 waters, and the relative importance of molecular scattering and particle scattering may alsochange between Case 1 and Case 2 waters. One may anticipate that both these factors willinﬂuence values of f , which may diﬀer signiﬁcantly between Case 1 and Case 2 waters.
Models such as those described here are important in remote-sensing applications,since they link reﬂectance (R) to the inherent optical properties (absorption and backscattering coeﬃcients), and isolate the environmental inﬂuences into the parameter f .
Most of these theoretical developments relate bb and a to reﬂectance R. But some eﬀorts
have also focused on the relationship between remote-sensing reﬂectance RRS and theinherent optical properties of the water (Morel and Gentili, 1993; Jerome et al., 1996).
These results are applicable to deep waters with a homogeneous water body. Numerical models have also been developed to deal with stratiﬁed water columns (Gordon and
McCluney, 1975; Gordon and Clark, 1980; Sathyendranath and Platt, 1989). Gordon andco-workers (Gordon and McCluney, 1975; Gordon and Clark, 1980) have suggested thatuniform-water-column models can be adapted to deal with non-uniform cases, if the inherent optical properties used in uniform-water models are weighted in a suitable way.
The weighting function used accounts for the fact that deeper layers in the water column make progressively smaller contributions to the water-leaving radiances, because
the light is attenuated as it travels down the water column, and then again as it travels
upwards after scattering.
2.4.1 Inﬂuence of inelastic processes on ocean colour:
The reﬂectance model presented so far accounts exclusively for elastic processes that
contribute to the diﬀuse reﬂectance. In other words, we have only dealt with elastic scattering, in which there is no diﬀerence between the wavelengths of incident or excitation(λex ) and scattered or emitted (λem ) light. That is to say, λem = λex in the case of elastic
scattering. On the other hand, inelastic or trans-spectral processes are associated withredistributions of light so that λex = λem . In general, this wavelength shift is towards
higher wavelengths (λex < λem ).
Trans-spectral processes of importance in passive ocean-colour studies can be divided
into Raman scattering (due to pure water) and solar-stimulated ﬂuorescence. In coastal
marine waters, ﬂuorescence is generated by dissolved organic matter, and by two types ofphytoplankton pigments: chlorophyll-a and phycoerythrin. Chlorophyll-a ﬂuorescencehas been studied extensively. It has been proposed that chlorophyll-a ﬂuorescence can beColour of Case 2 Waters •33

used as an alternative technique for deriving chlorophyll-a concentration from remotelysensed ocean-colour data (e.g. Gower, 1980; Gower and Borstad, 1990). Fluorescencefrom phycoerythrin has been observed in radiometric data (Culver, 1996), and also by
remote sensing using active lidar techniques (Hoge, et al. 1986).
The problem of incorporating inelastic processes into reﬂectance models has beenaddressed in investigations by Haltrin and Kattawar (1993), Lee et al. (1994), Haltrin etal. (1997) and Sathyendranath and Platt (1998). According to Haltrin et al. (1997), Raman
scattering dominates the contributions from ﬂuorescence by chlorophyll and dissolved
organic matter (except at some speciﬁc wavelengths) in oligotrophic waters. Individual
contributions to the total inelastic reﬂectance may change in Case 2 waters for which
one would expect Raman scattering to be less signiﬁcant, since the importance of Raman
scattering by water is known to decrease with decrease in water clarity.

2.5 Inﬂuence of the Bottom on Ocean Colour
For any Case 2 waters, one cannot overlook the possibility that the water may be
optically shallow (optically shallow implies that the product of the diﬀuse attenuationcoeﬃcient Kd and the geometric depth z is small). In such instances, relationships between surface reﬂectance and other optical properties of the water, such as Equations(2.8) and (2.9), will have to be modiﬁed to account for the fact that the signal from theshallow body of water would be less than that from deep waters. On the other hand, thewater-leaving radiance will have an additional component: light reﬂected oﬀ the bottom(Fig. 1.2).
In shallow waters, remote-sensing systems frequently image the bottom. The maximum depth from which the sensor receives any signiﬁcant signal varies as a functionof wavelength, and of the clarity of the water. Absorption by the water itself limits thedepth of penetration for the longer wavelengths, whereas absorption by dissolved organic matter and phytoplankton limits penetration at wavelengths shorter than 480 nm. In
the clearest waters, the bottom is detectable at 30 m over a narrow spectral band centred
at 490 nm. In the same waters, the spectral range reaching 20 m would be 430 to 580 nm.
At 10 m it would be 400 to 610 nm. The variation of spectral attenuation with depth is a
key tool for using remote-sensing data to determine bathymetry and bottom characteristics. The depth of penetration and the spectral range of penetration vary greatly as afunction of water clarity. In typical coastal waters, the bottom is detected to 10 m or less.
In highly turbid waters, such as rivers with a high suspended sediment load, the depthof light penetration is less than a metre for all wavelengths, and the bottom would not
be visible.
The approach to modelling ocean colour in shallow waters has been to use two-ﬂow
equations to obtain approximate solutions to the radiative-transfer equation. Early work

34 • Ocean Colour in Optically-Complex Waters(e.g. Lyzenga, 1978; Philpot, 1987; 1989) focused on using data from multispectral systems with high spatial resolution, such as Landsat Thematic Mapper. More recent approaches (Maritorena et al., 1994; Lee et al., 1998a; 1999) take advantage of the additional
information available in data with high spectral resolution. The two-ﬂow equations inMaritorena et al. (1994) are functionally similar to those in the earlier papers, and can beused to describe the general approach to the problem. When the bottom is detectable,
we recognise that Eu (0) is made up of two components: one from the water column andthe other from the bottom.
For a water column limited by a perfectly absorbing bottom at depth H, integration ofcontributions to the upwelling irradiance from all depths in the interval 0 to H yields thewater column contribution. If we now assume that the bottom is a Lambertian (uniformly
diﬀuse) reﬂector with albedo A, then the contribution from the bottom can be computed
knowing the attenuation coeﬃcients for downwelling and upwelling irradiances. Finally,
we obtain (Maritorena et al., 1994):R(0, H) = (Kd + κ)−1 bu + A − (Kd + κ)−1 bu exp − (Kd + κ)H ,(2.11)

where R(0, H) indicates reﬂectance at the surface from a water column of depth H, andwe have assumed that the values of κ applicable to the water column and the bottom are
the same.
The validity of results such as Equation (2.11) has to be evaluated by comparisonswith more complete Monte Carlo simulations and with ﬁeld observations (Maritorena etal., 1994). These equations are the simplest representations of the factors that inﬂuence
ocean colour in a shallow body of water. Nevertheless, they are suﬃcient to provide
a ﬂavour of the complexity of remote sensing in shallow waters, especially when we
consider that all the optical quantities in Equation (2.11) are wavelength dependent.
Once the relationship between ocean colour and the inherent optical properties is wellunderstood, the next step in the analysis is to express the inherent optical properties
as functions of the seawater constituents and their concentrations. This aspect of the
problem is examined next.

2.6 Relationship between Bulk Optical Properties and Water Constituents
The inherent optical properties used so far are the bulk optical properties, in thesense that they represent the combined eﬀects of water and all its constituents. To quantify the role of individual components, the absorption coeﬃcient a can be expressed as
the sum of contributions from each of the constituents, as mentioned previously. Inturn, the contributions of each component can be expressed as the product of the concentration of that substance and a corresponding speciﬁc absorption coeﬃcient (withColour of Case 2 Waters •35dimensions of [L2 M−1 ], provided concentration as measured has dimensions of [ML−3 ]).
Other bulk inherent optical properties can be treated analogously to the absorption coeﬃcient, and expressed as simple linear functions of the concentrations of contributing
components. Note that the speciﬁc coeﬃcients are referred to in limnological literatureas the optical cross sections (Bukata et al., 1981b; 1995). The speciﬁc inherent opticalproperties provide the linkages between the bulk inherent optical properties of a natural
water body and the concentrations of its organic and inorganic constituents.
Speciﬁc inherent optical properties of a medium are the inherent optical properties ofindividual components of that medium, per unit concentration of the component. Given
that phytoplankton, inorganic suspended particles, and yellow substances (and detrital
particles) contribute to the bulk absorption coeﬃcient of water, in addition to a constantbackground contribution from water itself, we can write:∗∗a = aw + P a∗
p + Y ay + Sas ,(2.12)

where the subscripts w, p, y and s stand for water, phytoplankton, yellow substances andinorganic suspended particles respectively, and P , Y and S stand for the corresponding
concentrations. In the notation used here, the asterisks indicate speciﬁc coeﬃcients.
Similarly, the back-scattering coeﬃcient can be partitioned into three components:∗∗
bb = bbw + P bbp+ Sbbs.

(2.13)

Note that equation (2.13) assumes that yellow substances (being dissolved materials) do
not contribute signiﬁcantly to the bulk back-scattering coeﬃcient. But in the partitioning
used here, the yellow substance component also incorporates detrital material which, inprinciple, does scatter. If necessary, an additional term can be added to Equation (2.13)
to account for scattering by detrital particles.
This type of partitioning of bulk inherent optical properties into their components
would be equally valid for Case 1 and Case 2 waters. What distinguishes Case 2 watersfrom Case 1 waters is the additional component for suspended particles other than phytoplankton. (In Case 1 waters, the factors that contribute to this term are assumed either
to be negligibly small, or to covary with the phytoplankton term.) Furthermore, one admits the possibility that the contribution to absorption from yellow substances may be
more important than in Case 1 waters, and may vary independently of phytoplankton.
All the apparent, inherent, bulk, and speciﬁc optical properties of water are wavelength dependent. It is the particular wavelength dependencies of individual componentsthat deﬁne ocean-colour signals in Case 2 waters. Figure 2.2 illustrates some examplesof remote-sensing reﬂectance spectra from diﬀerent types of waters. It is clear from the36 • Ocean Colour in Optically-Complex Waters
ﬁgure that there are distinct changes in the RRS signal with changes in the water constituents. The spectral qualities of the speciﬁc optical properties of individual components determine the exact nature of the interdependence between individual constituentsof the water and the RRS signal. In the next section, we examine brieﬂy what is known ofthese characteristic spectra.

Figure 2.2

Some examples of remote-sensing reﬂectance spectra from diﬀerent types of wa-

ters. The ﬁgure shows (a) waters with very high sediment and gelbstoﬀ concentrations, (b) highsediment and gelbstoﬀ concentrations, (c) moderate sediment and gelbstoﬀ with some phytoplankton, (d) clear water, (e) waters with moderate chlorophyll and sediment concentrations, (f)waters with moderate chlorophyll concentration. a, b, c and e are Case 2 waters; d and f are
Case 1 waters. Data courtesy of Robert Arnone.

2.7 Inherent Optical Properties of Aquatic Substances
2.7.1 Absorption characteristics of phytoplankton
In studies of the optical properties of phytoplankton, the concentration of the main
phytoplankton pigment, chlorophyll-a, is taken as the index of phytoplankton concentration, and speciﬁc coeﬃcients of absorption and scattering are estimated relative tochlorophyll-a concentration. Since pigment-bearing phytoplankton are the principal coloured agents in Case 1 waters, considerable eﬀort has been expended in studying the speciﬁc absorption spectra of phytoplankton. Theoretical considerations (Duysens, 1956;

Colour of Case 2 Waters •Figure 2.3

37

Examples of phytoplankton absorption spectra. (a) Data compiled by Gege (1998)

of some dominant species of phytoplankton found in Lake Constance. (b) Speciﬁc absorption
spectra of high light and low light-adapted Prorocentrum minimum cells (data courtesy of Geir
Johnsen, adapted from Johnsen et al., 1994).

38 • Ocean Colour in Optically-Complex Waters
Kirk, 1975a,b; Morel and Bricaud, 1981) and direct observations (Kirk and Tyler, 1986;
Sathyendranath et al., 1987; Mitchell and Kiefer, 1988a,b; Morel, 1988; Bricaud and Stramski, 1990; Bukata et al., 1991; Gallie and Murtha, 1992; Hoepﬀner and Sathyendranath,
1992; 1993) have led to the realisation that the numerical values of the speciﬁc absorption coeﬃcients of phytoplankton fall into a broad range. It has been shown that they
are dependent on the algal species that make up the phytoplankton population and on
their physiological state. Consequently, they vary with time and space. As examples,Fig. 2.3a shows speciﬁc absorption spectra of some phytoplankton species that may be
encountered in Lake Constance (Gege, 1998), and Fig. 2.3b shows variations in the absorption characteristics of a phytoplankton culture with changes in the growth environment(Johnsen, et al. 1994).
In Case 1 waters, some common trends in phytoplankton dynamics, and consequently in phytoplankton optics, have been observed. For example, typically, phytoplankton
populations that are found in oligotrophic waters have higher speciﬁc absorption coefﬁcients than phytoplankton in eutrophic (phytoplankton-rich) waters (Fig. 2.4). Several
authors (e.g., Prieur and Sathyendranath, 1981; Bricaud et al., 1995; Cleveland, 1995)
have parameterised such general trends, and their regional variations (Lutz et al., 1996),
using non-linear relationships between phytoplankton absorption and chlorophyll-a concentration.
Such models of phytoplankton absorption may not be directly applicable to Case 2
waters, because the species composition and dynamics of phytoplankton in coastal andinland water bodies may diﬀer signiﬁcantly from those observed in the open ocean. For
example, Stuart et al. (1998) have compared the optical properties of phytoplankton
from the Arabian Sea with those from coastal waters oﬀ Vancouver Island, and foundconsiderable diﬀerences.
As mentioned in Chapter 1, the phytoplankton component, in a remote sensing context, is often taken to include the contributions to the optical properties from othermicroscopic organisms. This is a device of convenience for dealing with data from thenatural environment. However, unlike ﬁeld samples, phytoplankton cultures in laboratories can be maintained more “pure”, making direct comparisons between in situ measurements and laboratory measurements of phytoplankton optical properties a diﬃcult
exercise. Furthermore, in the natural environment, several species of phytoplankton
co-exist at any given time, whereas laboratory measurements are generally carried out
on mono-speciﬁc cultures. This also makes it diﬃcult to compare laboratory and ﬁeld
measurements of the optical properties of phytoplankton.
2.7.2 Absorption by inorganic suspended material
Suspended inorganic particulate material in natural water bodies generally reﬂects
the geologic structure and composition of the adjacent land or basin area. The geologic
diversity of coastal regions, the dynamics of ocean currents, and a variety of residenceColour of Case 2 Waters •39times would result in substantial disparities in the scattering and absorptive properties
of indigenous suspended matter. An impediment to the interpretation of ocean-colour
data in Case 2 waters is the lack of suﬃcient information on the spectral variationsin the speciﬁc inherent optical properties of terrigenous suspended matter. Spectra of
speciﬁc absorption and scattering coeﬃcients of inorganic matter are dependent upon
particle shape, particle size distribution, and refractive index. This is evident from thedissimilar spectral optical properties reported by Morel and Prieur (1977), Whitlock et al.
(1981), Prieur and Sathyendranath (1981), Bukata et al. (1991), Gallie and Murtha (1992)
and others. See Bukata et al. (1995) for more detailed discussion on the absorption
characteristics of inorganic suspended material. Note that this component, as deﬁned
here, does not include the "detrital component" of particulate matter measured in Case 1
waters, which apparently has an organic source. This is discussed in more detail below.

Figure 2.4

Some examples of speciﬁc absorption spectra of natural phytoplankton popula-tions from various marine environments. The dominant phytoplankton group or species (identiﬁed on the basis of HPLC analysis and microscopic examination) in each sample is indicated.
The ambient chlorophyll-a concentration at the time of sampling is also shown. Unpublisheddata from the Biological Oceanography Group at the Bedford Institute of Oceanography, Canada.

40 • Ocean Colour in Optically-Complex Waters
2.7.3 Absorption by yellow substances
There is excellent agreement in the spectral form of absorption by dissolved organic
materials reported by various workers (e.g., Unoki et al., 1978; Bricaud et al., 1981; Bukata
et al., 1981b; Roesler et al., 1989; Gallegos et al., 1990). According to several studies,absorption spectra of yellow substances, say ay , can be expressed as an exponentialfunction of the form:ay (λ) = ay (λ0 )exp(−S(λ0 − λ)),(2.14)

where λ0 is a reference wavelength and S is an empirically-determined slope of the exponential curve. Roesler et al. (1989) noted that the published mean value of S for yellow
substances was 0.016 nm−1 with a standard deviation of 0.002 nm−1 . Their conclusion
is in agreement with the results of Kopelevich et al. (1989) based on over 400 spectra
measured by various investigators in oceanic, coastal and fresh waters. They reported
that S had a mean value of 0.017 nm−1 (with standard deviation, SD, of 0.001) in the
spectral domain from 280 to 490 nm. According to Carder et al. (1989) the variability inthe slopes of the absorption spectra of yellow substances is associated with variationsin their composition: they consist of fulvic and humic acids. They report a slope of
0.019 nm−1 for marine fulvic acid and a slope of 0.011 nm−1 for marine humic acid (seealso Kopelevich and Ershova, 1997). It is intriguing, however, that some values outside ofthis range have also been reported. For example, Højerslev (1998) has reported a range in
S of 0.008 to 0.042 nm−1 . It might be worth investigating whether these extreme values
indicate diﬃculties with measurement, or some other source of absorption. Kopelevich
et al. (1989) have reported that the mean slope decreases to 0.011 nm−1 for the spectral
range from 500 to 680 nm.
Absorption spectra for the so-called detrital component of particulate matter (similar
to those of yellow substances, see Equation 2.14) are now routinely measured, by concentrating particulate material from ﬁeld samples on to ﬁlters. The detrital absorption
is taken to be the residual particulate absorption that remains when the absorption byphytoplankton pigments is subtracted from total particulate absorption. It is usually
measured using the ﬁlter technique (Yentsch, 1962) as modiﬁed by Mitchell and Kiefer(1984; 1988a) and Kishino et al. (1985). Based on a literature survey, Roesler et al.
(1989) reported that the mean value for the slope of the absorption spectra for detritus
is 0.011 nm−1 (with SD of 0.002 nm−1 ). This material is typically assumed to have a local,
biological origin, even though detrital material may also have other sources in Case 2
waters (e.g., resuspension of organic material from the bottom; river and land drainage).
The similarities in the absorption coeﬃcients of detrital material and yellow substances render problematical the separation of absorption due to detrital particulates
from that due to yellow substances, from a remote-sensing perspective. This is the mainColour of Case 2 Waters •41reason why, in this report, we have included the “detrital” component with the yellowsubstance component. However, if techniques emerge for separating the contributionsof these two components (perhaps by exploiting the small diﬀerences in the slope of theabsorption spectra), then of course these two components can be treated as independent
variables.
A practical problem arises when using the ﬁlter technique to measure absorption
in Case 2 waters. The absorption by material retained on a ﬁlter, after subtraction ofabsorption by pigments, would represent the sum of absorptions by detrital material
and inorganic suspended material. No standard procedure exists now for separatingthese two components. But some statistical optimisation techniques that exploit the
known spectral form of absorption by detrital material may be envisaged, for achieving
this goal.
In modelling absorption by the yellow-substance component, variations in the magnitude of this component are parameterised through ay (λ0 ), rather than through a moreconventional measure of concentration.
2.7.4 Absorption by pure water
The absorption characteristics of pure water may, with greater conﬁdence, be considered invariant, although the optical properties of pure water have themselves beenreanalysed (Morel and Prieur, 1977; Smith and Baker, 1981; Pope and Fry, 1997). At thetime of writing this report, there is consensus in the community of optical oceanographers that the results of Pope and Fry (1997) represent the best measurements to dateof the spectral variations in the absorption coeﬃcient of pure water in the visible range.
These absorption measurements in pure freshwater are taken to be valid for pure seawater, since the dissolved salts in seawater are not expected to have a signiﬁcant impact
on absorption properties in the visible domain, given that their major absorption bands
are in the ultraviolet and infrared domains.

2.7.5 Back-scattering by phytoplankton
Theoretical studies and laboratory measurements have shown that phytoplankton
have extremely low back-scattering coeﬃcients (Bricaud et al., 1983). Recent reports(Morel and Ahn, 1991; Stramski and Kiefer, 1991; Ulloa et al., 1992) have drawn attention
to the importance of back-scattering by small organisms such as bacteria and viruses,
which complement and augment the back-scattering by phytoplankton. These studies
have pointed out that back-scattering by phytoplankton alone would be insuﬃcient to
explain the observed back-scattering, even in Case 1 waters, and suggested that backscattering by microscopic organisms covarying with phytoplankton may be responsible
for most of the observed back-scattering by marine organisms. Typically, this component42 • Ocean Colour in Optically-Complex Watersof scattering is parameterised as a function of chlorophyll-a concentration (Gordon et al.,
1988; Morel, 1988; Ulloa et al., 1994).
Another possible explanation for the observed back-scattering is that its source may
be small terrigenous particles brought to the ocean by rivers and winds (Kopelevich,
1983b). Recent works (Stramski, 1994; Zhang et al., 1998) have also pointed to the possible importance of back-scattering by bubbles in the ocean. Thus, some uncertainties
persist on the sources and mechanisms responsible for the observed back-scattering,even in Case 1 waters. At this stage, we do not know whether back-scattering by the phytoplankton component ought to be parameterised diﬀerently in Case 2 waters. Models
in use today treat this component similarly in Case 1 and Case 2 waters.
2.7.6 Back-scattering by inorganic suspended material
Bukata et al. (1995) have compared the limited number of existing spectra of the
back-scattering coeﬃcient for suspended particulates from Lake Ontario, Lake Ladoga,
and Chilko Lake along with spectra derived from Whitlock et al. (1981) for three rivers in
Virginia, USA. Some of these spectra showed a gradual increase toward lower wavelengths
in reasonable agreement with Morel (1973), who has suggested, based on theoreticalstudies, that the wavelength dependence of particulate scattering can be expressed bya λ−n law, with the exponent n varying from 0 to 2. Analyses of ocean-colour datafrom natural marine environments have also led to the conclusion that the wavelength
dependence of particle back-scattering can be expressed as a λ−n law, with the exponent
n varying from 0 in turbid coastal waters to 2 in oligotrophic waters (Sathyendranath
et al., 1989; Carder et al., 1999). But these are limited observations, and it may well bepossible that values of n outside of this range may be encountered in Case 2 waters.
Estimates of the properties of back-scattering by suspended material are made indirectly, either by theoretical calculations, or by analyses of ocean-colour data. Instruments
for direct measurement of back-scattering have only just begun to appear (Maﬃone andDana, 1997; Dana et al., 1998). They measure the volume scattering function at 140◦ ,which can be converted to a back-scattering coeﬃcient using empirical relationships.
Our knowledge of the scattering and back-scattering coeﬃcients of inorganic suspended material remains sparse. To develop remote-sensing capabilities in Case 2 waters, itwould certainly be desirable to improve our existing data base on the absorption andscattering spectra of inorganic suspended particles.
2.7.7 Back-scattering by pure water
Morel (1974) has studied and reviewed scattering by water. He has noted that scattering by pure water follows a λ−n law, and that the appropriate value for n would be 4.32.
Shifrin (1988) has also provided a detailed account of the study of molecular scattering
by water, and concludes, based on both theoretical considerations and experimental dataColour of Case 2 Waters •43from diﬀerent authors, that the appropriate value for n may lie between 4.05 and 4.35.
According to the tabulation of Morel (1974), bw , the scattering coeﬃcient for pure seawater, is 0.00288 m−1 at 500 nm, and the corresponding value for pure freshwater tabulated
is 0.00222 m−1 . Shifrin (1988), on the other hand, has concluded that the scattering coefﬁcient at 546 nm can increase from 0.00149 for fresh water to 0.00207 m−1 for seawaterwith a salinity of 35 psu. These results quoted from Shifrin (1988) are for water at atemperature of 20◦ C; he has also reported that scattering by water decreases slowly withtemperature. The back-scattering by pure water can make a noticeable contribution to total back-scattering under certain conditions (for example in yellow-substance-dominatedCase 2 waters), and hence the eﬀect of salinity on molecular scattering is worth someconsideration, if we are dealing with coastal waters or estuaries aﬀected by river run oﬀ.
The volume scattering function for water is symmetric with respect to the 90◦ angle,
such that back-scattering is always 50% of total scattering by water. In contrast, the backscattering is of the order of 1% of total scattering, for particles. Because water has thishigh ratio of back-scattering to total scattering, it often plays an important role in the
remote-sensing context, even when the total molecular scattering is only a small fractionof total particle scattering (Morel and Prieur, 1977).
2.7.8 Volume scattering function of aquatic particles
Monte Carlo models of ocean colour make use of the volume scattering function forparticulate matter in the ocean, rather than just back-scattering coeﬃcients (e.g., Gordon
et al., 1975; Kirk, 1981a,b; Morel and Gentili, 1991). Volume scattering functions ofmarine particles can be estimated by measuring the total volume scattering function ofwater samples. Subtracting the volume scattering function of pure water from the total
measured value yields the volume scattering function for particles. Direct measurementsof the volume scattering functions of aquatic samples remain sparse (but see Petzold,
1972; Morel, 1973; review by Kullenberg, 1974; and citations in Haltrin, 1997).
Kopelevich (1983b) has derived a model of volume scattering functions for particles,
which is designed to reproduce observed variations (Kopelevich, 1983a) in the shapeof the volume scattering functions in the natural environment. The model is based on
the assumption that the particle population consists of two components: a small-sized
inorganic component and a large-sized organic component. It has features that are consistent with the theory of particle scattering, and Shifrin (1988) notes that the model
results compare well with observations in a number of locations. But Mobley (1994) has
noted some diﬀerences between the Kopelevich (1983b) model and the observations ofPetzold (1972). Theoretical approaches based on Mie scattering have also been used to
examine variations in the volume scattering function of marine particles with changes inthe size distribution and refractive index of the particles (Beardsley, 1968; Beardsley etal., 1970; Pak et al., 1971; Gordon and Brown, 1972; Brown and Gordon, 1973; Kullenberg, 1974). Kullenberg (1974) has noted, based on comparisons between observations

44 • Ocean Colour in Optically-Complex Waters
and model calculations of volume scattering functions, that coastal regions may havevolume scattering functions that diﬀer from those observed in oceanic waters.
It is unfortunate that our understanding of the variability in the shape of the volume
scattering function in natural aquatic bodies remains somewhat limited. What we doknow is heavily based on studies from the nineteen sixties and seventies. An exception
is the recent work of Volten et al. (1998), who have reported on the volume scatteringfunction of two size classes of silt. More such studies are required to improve models of
scattering designed for Case 2 waters.

2.8 Properties of Inelastic Scattering
Raman scattering by pure water is generally accounted for in radiative-transfer calculations through bR , the Raman scattering coeﬃcient for water. This coeﬃcient has awavelength dependency similar to that of the elastic scattering coeﬃcient for water, i.e.,a λ−n dependency. Bartlett et al. (1998) have reported a Raman scattering coeﬃcient forpure water at 488 nm, of 2.7 x 10−4 m−1 , which compares well with a number of earlier
reports (Marshall and Smith, 1990). In radiative transfer calculations, bR (λ) is generally
expressed as:λr efbR (λex ) = bR (λr ef )
λex

n
,

(2.15)

where λr ef is some reference excitation wavelength at which bR is known, and λex is any
excitation wavelength. Raman scattering is characterised by a constant frequency shift
around 3350 m−1 (Marshall and Smith, 1990). According to Bartlett et al. (1998), n = 5.5,
close to the value of 5 reported by Sugihara et al. (1984).
When accounting for ﬂuorescence in radiative-transfer calculations, absorption coefﬁcients of ﬂuorescing components have to be known, and a ﬂuorescence quantum yield,
φF (λex , λem ), is applied to express the yield at which photons absorbed at a given wavelength (λex ) are re-emitted at a longer wavelength (λem ). Also, parallel computations ofradiative transfer must be carried out at excitation and emission wavelengths.
In the case of dissolved organic matter, φF (λex , λem ) maybe represented by a threedimensional function that follows a log-normal distribution both in the λex and λem
dimensions (Hawes, 1992). In the case of chlorophyll and phycoerythrin ﬂuorescence,φF (λex , λem ) generally varies with λem , following a Gaussian distribution. Some λex dependency may also be induced, mostly by the presence of non-photosynthetic pigments.
Typical values for φF range from 0.005 to 0.03 (dimensionless) for dissolved organic matter and from 0.005 to 0.05 for chlorophyll-a. Insuﬃcient data exist at present to provide
a plausible range of values for phycoerythrin ﬂuorescence.

Colour of Case 2 Waters •45

Fluorescence by dissolved organic matter inﬂuences the blue part of the spectrum,with a centre wavelength ∼ 430 nm, whereas phycoerythrin ﬂuorescence is centred around585 nm, and chlorophyll-a ﬂuorescence at about 685 nm. The Raman scattering inﬂuences the entire spectral range of interest; however this eﬀect reduces as the attenuationof the water column increases (i.e. Case 2 waters).

2.9 Issues Related to Atmospheric Correction
The signal received by the remote sensor, whether on aircraft or on satellite, contains an optical return from the atmosphere: it is a radiance term that obscures thetrue colour of the water body being remotely monitored. Many eﬀorts at remote sensing of aquatic targets have been compromised by atmospheric noise. A most obvious
case in point would be the obscuring of targets by clouds. But even under cloud-free
conditions, the interpretation of ocean-colour data can be compromised if variabilities in
the atmospheric signal are not suitably accounted for. A high-altitude aircraft or Earthorbiting satellite views one optical medium through another intervening optical medium.
Therefore, interpretation of ocean-colour data requires that we understand the physics
of light transmission through the atmosphere in addition to knowing the aquatic opticalprocesses.
Upon entry into the Earth’s atmosphere solar radiation undergoes absorption andscattering by air molecules and aerosol. (Aerosol is a collective term referring to suspended liquid and particulate matter that may exist in a variety of forms and shapes
from a myriad of diverse sources. Aerosol includes smoke, water and hydrogen sulfate
droplets, dust, ashes, pollen, spores, and other forms of atmospheric suspensions.) As aconsequence, two radiative components of diﬀering nature reach the surface of the Earth:
direct solar radiation, and diﬀuse solar radiation (generally referred to as skylight). The
direct radiation maintains the directionality of the sun’s radiation as it exists outside
the atmosphere, whereas the diﬀuse component reaches the sea surface from multiple
angles, as a consequence of scattering by aerosol and air molecules. Just as the aquatic
medium is deﬁned in terms of apparent and inherent optical properties, so also is theattenuating medium of the atmosphere.
Atmospheric correction procedures for ocean-colour data have to evaluate these atmospheric optical properties accurately and precisely, so that one may compute what
the ocean-colour signal would have been, in the absence of an intervening atmosphere.
Models of the atmospheric processes employed in the development of ocean-colour algorithms generally incorporate a treatment of Rayleigh scattering eﬀects (molecular scattering) and Mie scattering eﬀects (aerosol scattering).
Typically, atmospheric correction techniques for Case 1 waters rely on the fact thatthe water-leaving radiance approaches zero in the near-infrared domain. The signal thatreaches the satellite sensor in this spectral domain is analysed to infer optical properties46 • Ocean Colour in Optically-Complex Watersof the atmosphere in the same spectral domain, and the results are extrapolated to thevisible domain using optical models of the atmosphere (Gordon and Morel, 1983; Gordon,
1993; Stürm, 1993; Gordon and Wang, 1994; Gordon, 1997). This allows computation of
the water-leaving radiance in the visible domain.
This assumption of zero water-leaving radiance in the near infrared breaks down inhighly-scattering Case 2 waters (waters dominated by suspended particulates). This has
two implications: First of all, we have to improve our knowledge of the inherent opticalproperties of in-water constituents in the near-infrared domain, so that they may be usedin atmospheric correction techniques. Secondly, we have to recognise that atmosphericcorrection techniques developed for Case 1 waters may not be applicable in all Case 2
waters.

2.10 Concluding Remarks
The preceding review of the factors that inﬂuence ocean colour in Case 2 waters has,of necessity, been brief. It was designed to provide a glimpse of the reasons why ocean
colour in Case 2 waters is a much more complex phenomenon, relative to the colour ofCase 1 waters. In particular, we emphasize the following points:❖ In Case 2 waters ocean colour is inﬂuenced in a non-linear fashion by a number of
constituents in the water. In shallow waters, it is further inﬂuenced by the depth of
the water column, and by the nature of the bottom.
❖ Some of the inherent optical properties of the constituents that inﬂuence ocean colour
can be similar to each other. The similarity may persist over the entire spectral range
of interest, as in the case of the absorption spectra of coloured dissolved organic
matter and detrital particles. Or, the similarities may be pronounced over only a partof the spectral domain (e.g., typically, the absorption coeﬃcients of phytoplankton
and yellow substances decrease from about 440 nm to about 550 nm). Similarities
impede diﬀerentiation of the substances by remote sensing.
❖ The concentrations of the in-water constituents have a very wide dynamic range.
For example, chlorophyll-a concentration varies over four orders of magnitude, fromabout 0.01 to 100 mg chl-a m−3 . Concentrations towards the high end of the range
are more readily encountered in Case 2 waters than in Case 1 waters.
❖ Each of the three major components of the water that inﬂuence ocean colour in Case
2 waters (phytoplankton, other suspended particulates and yellow substances) represents a group of substances rather than a single substance. A consequence is somevariability in their optical signatures.
These are points that have to be borne in mind when developing algorithms for interpretation of ocean colour in Case 2 waters. In the next chapter, we examine the methods
that have been brought to bear to address the inverse problem of deriving information
on aquatic substances, given the ocean-colour signal from Case 2 waters.

Algorithms for Case 2 waters•

47

Chapter3
Algorithms for Case 2 Waters
A. Neumann, R. Doerﬀer, H. Krawczyk, M. D. Dowell, R. Arnone, C. O. Davis, M. Kishino,
A. Tanaka, C. Hu, R. P. Bukata, H. R. Gordon, J. Campbell, S. Sathyendranath

3.1 Introduction
Chapter 2 described the characteristics of Case 2 waters, and the basic interdependencies and relationships between coloured water constituents and the upwelling light
spectrum from the water body.
Algorithms are now available that are used on a routine basis for interpretation ofocean-colour data from satellites. These algorithms (e.g. those used to analyse datafrom satellite sensors SeaWiFS and OCTS), work on the general principle that the signal
received by the satellite can ﬁrst be corrected for atmospheric eﬀects, using detailed
models of radiative transfer in the atmosphere. The atmospheric correction procedures
typically rely on signals from the near-infrared (NIR) part of the spectrum, where thewater-leaving radiances are typically negligible in Case 1 waters. This allows the opticalcharacteristics of the atmosphere to be determined for the near infrared, and the results
are then extrapolated into the visible domain. The signals received by the satellite in thevisible are then corrected for the atmospheric contribution to the signals, and for themodiﬁcations of the signals at the sea surface by the intervening atmosphere, to obtain
the water-leaving radiances. The water-leaving radiances are then used to derive information on the main phytoplankton pigment, chlorophyll-a, using fairly simple empirical
relationships. It has been demonstrated that this type of approach works well in Case 1
waters, but they often break down in Case 2 waters.
Development of algorithms for Case 2 waters requires more than simple adjustments
to the Case 1 algorithms. In fact, the very assumptions that form the foundations of Case1 algorithms break down in Case 2 waters. For example, in some highly-scattering Case
2 waters and in some shallow waters, the water-leaving radiance in the near infrared canbe signiﬁcant. In such waters, the Case 1 assumption that the water-leaving radiance iszero in the near infrared would lead to errors in atmospheric correction, and hence in theestimated water-leaving radiances in the visible. As we saw in the previous two chapters,
a number of factors inﬂuence the colour of Case 2 waters, whereas Case 1 waters are48 • Ocean Colour in Optically-Complex Waters
typically treated as a single-variable problem. Remote sensing of ocean colour in Case
2 waters is a non-linear, multivariate problem, and the algorithms must be designed
accordingly.
This chapter provides an overview of approaches and algorithms that are currently
being developed to derive relevant oceanic quantities from satellite ocean-colour datafrom Case 2 waters. The following scheme (Fig. 3.1) can be used to explain the diﬀerent
approaches:
From remote-sensing measurements a set of spectral radiance or reﬂectance values(R1 . . . Rn ) is obtained which represents the apparent optical properties of the water body.
If atmospheric correction procedures have not been applied, these radiances will also
contain information on the state of the atmosphere. The number of spectral bands (n)is dependent upon the instrument used for the measurement. In addition to the spectral radiance or reﬂectance data, geometric information {G} is required to describe thegeometry of the detector and the viewing angle relative to the sun and the earth, for agiven pixel. Furthermore, auxiliary information {A} such as geographical region, season,
surface wind ﬁeld, and bathymetry maps may also be required. These input values canbe considered to be a vector of known values {I} describing a given pixel in the satellite
image. On the other side of the scheme are the physical quantities which are to be derived
from the input variables i.e. the concentrations of chlorophyll-like pigments (C), inorganic suspended particulates (S), and yellow substances (Y ) . Though other quantities may
sometimes be retrieved, the derived variables are primarily related to this main groupof constituents in remote sensing of Case 2 waters. Ocean-colour algorithms transformthe input vector {I} to the vector of unknowns {U}. These relationships are based on theinherent optical properties (IOP). In the simplest case, this may involve algorithms that
relate the ratio of reﬂectances at two wavebands to a derived property, such as chlorophyll. At the opposite end of the scale, sophisticated iterative procedures are applied
using extensive mathematical and physical models. In the next section the advantages
and disadvantages of these approaches are discussed.
3.1.1 Speciﬁc issues related to algorithms for Case 2 waters
Case 2 waters are primarily characterised by several optically-active substances whichvary independently of each other (see Chapters 1 and 2), and, in many cases, are accompanied by relatively high levels of scattering. Algorithms developed for Case 1 waters
are not applicable to Case 2 waters, and special Case 2 algorithms are required for thefollowing main reasons:❖ A greater number of variables inﬂuence the measured spectra; this necessitates speciﬁc algorithms to account for the multivariate characteristics.

Algorithms for Case 2 waters

Figure 3.1•

49

General scheme for ocean-colour retrieval algorithms. Here, (x, y) coordinates

represent the geographic location of the data. See text for details.

❖ Because of overlapping absorption and scattering spectra, variations in radiance (or reﬂectance) cannot be related directly to any one component. In general, all constituents
have to be solved for simultaneously.
❖ Inherent optical properties of diﬀerent waters are impacted by seasonal changes inthe dominant constituents of each class of substance; this requires algorithms that
account for these local and seasonal variations. This implies that no single, global
algorithm for Case 2 waters would work equally well in all regions (see section 3.2).
❖ In many instances, Case 2 waters are shallow coastal regions and inland waters, andwater depths less than ∼30 m may require special algorithms to account for bottom
eﬀects.
It is thus clear that Case 2 waters require more complex and sophisticated algorithms
than those developed for open-ocean (Case 1) waters.

50 • Ocean Colour in Optically-Complex Waters
3.1.2 Atmospheric correction over turbid waters
During the interpretative process, the atmospheric correction procedure is critical.
The performance of retrieval algorithms and the quality and accuracy of derived quantities are strongly inﬂuenced by atmospheric corrections. Classical atmospheric correction
schemes assume that the water-leaving radiance is zero in the near-infrared part of thespectrum. However, recent experiences with space-borne data (e.g. SeaWiFS and MOS),
and ship-based optical measurements, clearly indicate that this assumption is not valid
over turbid waters. The principal contributing factors are:❖ High concentrations of scattering constituents may cause the water-leaving signal
in the near-infrared part of the spectrum (wavelength λ>700 nm) to be signiﬁcantly
greater than zero; i.e., turbid waters are not “black” in the near infrared, as is generally
assumed for atmospheric correction procedures (see Fig.3.2).

Figure 3.2

Example of radiances at the top of the atmosphere in the two NIR channels of

MOS-IRS. Data collected on 1 April, 1997 over the Mouth of the River Thames, UK. The images
have been brightness-enhanced to provide a qualitative demonstration of the eﬀects of scattering
by suspended particles in the river. The distributions of radiances at these wavebands suggest
the inﬂuence of suspended particles from the river rather than that of aerosol. Data courtesy ofHarald Krawczyk, DLR, Berlin, Germany.

❖ Over shallow waters, some of the back-scattered signal from the bottom may be transmitted to the surface, causing a residual water-leaving radiance in the near infrared.
The non-zero water-leaving signal in the near infrared may result in an overestimation of
the aerosol optical thickness (which is typically derived from the signal in the NIR), andAlgorithms for Case 2 waters•

51

therefore, an atmospheric “over-correction” in the visible part of the spectrum. Someof the more recent atmospheric correction algorithms for Case 2 waters therefore account for the non-zero water leaving signal in the NIR by applying a coupled approachto separation of the atmospheric and aquatic contributions to the signal.
Figure 3.2 shows, as an example, the radiances at the top of the atmosphere in MOSIRS channels 9 (750 nm) and 11 (870 nm). The images clearly show the eﬀect of scatteringfrom suspended particles in the River Thames. In typical Case 1 algorithms, the waterleaving radiances in these channels would be taken to be zero. This can lead to the failureof the algorithms over turbid waters. In fact, it has been shown that the performanceof standard, Case 1 algorithms over turbid waters can be improved if the atmosphericcorrection procedures are modiﬁed to account for non-zero, water-leaving radiances in
the near infrared (Hu et al., 2000a; Ruddick et al., 2000; see also Fig. 3.3). Althoughsome algorithms have been developed for atmospheric correction over turbid watersthat use either iterative techniques (Arnone et al., 1998; Land, 1999; Lavender and Groom,
1999; Moore et al., 1999), or an integrated approach (Krawczyk et al., 1993; Kopelevich etal., 1998a), the problem requires more extensive research to provide reliable correction
procedures. Potentially complicated mixtures of diﬀerent aerosol types, which may occur
over inland or coastal waters, must also be accounted for with these procedures.
It is now recognized that standard algorithms for atmospheric correction over Case
1 waters fail in the presence of strongly-absorbing aerosol. Examples include windblown mineral dust and anthropogenic aerosol. The presence of such strongly-absorbing
aerosol can only be inferred in the visible, where multiple scattering is high. In such situations, one can no longer derive water properties by a two-step process — atmosphericcorrection followed by a bio-optical algorithm to estimate water properties. What is required instead is a single-step process that retrieves atmospheric and water properties
simultaneously. Chomko and Gordon (1998) have proposed a spectral optimisation algorithm to deal with this problem, which uses a non-linear optimisation technique. On
the other hand, Gordon et al. (1997) have used a spectral matching algorithm, in which
the optimisation is achieved by a systematic variation of all the unknown quantities in
the model. At present, these algorithms are used with the assumption that the waterleaving radiance in the near infrared is negligible, thus limiting the application to Case
1 waters. However, it is possible to relax this condition, at the cost of an increase inprocessing time. This would of course, make these algorithms more applicable in Case
2 waters. Thus, this is an area of research where Case 1 and Case 2 algorithms are on aconverging course, and both types of algorithms can beneﬁt from experiences gatheredby each other.

3.2 Desired Products
The spectral signature of the water-leaving radiance can be assessed directly by oceancolour remote sensing (once atmospheric inﬂuences are corrected for). This signature,52 • Ocean Colour in Optically-Complex Waters

Figure 3.3

Chlorophyll-a distribution in the northern Gulf of Mexico derived from SeaWiFS

data collected on 3 November, 1997. This pair of images illustrates some of the issues related
to the use of standard Case 1 algorithms for atmospheric correction over turbid coastal waters. (a) Note extensive white areas around the coastal regions which indicate failure of thestandard atmospheric correction algorithm over turbid waters. (b) Results obtained when theatmospheric correction procedure is modiﬁed to admit non-zero water-leaving radiances in thenear infrared. These results are obtained using neighbouring non-turbid pixels to derive some
optical properties of aerosol over the turbid pixels. This work focussed on issues of atmospheric correction over turbid waters, and not on problems related to in-water algorithms in Case 2
waters. Adapted from Hu et al. (2000a).

Algorithms for Case 2 waters•53

which describes the colour of the water, is determined by the inherent optical properties of the constituents i.e. spectral absorption and scattering properties (see Chapter 2).
Therefore, the ﬁrst group of derived products is the optical properties, which describe theoverall behaviour of the water body (e.g. diﬀuse attenuation coeﬃcient, and the inherent
optical properties of individual components such as the spectral absorption coeﬃcientof dissolved or particulate organic matter or scattering coeﬃcient of particles).
Many users of ocean-colour derived products, however, require data for biological,
ecological and oceanographic applications. The desired products here are the concentrations of the constituents rather than their optical properties (e.g. concentration ofchlorophyll-a). The concentrations can only be assessed indirectly by remote sensing,through the inﬂuence of their inherent optical properties on the spectral signature of thelight remitted from the water body. The concentration of a constituent of the water bodycan be calculated either from empirical regressions, or from bio-optical models, which
are implemented using experimental data on the spectral variations of speciﬁc opticalproperties. For Case 2 waters, the diﬃculty is that the relationship between ocean colour
and the constituents of the water may vary regionally or seasonally, according to thecomposition of algal species, size distribution of phytoplankton cells, mineral composition of the inorganic particles in suspension or the chemical composition of dissolved
organic substances. Therefore, Case 2 algorithms are typically local or regional in nature.
Some studies have paid attention to the question of phytoplankton species composition, and their impact on ocean colour. For example, blooms of coccolithophores can
now be tracked using colour-composites of ocean-colour data (Fig. 3.4). Algorithms arealso being developed for monitoring blooms of Trichodesmium (Subramaniam, 1999a,b).
Gege (1998) showed that the concentrations of some of the major algal species of Lake
Constance can be monitored, using ocean-colour data with very high spectral resolution
measured from a boat.
However, it remains to be established whether the results of Gege (1998) can be extended to satellite measurements, since atmospheric inﬂuence may mask ﬁne featuresin the water-leaving radiance. In situ measurements of spectral reﬂectance of a red tidein the North Sea (see Fig. 3.5) show some distinct features which demonstrate potential
for their identiﬁcation by remote sensing. Species composition is of great importance inbiological and ecological studies, and there is certainly some interest in developing thepotential of remote sensing to distinguish various types of phytoplankton. However, thistask is likely to be even more diﬃcult in Case 2 waters than in Case 1 waters, and we
focus here primarily on the task of distinguishing phytoplankton from other substancesin Case 2 waters.
Other quantities, such as primary production, can also be derived from satellite data,in addition to the primary products discussed above. These quantities are considered to
be secondary products, because, in addition to the derived “optical” data from satellites,54 • Ocean Colour in Optically-Complex Waters
additional information is required to compute them. Chapter 5, which covers applications, oﬀers a more detailed discussion on these secondary products. Table 3.1 gives an
overview of the primary products that may be derived from ocean-colour data over Case
2 waters.

Figure 3.4

Coccolithophore bloom in the English Channel oﬀ Brittany; image taken by

SeaWiFS on 26 June, 2000. Provided by the SeaWiFS Project, NASA/Goddard Space Flight Center,and ORBIMAGE.

In addition to products related to the water column itself, there is another groupof desired products related to the sea bottom: especially for shallow waters, there arealgorithms to derive bathymetry information, characteristics of the bottom itself and of
the macro-algae that grow on it. These are of particular interest to oceanographers andAlgorithms for Case 2 waters

Figure 3.5•55(a) Photograph of a red tide in the North Sea, taken from a light aircraft at analtitude of approximately 1000 m. The organism responsible for the red tide was not identiﬁed,
though Gyrodinium aureolum and Dinophysis spp. are known to occur in bloom concentrationsin this area. (b) Water-leaving radiance spectra (Lw /Ed ) of waters collected from diﬀerent areasof the North Sea. The position of the MERIS bands, as currently deﬁned, are also superimposed.
Red tides have reﬂectance spectra (red curve) that are very distinct from those from central
North Sea water (blue curve) or coastal waters (green curve). Data courtesy of Roland Doerﬀer,
GKSS, Germany.

56 • Ocean Colour in Optically-Complex Waters
biologists. However, no routine algorithms are in place today to address the shallowwater case. Issues related to shallow waters are discussed in Section 3.5, following thediscussion of the deep-water case.
Product
Optical properties

Remarks/Explanation

Scattering coeﬃcient

Necessary to improve atmospheric

due to inorganic

correction algorithms. Required,suspended material

especially in Case 2 waters, to improve(sediments)

light-transmission computations forbs (λ)

deriving products such as primary
production

Absorption of dissolved

Potentially useful information for bio-organic material

geochemical models. Also an essential(gelbstoﬀ)

component of light transmission models.

ay (λ)
Diﬀuse attenuation

Used in light-transmission models.

coeﬃcient Kd (λ)

Constituents of water

Absorption by

Of general use in biological oceanography.

phytoplankton

Particularly useful in primary-production

pigments ap (λ)

modelsChlorophyll-a

Main derived biological product fromconcentration,

ocean-colour data

Species composition
Inorganic suspended

Potentially important in Case 2 waters.

matterNeeded in studies of sediment transport.

Total suspended matter

Potentially important in Case 2 waters.
Needed in studies of sediment transport.

Other properties

Bathymetry and bottom

Shallow water applications,characterisation, Macro-

coastal management.

algae, Coral reefs
Table 3.1

Overview of products derived from ocean-colour data over Case 2 waters.

It is also essential to provide an estimate of the accuracy of the algorithms, in addition
to estimating the optical properties and the related concentrations of the constituents.
This is not easy to compute because of the many diﬀerent sources of error in the derived
products, such as:

Algorithms for Case 2 waters•

57❖ violation of the statistics underlying the applied model or experimental data set (which
may relate to assumed component composition, distribution functions for concentrations or covariance between components);❖ inadequate representation of the physics in the model used to derive the algorithm
for a particular region;❖ errors resulting from the atmospheric correction procedure;❖ calibration errors in the radiance data measured by the satellite sensor;❖ diﬀerences in the spatial scales of in situ and satellite observations; and❖ diﬃculty with obtaining exact temporal matches between in situ and satellite observations.
Only some of the sources of error listed above can be directly attributed to the interpretation algorithm. Other problems include, for example, practical diﬃculties with designing
an ideal validation experiment. However, it is possible to derive error budgets based either on a sensitivity analysis or using in situ validation measurements. Also, forward
computation of radiance spectra based on the retrieved concentrations compared withthe measured spectra may yield an indication of the accuracy of the retrieved variable.
We examine in some detail the speciﬁc algorithms used to address the deep-water case(waters where bottom inﬂuence can be considered to be negligible). This is followed bya discussion of the issues related to shallow waters.
Diﬀerent nomenclatures may be applied to classify existing algorithms and approaches based on the question to be answered and the approach used to address the question.
This chapter provides a comprehensive overview of the two major groups of algorithms:❖ Empirical approaches; and❖ Model-based approaches.
The following sections give a more detailed discussion of the principles, assumptions
and limitations of various types of algorithms.

3.3 Empirical ApproachAll empirical algorithms establish a relationship between optical measurements andthe concentration of constituents based on experimental data sets, i.e. measurements ofabove- or below-surface reﬂectance (or radiance) spectra and coincident measurements
of in situ concentrations. The most common relationship makes use of the so-called
colour ratio as described by equation 3.1:
R1p̂ = α
R2

β
+ γ,

(3.1)

58 • Ocean Colour in Optically-Complex Waters
where p̂ is the physical quantity to be estimated (e.g. chlorophyll concentration, suspended matter, yellow substances, and attenuation coeﬃcient) and Ri is the reﬂectance (orradiance) in spectral channel i. The coeﬃcients α, β and γ are derived from regressions
between the radiance ratios and the desired property and are based on experimental datasets.
A detailed discussion of such empirical algorithms can be found in Gordon and Morel(1983) and Sathyendranath and Morel (1983). The relatively-low accuracy usually foundin Case 2 conditions using algorithms of this type can be improved by employing morebands (multiple colour ratios or curvature algorithms) to cover a wider range of variability or to assess speciﬁc spectral features (Hoge and Swift, 1986; Cippolini et al., 1999).
Empirical algorithms for Case 2 waters can also be improved by using wave bands thatare more appropriate for the waters. For example, there have been reports that pigment
algorithms in high-chlorophyll Case 2 waters can be improved by using longer wavebands
than the typical blue and green bands used in Case 1 waters (Dekker et al., 1991; Gitelson,
1992; Sathyendranath et al., 1997a; Schalles et al., 1998). This decreases the inﬂuence ofyellow substances on the algorithms. The advantages of empirically-derived algorithms
are that they are simple, easy to derive even from a limited number of measurements(provided they cover the desired range of measurements), and easy to implement andtest. They have a short computing time due to their mathematical simplicity, and theyyield stable results, provided the scope of the algorithm is not violated. By their very
nature, empirical algorithms for Case 2 waters are always regional in scope.
There are, however, several limitations to empirical algorithms. The derived relationships are valid only for data having statistical properties identical to those of the data
set used for the determination of the coeﬃcients. These algorithms are thus particularly
sensitive to changes in the composition of water constituents (e.g. regional or seasonal
eﬀects). The resulting errors in derived products may quickly exceed acceptable limits.
A further problem is that the use of an empirical relationship, rather than a mathematical formulation, makes it diﬃcult to perform a systematic sensitivity analysis to obtain
estimates of the error budgets with respect to diﬀerent sources of error.

3.4 Model-based Approaches
A common feature of model-based algorithms is that they use bio-optical models to describe the relationship between water constituents and spectra of water-leaving radianceand reﬂectance, as well as radiative-transfer models to simulate the light propagation
through the water and the atmosphere (see Chapter 2). These models are used to generate sets of simulated spectra above the water surface or at the top of the atmosphere(TOA) for given ranges of concentrations for diﬀerent components in the water and for
diﬀerent states of the atmosphere. This information is then used to derive an algorithm,
which establishes an inverse mapping from the radiance or reﬂectance spectra to the desired in-water constituents. A variety of mathematical principles with diﬀerent degrees

Algorithms for Case 2 waters•

59

of sophistication may be used to implement such algorithms. An overview of approaches
developed to date is provided below.
3.4.1 Algebraic methods
The simplest of the model-based approaches is the one in which algebraic expressions are obtained that relate semi-analytic models of ocean colour to the geophysicalproducts to be retrieved. In this approach, an ocean-colour model is ﬁrst implemented
using empirical data on the spectral form of the inherent optical properties of the modelcomponents, and on the relationships between those properties and the concentrationsof the individual components in the model. It is this need for empirical data to implement theoretical models of ocean colour that earns them the epithet “semi-analytical”.
The model is then simpliﬁed, making use of approximations to reduce the number of
unknowns, or the inter-dependencies between unknowns. The result is a set of algebraic
equations that can be solved sequentially to obtain each of the unknown components ofthe model. If the relationships are non-linear, the solutions may be found either throughthe use of look-up tables or through bisection methods for reducing diﬀerences between
prediction and observation. Examples of this approach can be found in Carder et al.
(1999) and Lee et al. (1996, 1999). An example of output from this approach is shown in
Fig. 3.6.

Figure 3.6

Distribution of (a) chlorophyll-a, and (b) absorption by yellow substances at

400 nm, derived from SeaWiFS data of 2 June, 1998 using the algorithm of Carder et al. (1999).
The Carder algorithm showed good agreement with concurrent in situ observations, whereas
the standard Case 1 algorithm in use with SeaWiFS data tended to overestimate chlorophyll-aconcentrations. Results courtesy of Chuanmin Hu, Marine Science Department, University ofSouth Florida, St. Petersburg, Florida, USA.

60 • Ocean Colour in Optically-Complex Waters
It was shown in Chapter 2 that the RRS signal is a function of the ratio of the backscattering coeﬃcient (bb (λ)) to the absorption coeﬃcient (a(λ)), and that bb and acan in turn be partitioned into components that have characteristic spectral signatures.
Furthermore, each of the components of back-scattering and absorption (phytoplankton,
yellow substances, other particulate matter) can be linked to the concentrations of thecomponents through their respective speciﬁc optical properties. The spectral natures ofeach of these components are determined from empirical data. These relationships canbe redeﬁned and “tuned” to speciﬁc regions.
The advantage of this approach is that it makes use of the known optical properties for
speciﬁc components and couples them with theoretical models of RRS . Although there
are errors associated with the measurements used in the empirical relationships and withthe radiative-transfer models, the results appear quite accurate when applied to speciﬁc
Case 2 waters (Carder et al., 1999; Lee et al., 1996, 1999). The disadvantage of the method
is that it can be implemented only with a limited number of unknown model parametersand variables (concentrations to be retrieved). As the number of unknowns increases, theapproach has to be abandoned in favour of methods that allow for simultaneous retrievalof many unknown quantities. Some of these more sophisticated methods are discussed
next.
3.4.2 Non-linear optimisation techniques
The inversion of satellite radiances based on non-linear optimisation schemes provides an alternative approach. In this method, a forward model (see Chapter 2), whether
it be at the water level or at the TOA, is inverted directly by minimizing the diﬀerences
between the calculated values and the measured radiances. Diﬀerent methods are available to perform the minimisation, including techniques such as Levenberg-Marquardt andthe simplex algorithm (Nelder and Mead, 1965). The mathematical functions required toperform these minimisations are available to users through sources such as the NetLib(http://www.netlib.org), or the IMSL (International Mathematical and Statistical Library)from where Levenberg-Marquardt type of routines can be obtained and implemented.
The basic concept behind all of these non-linear optimization techniques is that theyattempt to minimize the χ 2 of diﬀerence between modelled and measured radiances, byvarying the concentrations of the variables that are provided as inputs to the model (i.e.
chlorophyll, total suspended material, yellow substances and aerosol optical thickness).
The χ 2 is deﬁned as follows:χ 2 = Σλ (Lsat − Lmod )2 ,(3.2)

where Lsat is the radiance measured by the satellite sensor, and Lmod is the modelled radiance, and the summation is taken over all wavelengths. A threshold for the convergenceof χ 2 is normally set to deﬁne the termination of the iteration procedure.

Algorithms for Case 2 waters•

61

This type of inversion technique is capable of reproducing the non-linear nature of
the modelled environment. Furthermore, the inversion method diﬀers from other methods such as the neural network or the principal component approaches (see the next
two sections), in that it does not depend on a pre-deﬁned, simulated data set. In approaches that depend on simulated data sets, the data are generated over a wide rangeof concentrations, and it is often diﬃcult to select ranges and frequency distributionsof concentrations that are representative of the natural variability of the oceanic constituents over the scene considered.
Various authors have attempted to apply such techniques to remotely-sensed radiance or to in situ reﬂectance measurements. Bukata et al. (1981a,b; 1991) undertooka study in Lake Ontario using information on the inherent optical properties speciﬁc tothat region and found good agreement between the modelled and measured values ofchlorophyll, suspended matter and dissolved organic carbon. Another important example is provided by Doerﬀer and Fisher (1994), who applied the simplex algorithm anda TOA model to retrieve chlorophyll, suspended sediment, coloured component of dissolved organic matter and aerosol path radiance, for CZCS data in a scene over the North
Sea (see Fig. 3.7). It was demonstrated that the frequency distribution of the retrieved
variables showed good agreement with those retrieved from in situ data collected at thesame time. Another use of such an inversion technique was presented by Roesler andPerry (1995) and by Lee et al. (1996, 1999), who used the Levenberg-Marquardt method
to retrieve inherent optical properties from in situ measurements of reﬂectance spectra.
The results showed a positive correlation with the measured values of the properties.
Garver and Siegel (1997) used the Gauss-Newton algorithm to retrieve oceanic components from ocean-colour data, and found excellent correspondence between measured
and estimated values of chlorophyll-a concentration.
An underlying assumption of the optimisation methods is that the forward modelis representative of the natural environment. However, this assumption is common to
all the inversion methods that are based on bio-optical models. Other sources of error
include the noise in the satellite measurements. However, methods now exist that include
these in the model calculation using error estimates. Considering these diﬀerent sourcesof error that are inherent to the method, it is clear that the χ 2 given above can only
be considered a “minimum error”. The map produced from the calculation of χ 2 can,however, be used to give an indication of the geographic variability of the convergenceof the algorithm.
Algorithms for interpretation of ocean-colour data based on non-linear optimisation
techniques provide simultaneous estimates of all the concentrations of the aquatic constituents, provided the inherent optical properties of the constituents in the water are
either known, or can be inferred. Such multivariate analyses can be seen as a tandem
operation involving both forward modelling activities to determine site-speciﬁc inherent
optical properties and inverse modelling activities that utilize these inherent properties62 • Ocean Colour in Optically-Complex Waters

Figure 3.7

Distribution of suspended matter, chlorophyll concentration and absorption byyellow substances in the southern North Sea derived from CZCS data for 6 September, 1979,
using a non-linear simplex optimization technique (Doerﬀer and Fischer, 1994). The algorithm
also computes aerosol path radiance at 670 nm. The model however, assumes a ﬁxed wavelength-dependence (constant Ångström coeﬃcient) for the aerosol component of the atmospheric signal. Images courtesy of Roland Doerﬀer.

Algorithms for Case 2 waters•

63

to extract the concentrations of water constituents from ocean colour. The forward modelling part of the exercise must be based on representative ranges of concentrations ofeach of the aquatic constituents, in the region under scrutiny. As discussed in Chapter 2,
there are gaps in our knowledge of the spectral inherent optical properties of some aquatic constituents, and poor assumptions regarding their optical properties in the forward
model can lead to poor performance of the algorithms.
Several aspects of this technique require attention during implementation. In particular, consideration should be given to the parameterisation of the forward model, toensure that the unknowns to be retrieved are as independent of each other as possible.
There may always be a certain degree of “local correlation” between the derived quantities, for example between chlorophyll-a and total suspended material, which may leadto ambiguities in the estimation of desired quantities. An appropriate selection of theinitial guess is also of prime importance. Where possible, adequate upper and lower limits should be set for each quantity to be retrieved. This acts as a guide to ensure thatthe correct minima are selected (in cases of multiple minima to the χ 2 , which implies
that multiple solutions are possible), and also accelerates the convergence time for eachpixel. Various approaches may be used to achieve this, including an empirical algorithm
for the initial guess, or using the value from the adjacent pixel.
A major inconvenience of the proposed inversion method is the substantial computation time required. This may be considerably reduced if due attention is paid to formulating the initial guess of the solution, and to the coding of the model. Computation
time will, however, remain signiﬁcantly high. On the other hand, model changes can bemade easily in this approach, which is an advantage if models are to be tuned for local
conditions. The next two approaches considered here avoid the high computation time
when processing satellite data.
3.4.3 Principal component approach
In all algorithms for interpretation of ocean-colour data, scattering and absorption in
the atmosphere must be accounted for, since they are responsible for a signiﬁcant portionof the radiance measured at the top of the atmosphere. In the classical approach, this isachieved by carrying out an atmospheric correction procedure to obtain the water-leaving
radiances. An alternate solution would be to use an integrated approach, in which theoptical properties of the atmosphere are taken to be additional variables in an inversion
problem. In this method, the initial points of the inversion are the radiances at the topof the atmosphere measured by an ocean-colour sensor. The quantities derived fromthese measurements are the three main constituents characterising the water quality i.e.
concentrations of chlorophyll-a, yellow substances, and inorganic suspended particles,
as well as the optical properties of the atmosphere. In this section we discuss one suchalgorithm intended for use in Case 2 waters, and capable of being tuned systematicallywith respect to diﬀerent inherent optical properties of the water body. To function in64 • Ocean Colour in Optically-Complex Waters
Case 2 waters, the algorithm must be able to separate the diﬀerent water constituentsusing a multi-spectral, multivariate data set. A further goal is a high computation speed.
The basic idea of the interpretation algorithm is to ﬁnd a piecewise-linear map between a set of spectral TOA radiances and a set of geophysical quantities. Although this
discussion is restricted to the derivation of chlorophyll-a, suspended particles and gelbstoﬀ, it is important to note that the algorithm is not limited to these quantities, andcan be modiﬁed to meet diﬀerent requirements. The algorithm development leads to thedeﬁnition of optimal weighting coeﬃcients (see Equation 3.3 below) and is closely related
to multivariate linear regression analysis. Special methods are required to analyse Case2 ocean-colour data, since the speciﬁc properties of the data result in a high correlation
between signals from diﬀerent wavebands. Principal component analysis (PCA) is oneof the methods that can be used to deal with this high correlation (Mueller, 1973, 1976;
Fisher, 1985; Sathyendranath et al., 1989; Sathyendranath et al., 1994).
Radiative transfer modelling is used to generate data sets of radiances at the topof the atmosphere corresponding to given variations of water constituents and atmospheric properties, and for the spectral characteristics of the sensor under consideration(Krawczyk et al., 1993, 1999). Then, PCA of the simulated data is used to determine
the spectral dimensionality of the data, and the weighting of each spectral channel required to retrieve the geophysical variables of interest. This approach accounts for thecorrelation between the signals in diﬀerent spectral channels, and enhances the potential discrimination and reconstruction accuracy of retrieved constituents, compared withthe use of subsets of available wavebands. A similar method was also used by Sathyendranath et al. (1989; 1994) to deal with interpretation of the water-leaving signal rather
than with the signal at the top of the atmosphere. In the method of Krawczyk et al. (1993;1999) all calculations are performed using error-normalized values, such that the inﬂuence of measurement accuracy on the quality of retrieval is automatically considered. A
linear estimation formula is derived to compute the geophysical variables from the TOA
measurements as follows:p̂ i =

kij Lj + Ai ,

(3.3)

j

where p̂ i is an estimate of the geophysical variable, e.g. pigment, aerosol-optical thickness; kij is the weighting coeﬃcient of channel j for variable i; Lj is the measured radiance in channel j; Ai is the oﬀset value for variable i; and j is the measurement channel
number, from 1 to n, where n is the number of spectral channels in the instrument.
One of the basic ideas is to apply the proposed algorithm directly to TOA radiancevalues and thus account implicitly for the atmospheric inﬂuence. The background to
this approach is that the application of an explicit atmospheric correction does not increase the information content with respect to water constituents. Although atmosphericAlgorithms for Case 2 waters•

65

scattering has a major inﬂuence on satellite data, the data also contain all signal variations caused by the water constituents. In other words, signal variations that are notresolved in the TOA data, cannot be resolved after atmospheric correction either. The
atmospheric correction merely achieves a kind of “dimensionality reduction”. Thus, it
should be possible to develop an algorithm that reconstructs water constituents from
TOA measurements. The algorithm is not discussed further in this report, but a detailed
description can be found in Neumann et al. (1995). Figure 3.8 presents a diagrammatic
illustration of the scheme.

Figure 3.8

Scheme of Principal Component Inversion (PCI).

Potential and limitations: The approach outlined here has a number of advantages anddisadvantages. The main advantage is the linear nature of the algorithm, which results
in a very simple, stable algorithm that can be implemented very rapidly (even for large
scenes), and which can be run on any computer system. For example, the full inversion ofa MOS-IRS scene to compute pigments, sediments, yellow substances and aerosol-optical
thickness takes only a few seconds on a Sun Sparc station.
Because of the linear nature of this algorithm, there are no convergence problems suchas those found in iterative inversion techniques. The high correlation of the data can be
attributed to the use of all available spectral channels. However, it should be noted thatan increase in the number of spectral channels does not necessarily result in an increasein the number of variables or parameters that can be retrieved. Rather, the discrimination between the diﬀerent variables (chlorophyll, sediments and yellow substances)

66 • Ocean Colour in Optically-Complex Waters
and the accuracy of interpretation, is enhanced. Another advantage of this approachis that the atmosphere is integrated in the same way as the water body. Atmospheric
and water-related parameters and variables therefore play an equal role. For this reason,an explicit atmospheric correction procedure is unnecessary. The atmospheric inﬂuence
will be automatically accounted for in the set of weighting coeﬃcients for the estimator(Equation 3.3). The algorithm can be optimized to take into account regional or seasonalvariations by applying regional optical models to derive the weighting coeﬃcients for theestimator.
The main limitation is due to the non-linearity of the dependence between the opticallysigniﬁcant substances and the radiances. The performance of the algorithm can therefore
be improved by applying a linear relationship to certain sub-ranges of the data, leading
to a segmentation of the parameter space. Additionally, auxiliary parameters (q) canbe used to account for the nonlinear behaviour. They are dependent on the original
geophysical variables (p) in a semi-logarithmic fashion:q = p + m log(p),(3.4)

where m is derived from model limitations.
This has two main eﬀects. First, for larger values, q tends toward the original geophysical variables, p. Secondly, for smaller values, where nonlinearity due to absorption
eﬀects plays a more important role, q tends toward a logarithmic dependence on p. This
also assures that the computed geophysical variables are always positive. This idea has
an analogue in the sigmoid function used in neural network algorithms.
Multivariate quasi-linear regression techniques form a powerful tool that can be easily
manipulated and implemented for deriving model-based, Case 2 algorithms. Results
obtained from MOS-IRS data over European coastal waters have proven the feasibilityof the approach (see Figure 3.9). Error analysis has demonstrated that an accuracy of
better than 30% can be achieved over a wide range of values for the retrieved variables(Krawczyk et al., 1999). It has also been demonstrated that it is possible to derive water
constituents directly from TOA measurements with the same level of accuracy. However,at this stage of algorithm development, the retrieval of gelbstoﬀ is still problematic.
A similar approach was developed by Levin and Zolotukhin (1997) and Levin et al.
(1998) based on an optimization strategy for selecting suitable spectral channels, theircentre wavelength and bandwidth for optimal retrieval of water constituents from oceancolour data.
3.4.4 Neural network approach
One technique that may be used to solve the inverse problem in hydro-optical remotesensing is the use of a neural network (NN) approach, which involves the inversion of theAlgorithms for Case 2 waters

Figure 3.9•

67

Water constituents and aerosol-optical thickness from MOS-IRS data over the Ger-

man Bight (2 April, 1999) derived using the principal components approach. This set of images
illustrates the decomposition of the individual components from the multivariate data set. The
underlying bio-optical model is a generic, three-component model, adapted from Prieur andSathyendranath (1981) and Sathyendranath et al. (1989). Images courtesy of Andreas Neumannand Harald Krawczyk, DLR, Germany.

relationship between reﬂectances in diﬀerent spectral bands, and the concentrations ofmultiple types of water constituents. For this purpose, the neural network is used asa multiple non-linear regression technique, and is thus related to the simpler case of alinear regression. The advantage of the neural network technique is that it combines
the detailed physical description of the remote-sensing process in the form of a forward
model, with a fast rate of application. In this section we describe brieﬂy the neuralnetwork technique in the context of diﬀerent regression analyses. Examples of usingneural network in optical remote sensing can be found in Doerﬀer and Schiller (1998),
Sandidge and Holyer (1998) and Lee et al. (1998b).
Linear regression analysis is used in remote sensing of Case 1 waters, to determine
the relationship between the logarithm of chlorophyll concentration, and the logarithmof the ratio of two bands in the blue-green spectral range. In Case 2 waters, this typeof linear regression can be used to derive high concentrations of suspended particulate68 • Ocean Colour in Optically-Complex Watersmaterial from a band ratio in the red region of the spectrum, provided that the eﬀect of
pigments and gelbstoﬀ is small.
When the reﬂectance in a band depends on more than one variable, as is the casefor waters containing pigments, yellow substances and suspended particulate material,a multiple linear regression can be used for a limited range of concentrations. Whenhigher ranges of concentrations have to be considered, non-linear regression techniquesmust be used, e.g. polynomials of a higher degree. Chebychev polynomials are one
way of determining the complex, non-linear relationship between multiple bands and theconcentrations of all three components. Another method is the neural network approach.
A regression can be determined either from a set of measurements (i.e. concentrations
and reﬂectances), or from a set of simulated reﬂectances. In the case of simulations using
a radiative transfer model, the inherent optical properties of the water constituents haveto be known. The relationship between the concentrations of the components and theinherent optical properties can be determined by linear or non-linear regression analysis.
Furthermore, assumptions about other properties, such as the vertical distribution of theconstituents, must also be made.
A neural network consists of a large number of nodes (or neurons) arranged in theform of input, and output layers, with a number of hidden layers in between. In a forwardfeeding neural network, each node of a layer is connected to the output of all nodes ofthe previous layer. All inputs to a node are weighted independently, summed with a bias
and fed into a logistic or other non-linear function. The output is then connected to allneurons of the next layer. The logistic function is particularly suitable for remote sensing,since the relationship between the logarithm of a concentration and the reﬂectance also
follows a logistic-like function. Since each node has diﬀerent coeﬃcients, the network
can describe almost any non-linear relationship.
These coeﬃcients (i.e. the weights and bias for all neurons) are determined in thelearning or training phase by minimizing the deviation between the concentrations used
for simulating the reﬂectances, and the concentrations produced by the neural networkwith the corresponding reﬂectances.
The determination of the coeﬃcients, i.e. the training, can be performed using different minimization techniques such as the back-propagation algorithm, which is commonly used. Problems arise when the inverse relationship between concentration andreﬂectance has more than one solution within the error range. The success of the learning step thus depends on avoiding such problems.
Furthermore, over-learning must be avoided. This occurs when the accuracy of the
network is higher at the training points than in between, which is analogous to a polynomial that describes the data, but does not interpolate the points correctly.
The resulting network can either be organized in the form of a subroutine code orin the form of a table of coeﬃcients. In the production phase, the network is used

Algorithms for Case 2 waters•

69

as a subroutine with reﬂectances and other information as input, and concentrations
as output. A warning ﬂag can be switched on if these input and output coeﬃcients lie
outside the training range. Other variables, such as the diﬀuse attenuation coeﬃcient for
downwelling irradiance at speciﬁc wavelengths, or Photosynthetically-Active Radiation(PAR, which is typically deﬁned as total radiation in the visible domain, from 400 to
700 nm), can also be simulated and included in the neural network.
Forward model: The success of a network depends primarily on the model used to produce the training table. It must describe as accurately as possible the optical propertiesof the water constituents and the radiative transfer. Two examples are presented fromthe numerous model options available.

Figure 3.10

Distribution of (from left to right) chlorophyll-a, yellow substances and suspend-

ed sediments over the Yellow Sea, derived from OCTS data collected on May 31st, 1997, using
the Neural Network approach of Tanaka et al. (1998). Atmospheric correction according toFukushima and Toratani, personal communication. Image courtesy of NASDA, Akihiko Tanaka
and Motoaki Kishino.

In the ﬁrst example, a simple two-ﬂow model is used to describe the underwater portion,
while a single scattering radiative transfer model is used to simulate atmospheric pathradiances and transmittances. Data from OCTS (on ADEOS I) have been analyzed using
this type of approach (Tanaka et al. 1998). Figure 3.10 shows an example of this work.
A more complex example is the Case 2 water algorithm developed for MERIS and MOS
which is based on radiance simulations using a Monte Carlo photon-tracing model, in70 • Ocean Colour in Optically-Complex Waterswhich the results are resolved according to sun and observation angles (see Fig. 3.11).
This model was used to avoid errors due to poor approximations of the factor Q (seeEquation 2.5), the parameter f (see Equations 2.8 and 2.10) and the shape factor s (seeEquation 2.10). This model also includes the eﬀects of a rough sea surface on water-air
transfer of upwelling ﬂux, and a complex incoming radiance distribution for diﬀerent solar zenith angles. A model based on the Monte Carlo technique cannot be used for direct
inversion using optimization techniques because of the extremely high computational
requirement. Fig. 3.12 shows an output from this algorithm applied to MOS data. This
example demonstrates that the neural network technique provides a unique possibility
to combine the advantage of a realistic, but computationally-intensive radiative transfer
model with the high speed of a neural network for processing satellite data. For a MOS
scene with about 1 million pixels, the processing time on a 300 Mhz PC is about one
minute.

Figure 3.11
Doerﬀer.

Flow chart of Neural Network algorithms for MERIS. Figure courtesy of Roland

Algorithms for Case 2 waters•

71

Atmospheric correction: To incorporate atmospheric correction into a one-step procedure, the neural network can either be trained using simulated reﬂectances at the topof the atmosphere, or use radiances from which the eﬀects of Rayleigh scattering byatmospheric gases have been subtracted. Alternatively, it is possible to perform the atmospheric correction separately with a neural network, and to derive the water-leaving
reﬂectances and radiances as an interface for the neural network for retrieval of in-water
constituents. Such a two-step neural network is described in the Algorithm Theoretical
Basis Documents (ATBD) for MOS (Doerﬀer and Schiller, 1999). In this case, the atmospheric path radiances and transmittances of the visible bands are derived from the TOA
reﬂectances in the near-infrared bands. For turbid waters, the water-leaving reﬂectancein the near infrared, caused by high concentrations of suspended particulate material,
have to be included in the simulations.

Figure 3.12

Distribution of suspended matter, chlorophyll-a and yellow substances in the

North Sea, north of the Elbe estuary. Derived from MOS data of 26 April, 1997 using neuralnetwork approach. Images courtesy of Roland Doerﬀer.

Interface: optical constituents and water constituents: The NN algorithm may be constructed with diﬀerent interfaces, for example, the following input options are possible:❖ TOA radiances or reﬂectances;❖ Rayleigh-corrected radiances or reﬂectances;❖ Directional water-leaving remote-sensing reﬂectance, after atmospheric correction;and❖ Normalised water-leaving remote-sensing reﬂectance, after atmospheric correction.
Optional outputs are the concentrations of water constituents or optical components
used as intermediate variables, which can be converted to concentrations using regional72 • Ocean Colour in Optically-Complex Waters
conversion factors (that will depend on regional information on the speciﬁc inherent
optical properties).
Assumptions and limitations: All assumptions must be included in the forward model
used for computing the training table. They consist primarily of the range of concentrations, any co-variance between the concentrations if used for training, the speciﬁc inherent optical properties, and the vertical distribution. When a simpliﬁed model is used forcomputing the training set, all assumptions made here, e.g. regarding irradiance-radianceconversion factors, are important factors in retrieval success. For an atmospheric correction neural network, the range of assumed aerosol optical properties is principally
the limiting factor. Furthermore, accuracies are limited by ambiguities that can occur inthe retrieval. It is important to ensure that these ambiguities lie within acceptable error
ranges. A drawback of neural nets is the time-consuming training. If either the model or
any of its parameters is changed, the neural net must be retrained.
Error determination and calibration sensitivity: Errors in the retrieval of a substancedepend on its concentration as well as the concentration of all other substances. The
error can be computed as a function of error in input, using a test set of computed
reﬂectances. In the case of a neural network that was trained with realistic assumptions
about the instrument and atmospheric correction errors, an accurate test data set should
be used to determine the consequences of this input error on the retrieval error. To
determine error propagation, the test data set must be computed using an error model.
Regional and seasonal applications: Since the neural network algorithm is very compact,
it is possible to prepare regional or seasonal neural networks, each of which is trainedusing those optical properties that are valid for the particular region or season. The
choice of a neural network may also be based on a water classiﬁcation with various
indicators (e.g. a coccolithophore bloom or a red tide).
Products: The output from a neural network algorithm is the concentrations of water
constituents (or scattering and absorption coeﬃcients of the major optical components),
and ﬂags which indicate whether the data lie outside the training range. An error map
may be produced that shows the error associated with each of the retrieved constituentson a pixel-by-pixel basis. For an independent atmospheric correction, neural network
maps of the atmospheric path radiance and the water-leaving radiance (or reﬂectance)
can also be produced.
Summary: needs, requirements and recommendations: The neural network is a versatile and powerful approach for retrieval of water constituents and also for atmosphericcorrection over Case 2 waters. It can combine the most sophisticated radiative transfer
model with the high processing speed required for mass production of satellite data andwould be particularly useful in real-time processing. The disadvantage is that a neuralnetwork is relatively expensive to prepare, especially when a sophisticated model is used
for computing the training and test data set. Additionally, both the design and training
require extensive experience. In general, the preparation of a neural network requires a

Algorithms for Case 2 waters•

73

forward model for computing a training-set and test data set and a neural network training procedure. Depending on the kind of forward model, the speciﬁc inherent opticalproperties a, b and the shape of the volume scattering function have to be known foreach of the constituents or optical components.

3.5 Bathymetry and Bottom Characterisation
Algorithms have been developed for estimating bathymetry in clear waters. In thiscontext, there are three properties to be estimated: water depth, bottom reﬂectanceand water clarity. The starting point in these exercises is the solution for reﬂectancein shallow waters, which was presented in Chapter 2 (Equation 2.11). The challenge ofusing this equation is that one needs an estimate of two of the three unknowns in thesolution, to calculate the third. For example, estimates of bottom albedo (A) and watercolumn properties (spectral reﬂectance and diﬀuse attenuation coeﬃcients) are needed
to calculate depth from the remotely-sensed data. Typically this is done by deriving
water-column optical properties from an adjacent deep-water area and assuming thatthe water properties are the same in the shallow waters, and by using bathymetric charts
or other ancillary data sources to ground truth at least a few points in the scene to obtain
initial estimates of bottom albedo.
Remote sensing of the shallow coastal environment requires high spatial resolution,
30 m or better, to be of practical value for navigation or other uses. The need for highspectral resolution in the remotely-sensed data has also been emphasised (Maritorena etal., 1994).
Lee et al. (1999) have proposed a method that can be used with hyperspectral data. They use a semi-analytic model (see Lee et al., 1998a) for remote-sensing reﬂectanceas a function of absorption, scattering, bottom albedo and depth. They use a predictorcorrector approach and optimize the result by minimizing an error function, with bottom
depth, bottom albedo and water properties retrieved simultaneously. They demonstrate
good results using simulated data as well as ﬁeld remote-sensing reﬂectance data collected with a ﬁeld spectrometer. It must, however, be noted that their model assumes asandy bottom with known spectral form of bottom albedo, and that the inversion technique was only applied over sandy environments.

3.6 Summary and Conclusions
Whereas in Chapter 2 the complexity of the optical properties of Case 2 waters was
discussed, in Chapter 3 we have tried to give an overview of algorithms and approaches
developed to cope with this complexity. Table 3.2 summarises the diﬀerent types ofalgorithms discussed here.

74 • Ocean Colour in Optically-Complex Waters
Property of

Empirical

Algorithm

Algebraic

Non-linear

Statistical/

NeuralSolution

Optimisation

PCA

Network

Input

Represent-

Bio-optical

Bio-optical

Bio-optical

Bio-opticalrequirements

ative set of

model

model

model ormodel orbio-opticalrepresentative

representativeand in situ

set of bio-set of bio-measure-optical and in

optical and inments

situ measure-situ measure-

ments

ments

Medium forHigh formodelling/modelling/training, lowtraining, low

for

for

computation

computation

Medium to

Medium to

high

high

Level of

Low

MediumHigh

complexity

Accuracy of

Low toretrievalmediumDependency on

No

No

No

Yes

Yes

Low

Low

High

Medium forHigh formodelling/modelling/training,

training,

low forlow forcomputation

computation

MediumHigh

simulated/training data sets
CPU load

Spatial scale of

Dependent

Adaptable

Adaptable

Adaptable

Adaptable

applicability

upon data

throughthroughthroughthrough(local vs. global)

set“local”“local”“local” LUTs

“local”

model

model

network

Table 3.2 Summary of characteristics of inversion techniques

In spite of the remarkable progress in this ﬁeld in recent years, some important issues
remain and merit emphasis. The algorithms discussed here are more complex mathematically and computationally, compared with the simpler algorithms developed for Case 1
waters. They deal with more variables and unknowns in a non-linear system. However,increasing the number of variables in the data and the model may make the mathematical solution of the inverse problem unstable, and iterative techniques, inverse modellingor simpliﬁcations underlying the algorithms may fail. A major source of the diﬃculties in remote sensing of ocean colour in Case 2 waters is the non-linear nature of theAlgorithms for Case 2 waters•

75

problem, and the similarities between the characteristic optical properties of individualcomponents to be retrieved. These problems would persist even if all the inherent optical properties of individual components and their relationships to their concentrations(through the speciﬁc inherent optical properties) were known without any uncertainty
or errors. But superimposed on the mathematical diﬃculties are the variabilities in theinherent optical properties themselves, as well as in the speciﬁc inherent optical properties.
If one were to use ocean colour only to retrieve the inherent optical properties, thenit would avoid any errors in the results arising from errors in the presumed relationships between inherent optical properties and their concentrations. Therefore, there isa discussion in the scientiﬁc community about whether algorithms should be limited toretrieving optical properties (spectral absorption and scattering, attenuation coeﬃcient)rather than concentrations of constituents. However, limiting the potential of bio-opticalmodels in this way would also limit the applications that ensue from them, and one cannot forget that the driving force behind remote-sensing is the applications. There is atpresent some feeling in the community that globally-valid applications for Case 2 waters
might be more easily developed if the results were limited to optical properties, and that
regional or seasonal algorithms might then be used to relate the optical properties to theconcentrations of constituents.
However, this argument ignores the possibility that regional changes in the composition of the constituents may also be accompanied by modiﬁcations in the spectral formsof their inherent optical properties. Nonetheless, it is certainly desirable that the bulk
inherent optical properties be derived from ocean colour data at least as an intermediate
product. This would be helpful in testing models and in isolating sources of error.
Although the overview of algorithms given here might not be comprehensive withrespect to all the implementations and mathematical solutions that have been developed
and used in various laboratories, we have at least tried to give a basic understanding of
the principal techniques that may be applied to the problem.
What general conclusions can be drawn from the discussion?
❖ Due to the complex optical behaviour of Case 2 waters, the algorithms used also
have to be more complex than those for Case 1 waters. They have to account for themultivariate nature of the problem.
❖ The retrieval needs more spectral information than for Case 1 waters, i.e., more spectral channels with higher spectral resolution. For some of the applications even hyperspectral data sets would be essential.
❖ A general approach to improving Case 2 algorithms (perhaps even a requirement) is
to invoke a priori information on the output variables that can help to optimise thesolution. Statistical relationships (the simplest case is the regression equation), or
some physical ideas (models) about the properties of the unknowns to be retrieved can

76 • Ocean Colour in Optically-Complex Waters
be used to help obtain the right solution. Ideally, regional or seasonal variations withrespect to composition of water constituents, inherent optical properties, variability
ranges, or correlations between parameters and variables have also to be accountedfor.
❖ Optimization of Case 2 algorithms demands extensive and improved measurements
of inherent optical properties for a wide range of water bodies, resulting in comprehensive libraries of optical properties. The spectral range needs to be extended into
the near infrared, and additional quantities such as the volume scattering function
must also be included.
❖ Testing and validation of Case 2 algorithms will require optical protocols that are more
stringent and more extensive than those established for Case 1 waters. This would
facilitate intercomparison of algorithms and results. Protocols should be establishedfor calibrations of optical instruments and for sample preparation and measurement.
❖ Quantitative Case 2 products from satellites require that sensor calibration be maintained on a continual basis. Absolute sensor radiances are required for atmosphericcorrection and inversion of the RRS signature to obtain in-water constituents.
In general one can state that, during the last years, promising developments of algorithms
speciﬁcally designed for Case 2 waters have taken place. However, the algorithms are still
far from being operational at global scales. Although the general approaches to solving
the problem could be demonstrated, the algorithms as well as the underlying aquaticand atmospheric optical models still need signiﬁcant improvement to ensure acceptable
retrieval accuracy on an operational basis. But, based on existing investigations andexperimental results, it can be stated that retrieval errors less than 30% are realistic, andcomparable to the stated goals of error limits in Case 1 waters.

Measurement Requirements for Case 2 Waters•

77

Chapter4
Measurement Requirements for Case 2 Waters
C. O. Davis, J. T. O. Kirk, J. S. Parslow, S. Sathyendranath

4.1 Introduction
We have seen in Chapter 2 that the ocean-colour signal from Case 2 waters is a complex, non-linear function of a number of independent variables and parameters. This automatically imposes the need for sophisticated mathematical and statistical techniques
in dealing with the inverse problem of retrieving geophysical variables such as phytoplankton concentration (and possibly the type of phytoplankton present), concentrationof suspended sediments and yellow substances from the remotely-sensed signal (Chapter
3). The envisaged applications of the remotely-sensed data in Case 2 waters are also wideranging (discussed in Chapter 5), and speciﬁc applications introduce their own speciﬁc
requirements of the remotely-sensed data, with respect to the time and space scales ofmeasurements. The successful use of remote-sensing data under these diﬃcult circumstances demands careful planning, meticulous measurements, and constant vigilance. In
the absence of validated algorithms for Case 2 waters it is appropriate to treat each datacollection as an experiment designed to develop and validate appropriate algorithms.
Therefore, whenever possible, remote-sensing data should be collected together with insitu data, and one should pay due attention to the following steps:❖ Choice of a remote-sensing device and platform, and sampling strategy: The speciﬁcations of the sensor and the spatial and temporal coverage of the data should beappropriate for the applications envisaged.
❖ Selection of the appropriate in situ measurements to be made in support of theremote-sensing campaign: In situ measurements are necessary both in the development of algorithms, as well as for subsequent validation of the accuracy and precisionof geophysical variables that are retrieved from the remotely-sensed data.
❖ Validation measurements and error analysis: The remotely-sensed data and the in situ
observations must be subjected to careful and rigorous comparisons. The object ofsuch comparisons would be to establish the extent of validity of the results, to evaluate

78 • Ocean Colour in Optically-Complex Waters
their accuracy and precision, and to ensure that the quality of the remotely-sensed
data meets the requirements of the application.
In this chapter, we examine these three facets of a remote-sensing experiment, in theparticular context of remote sensing of ocean colour in Case 2 waters.

4.2 Remote-Sensing System Design Requirements
Ocean-colour remote-sensing instruments designed for Case 2 waters must meet four
additional requirements compared with the sensor requirements for a Case 1 oceancolour sensor as summarized in IOCCG Report No. 1 (1998). First, additional spectral
channels are required for the measurement of chlorophyll ﬂuorescence, inorganic particles in suspension, and shallow bottom reﬂectance. Second, one or more additional
channels are required for atmospheric correction over shallow or sediment laden waters.
Third, increased dynamic range is required so that the sensor does not saturate when
imaging the beach or other bright objects that are typical of a coastal scene. Hu et al.
(2000b) also note the need for increasing the precision of the data collected by satellites
by increasing the number of digitisation bits, to avoid “speckling” and other problems
in various derived products resulting from digitisation errors. They note that some ofthese problems may be more severe in turbid waters than in clear, open-ocean waters.
Finally, coastal features are more complex spatially than those of the open ocean, andhigher spatial resolution is required. Additionally, the coastal ocean can be extremely
dynamic and some applications may require daily or hourly sampling. First we review
the general requirements for an ocean-colour sensor designed for Case 2 applications.
Spatial resolution and sampling frequency are then discussed in relation to speciﬁc applications. Because of the diversity of applications envisaged (see Chapter 5), one sensor
system may not be appropriate or suﬃcient for all applications.
The ﬁrst IOCCG report (IOCCG, 1998) speciﬁed the minimum requirements for an
ocean-colour sensor designed for Case 1 applications. Case 1 waters can be seen as a
special case of Case 2 waters. It follows that a sensor for Case 2 waters must meet all
the requirements for Case 1 waters, as speciﬁed in the ﬁrst IOCCG report. Furthermore,
remote sensing in Case 2 waters requires additional channels over and above the minimal
set of ﬁve to six channels recommended for Case 1 waters (IOCCG, 1998). The additional
channels would be for the measurement of chlorophyll ﬂuorescence, inorganic suspended particles, and bottom reﬂectance. The additional spectral requirements are discussed
next.
Chlorophyll-a concentration is often high in Case 2 waters, ranging from less than
1 to 100 mg m−3 or higher. At these high chlorophyll levels, there would be a strong
ﬂuorescence signal from chlorophyll-a, centred at around 685 nm. The satellite sensors
MODIS, MERIS and GLI have all incorporated narrow spectral bands at around 680 nm andMeasurement Requirements for Case 2 Waters•

79

665 nm to measure the height of this ﬂuorescence peak. Fluorescence per unit chlorophyll varies greatly, and thus there is some uncertainty about the interpretation of thissignal. However, it does provide an indication of the abundance and productivity ofphytoplankton.
The characteristic signal from suspended particulates is high back-scattering overa broad spectral range. Superimposed on this spectrally-ﬂat signal is the eﬀect of absorption by phytoplankton pigments and yellow substances at wavelengths shorter than
500 nm, and by water at wavelengths longer than 600 nm (Figure 4.1). Figure 4.1 is asimulation of the remote-sensing reﬂectance that would be measured over a coral-sandbottom in very clear waters, of depths ranging from 1 to 30 metres. The case of inﬁnitely
deep water is included for comparison. The spectral signature from waters with a large
amount of coral sand suspended throughout the water column would be very similar
to the spectra from one-metre-deep waters. The signature of the suspended particles
is strongest in the 550 to 600 nm spectral range. Even in moderately-turbid waters (e.g.
chlorophyll-a of 4.0 mg m−3 ), the bottom signature is easily detected in this spectral
range, to a maximum depth of about 10 m. Narrow (10 nm wide) spectral channels centred at 570 and 590 nm are useful for detection of suspended particles and bathymetry.
The slope of the signal between these two channels changes dramatically as a functionof depth because of the much greater absorption by water at 590 nm compared with
570 nm. If the reﬂectance of the bottom is known, or can be estimated from a spectral
library, then the bathymetry can be calculated from these channels. Conversely, if thebathymetry is known, the bottom reﬂectance characteristics can be estimated from thespectral data.
The addition of a channel around 1040 nm was recommended in the ﬁrst IOCCG Report (1998) to aid atmospheric correction in the presence of whitecaps and foam. For
similar reasons, one or more additional channels in the near infrared are recommended
for atmospheric correction over sediment-laden or shallow waters. Waters with high particle loading (sediments or very high levels of phytoplankton) have signiﬁcant reﬂectanceat 670 and 760 nm (or even at 870 nm; see Fig. 3.2). Water absorption increases dramatically with increasing wavelength. Channels in the atmospheric transmission windows at
1040 and 1240 nm will not have a measurable water-leaving radiance over even the most
turbid waters, and thus would be suitable for atmospheric correction over turbid Case 2
waters.
Many coastal regions are urbanized, and absorbing aerosols are often present overcoastal waters. An additional spectral band at 380 nm may be useful for characterizing the presence of absorbing aerosol. If a typical marine aerosol is assumed when anabsorbing aerosol is present, the atmospheric correction will remove more atmospheric radiance than is actually present, resulting in negative water-leaving radiances in the
shorter wavelengths. An additional spectral channel at 380 nm may be helpful for distinguishing the presence of, and correcting for, absorbing aerosol. This channel may80 • Ocean Colour in Optically-Complex Waters

Figure 4.1

Above water remote-sensing reﬂectance curves generated using a commercial soft-

ware package (HYDROLIGHT 3.0, Sequoia Scientiﬁc, USA) for numerical computation of radiativetransfer under water. The computations assume a coral sand bottom and chlorophyll-a concentration of 0.1 mg m−3 . Water depths are 1, 2, 3, 4, 6, 8, 10, 14, 20, and 30 m. A water column of
inﬁnite depth is included for comparison.

also prove useful for the measurement of yellow substances, which absorb strongly at
380 nm. A third application may be for the detection of red-tide blooms caused by dinoﬂagellates. These phytoplankton have a characteristic absorption in this region due
to photoprotective pigments, which can be used as a diagnostic for that class of phytoplankton (Kahru and Mitchell, 1998).
The signal-to-noise ratio for Case 2 sensors should be similar to those of other oceancolour sensors (Figure 1, IOCCG, 1998). Case 2 waters with a high particulate load will
have a higher remote-sensing reﬂectance than Case 1 waters. However, Case 2 waters
with high levels of phytoplankton, or high levels of CDOM, will have a very low signal
in the 380 to 440 nm spectral range, requiring increased sensitivity and signal-to-noise
ratio. Many Case 1 ocean-colour sensors achieve the required high signal-to-noise ratio
by allowing the sensor to saturate over clouds or other bright objects. However, forCase 2 waters, the sensor must not saturate over clouds or the coast, as these features
are found in most coastal scenes, and it is essential to image the shallowest waters andadjacent beach areas for many of the proposed applications. This requirement can be
met with modern detectors and careful instrument design. Array detectors allow the useof several detector elements per spectral channel. Judicious summing of those pixels,
both on the detector chip and after readout, can achieve high signal-to-noise ratio andMeasurement Requirements for Case 2 Waters•

81

very high dynamic range. These high-dynamic-range instruments require 12- or 14-bit
digitization to record the data with suﬃcient accuracy over the full range.
Calibration requirements for ocean-colour sensors for Case 2 waters are similar to
those for Case 1 sensors (IOCCG, 1998). Prior to launch, ocean-colour sensors for Case
2 waters should be calibrated to best laboratory practices. This includes spectral andradiometric calibration and the full characterization of the sensor to detect any artifacts,
such as stray light, and the correction of such artifacts by modiﬁcation of the hardware,or appropriate calibration software. Aircraft sensors should be re-calibrated in the laboratory before and after each major ﬁeld campaign. Ideally they should also have a ﬁeld
or in-ﬂight calibration system such that at least a relative calibration can be completed
daily to assess the stability of the sensor.
Routine on-orbit calibration is essential for satellite sensors. On-orbit calibration using the moon is ideal because it allows viewing of a target with known reﬂectance, without
the need to correct for viewing through a variable atmosphere. The SeaWiFS programmeis presently using moon viewing for calibration, with excellent results (Barnes et al., 1999).
The programmes for satellite sensors MODIS and NEMO are also planning to use moon
imaging. As an added beneﬁt, this approach would provide a cross calibration of thesesensors. Case 2 sensors should also routinely image well-characterized, open-ocean, reference sites for calibration and cross calibration between sensors. These sites have large
uniform ﬁelds with measured reﬂectances that are excellent for checking the combination of calibration and atmospheric correction. Additional well-characterized land sites,such as some dry lake beds, are useful for checking performance over bright targets. Finally, routine imaging of well-characterized, representative, coastal sites with measured
biological and optical properties is essential for algorithm validation.
The spatial resolution, ﬁeld-of-view, and sampling frequency requirements for sensors
for Case 2 waters is closely related to the planned applications (Table 4.1).
Requirements for spatial resolution vary widely, depending on the application. Imaging areas that include coastal or bottom features or manmade objects (e.g. ﬁsh farms)
require 30 m or better resolution. Two planned spaceborne systems, ARIES and NEMO,
will provide 30 m resolution hyperspectral data. NEMO is speciﬁcally designed with a highsignal-to-noise ratio suitable for Case 2 water applications. Numerous aircraft systems
are also available providing resolutions from 1 m to 20 m for a variety of applications.
Coarser resolution systems are ﬁne for many Case 2 water applications requiring
imaging of water features. The high-resolution modes of MERIS (300 m) and GLI (250 m)
may be particularly useful for studies of coastal features that require better than 1 km
resolution, as well as large-area coverage. The Special Event Imager (SEI) is a proposed
imager for a geostationary satellite. It would have 300 m resolution and the ability torevisit a site as frequently as every 30 minutes for studies of fronts, river plumes, redtides or other dynamic features.

82 • Ocean Colour in Optically-Complex Waters
Applications/Issues

Spatial Resolution x

TemporalExamples of suitable

ExtentResolution

platforms/sensors

River plumes(30 m – 1 km)

Hours – weeks

GLI, MERIS, MODIS,

outfalls

x (300 m – 100 km)

Tidal plumes, jets,(100 m – 1 km)

frontal dynamics

x (1 km – 10 km)

HAB, aquaculture,(100 m – 1 km)

coastal water qualityx (1 km – 100 km)

Bathymetry and shallow(1 m – 30 m)

benthic habitat:x (1 km – 100 km)

NEMO, SeaWiFS
Hours

Airborne, SEI

Days – weeks

GLI, MERIS, MODIS,NEMO, SeaWiFS

Weeks – months

Airborne platforms,
ARIES, NEMO

distribution, status
Maritime operations:(30 m – 1 km)

navigation, visibility

x (30 km – 100 km)

Oil spills(100 m – 1 km)

Hours – days

NEMO, SeaWiFS
Hours – daysx (1 km – 100 km)
Operational ﬁsheries

1 km x 1000 km

MERIS, MODIS,Airborne, MERIS,MODIS, NEMO, SEI

Days

oceanography

GLI, MERIS,MODIS, SeaWiFS

Integrated regional(30 m – 300 m)

managementx (30 km – 300 km)

Days

MERIS, NEMO

Table 4.1 Examples of coastal ocean-colour application areas, requirements for spatial resolution and extent, temporal resolution, and examples of current or planned platforms and sensors
meeting those requirements. Additional details about the instruments can be found in IOCCG
Reports No. 1 and 2.

For large shelf features, 1 km resolution imaging is suitable. Although none of the existing and planned sensors have all of the features desirable in a Case-2 sensor, MERIS,MODIS and GLI have the Case 1 band set plus additional bands suitable for measuringchlorophyll ﬂuorescence and for atmospheric correction over Case 2 waters, and bands
that will likely be useful for measuring suspended sediments. Imaging at a resolution of
1 km allows for imaging an entire continental shelf and is particularly useful for addressing eﬀects of large scale ocean forcing (e.g. El Niño eﬀects) on coastal ocean dynamics.
The coastal ocean is very dynamic and some processes, e.g. tidal dynamics, require
several samples per day. Characteristics of the remote-sensing platform control the frequency of resampling.
❖ A single polar-orbiting satellite will give at best one image every 2–3 days.
❖ Two polar orbiters can give daily coverage.

Measurement Requirements for Case 2 Waters•

83❖ An imager on a geostationary satellite (> 300 m resolution) could give 30-min repeat
coverage for areas in its ﬁeld-of-view.
❖ Aircraft are suitable for special studies which require frequent resampling of a particular site or feature.
A mix of sensor systems will be required to image properly the full range of processes
and features found in Case 2 waters. Nested sampling using large-area coverage at 300 to1000 m resolution is particularly useful for providing the oceanographic context, whereas
100 m or higher resolution imaging is useful for detailed studies of the feature of interest.
Sampling schemes should always be planned assuming that considerable data will
be lost due to cloudiness. The global average cloud cover is 50%, therefore a good ﬁrst
estimate is to plan to sample at least twice as often as minimally required for the plannedexperiment or application.

4.3 In situ Data for Model Parameterisation and Validation
A primary research goal over the next few years is to demonstrate the use of aircraft
and spaceborne ocean-colour imagers for characterizing coastal-ocean optical properties
and to validate a basic set of oceanographic products derived from remotely-sensed data.
Achieving that goal will require conducting a series of validation experiments at a number of coastal sites representative of the major coastal environments. Each experimentshould include overﬂights with the remote-sensing system of interest together with insitu measurements needed for algorithm development and validation.
4.3.1 Composition and optical properties of seawater
The aim of ocean-colour remote sensing is to characterize the radiant ﬂux that emerges
from the ocean, and then by analysis of that ﬂux (its overall intensity and spectral distribution), to derive information on the constituents present in the water, particularly
phytoplankton, yellow substances, and suspended sediments. As we saw in Chapter 2(Equation 2.8), the magnitude of the emergent ﬂux per unit incident solar energy increases as light scattering in the water increases, and decreases as the absorption in the water
increases, approximately in accordance withbb (λ)

R(0, λ) = f a(λ) + bb (λ)

,

(4.1)

where R(0, λ) is the upward irradiance normalized by the downward solar irradiance justbelow the sea surface at wavelength λ, bb (λ) is the back-scattering coeﬃcient, a(λ) is theabsorption coeﬃcient, and f is a proportionality factor which is somewhat variable. It
is the diﬀerential absorption at diﬀerent wavelengths, with some contribution from the84 • Ocean Colour in Optically-Complex Waters
spectral variation in scattering, that gives the emergent ﬂux its characteristic spectralenergy distribution.
Thus, it is the inherent optical properties of the water (i.e. the spectrally-varying
absorption and scattering coeﬃcients) that determine the characteristics of the emergent
ﬂux, and it is by close examination of this ﬂux that we hope to learn something about
those constituents of the sea that determine these optical properties.
A number of diﬀerent calculation procedures, or algorithms, are being developed
for retrieval of the spectral distribution of water-leaving radiance from the remotelysensed radiance above the atmosphere, which are then used to arrive at estimates of theconcentrations of the optically-signiﬁcant components of the water (see Chapter 3). All
these methods require as inputs for their development, sound data on the absorption
and scattering properties of the components of the medium.
The constituents about which information is required in Case 2 waters include: phytoplankton of various taxonomic groups, coloured dissolved organic matter (CDOM), organic detrital particles, and resuspended sedimentary and river-borne terrigenous particles
of various mineralogical classes. It is generally accepted that the development of a single
remote-sensing algorithm applicable to all Case 2 waters may not be possible, but thatalgorithms eﬀective for a particular constituent or particular locations are entirely feasible. This will however require input optical data obtained speciﬁcally from the locationsin question.
When an algorithm has been developed and implemented, it will need to be validated.
In other words, the calculated output data on the distribution of optical properties, oron the concentrations of phytoplankton, yellow substances and suspended particles, will
have to be tested against direct in situ measurements.
Thus, both for the initial development of algorithms, and for their validation when
they are put to use, reliable methods for measuring the inherent optical properties ofseawater constituents and their speciﬁc values (values per unit concentration) are essential. Algorithms and protocols have been developed for the measurement of these
constituents and for the measurement of the apparent and inherent optical propertiesof seawater. These methods are summarized in Mueller and Austin (1995), Fargion andMueller (2000), and references cited therein. It is not the purpose of this section to repeat
those detailed protocols, but rather to highlight the applicability of speciﬁc methods tothe particular problems encountered in Case 2 waters.
4.3.2 Measurement of absorption
To achieve our aims, whether for data input or model validation, we need not only
to measure absorption, but also to determine how the absorption is distributed between
the diﬀerent components present, e.g. between CDOM, phytoplankton, and non-living
particulates. There are three approaches to making these measurements. The ﬁrst isMeasurement Requirements for Case 2 Waters•

85

a combination of the ﬁlter technique for absorption measurements for the particulate
absorption and a spectrophotometer method for measuring absorption by yellow substances. The second approach is to use an in situ absorption meter. The third approachis to use an integrating cavity meter. Each approach has advantages and disadvantages
for particular types of Case 2 waters, which are discussed in the following paragraphs.
The most commonly-used approach (Yentsch, 1962) to the measurement of the absorption coeﬃcient of particles is to concentrate them ﬁrst on a glass-ﬁbre ﬁlter. The
spectrum is then measured on the ﬁlter, using a clean moist ﬁlter as the blank. The
problem with this method is that a correction factor has to be established to eliminate
the path-length ampliﬁcation of the light beam by scattering on the ﬁlter. Empiricalequations do exist to carry out this correction (Mitchell and Kiefer, 1988a; Bricaud andStramski, 1990; Cleveland and Weidemann, 1993; Hoepﬀner and Sathyendranath, 1993).
However, the large correction involved is attended with signiﬁcant uncertainty. A recent
study by Roesler (1998) suggests that much of this uncertainty can be removed if particular care is taken to subject the blank ﬁlter to exactly the same ﬁltration procedureas the sample ﬁlter. An alternative to measuring particle spectra on a glass-ﬁbre ﬁlter is
to collect the particles ﬁrst on a membrane ﬁlter and then resuspend them in water by
a freeze-transfer method (Allali et al., 1995). This avoids the absorption ampliﬁcation
problem but is more laborious and subject to possible loss of material during the transfer
process.
The next step is to distinguish between particle absorption due to phytoplankton andthat due to organic detritus or mineral particles. To accomplish this the phytoplankton pigments are extracted from the ﬁlter, and the residual spectrum (presumably nonphytoplanktonic particulate matter) is then measured (Kishino et al., 1985). Instead ofusing methanol or acetone to extract the phytoplankton pigments, hypochlorite (to whichorganic detritus and mineral colour are apparently less sensitive) can be used to bleachthe pigments (Tassan and Ferrari, 1995). To obtain absorption by yellow substances, theﬁltrate is collected and its absorption measured in a spectrophotometer with a 10 cm
pathlength. This approach is highly eﬀective for some Case 2 waters, such as the Baltic
Sea, where concentrations of yellow substances are very high. However, in many Case 2
waters the concentration of yellow substances is too low to be measured accurately by
this method.
The second approach is to use an absorption meter which uses a reﬂective tube inwhich the scattering error is minimized and can also be corrected for, and in which sensitivity is enhanced by use of a longer pathlength. This approach works well in many
waters and an instrument of this type is commercially available (Moore, 1994; Twardowski et al., 1999). The commercial instrument “ac-9” measures absorption and beam
attenuation at 9 wavelengths, whereas a new version (HySTAR) measures a and c witha spectrometer over the 400 to 800 nm range. The longer pathlength (25 cm) makes accurate measurements of absorption possible for most Case 2 waters. The chlorophyll-a

86 • Ocean Colour in Optically-Complex Waters
absorption peak at 670 nm is used to get an independent estimate of phytoplankton
biomass to separate it from the total particulate absorption. Frequently, two ac-9s areused, one to measure the total water properties and the other, with a high volume ﬁlter on the inﬂow, to measure the dissolved component. Thus the ac-9 can be used tomeasure all three components of absorption. The primary advantage of this approach isthat the ac-9 is an in situ instrument that can be used on proﬁling systems, ﬂow-throughsystems and moorings.
The third approach is to use an Integrating Cavity Absorption Meter (ICAM) whichis particularly useful for seawater measurements since results are entirely unaﬀected byscattering. The ICAM has a very long eﬀective pathlength and is thus useful for measuring
very low absorption values (Fry et al., 1992). More recently, an alternative conﬁguration,
the Point Source Integrating Cavity Absorption Meter (PSICAM), has been described by
Kirk (1997). While neither of these instruments is commercially available, the PSICAM isrelatively easy to construct (some laboratory prototypes exist), and some workers may
choose to build their own. As with the ac-9, the chlorophyll-a absorption peak at 670 nm
can be used to obtain an independent estimate of phytoplankton absorption and to separate it from the total particulate absorption. The PSICAM can also be used with ﬁlteredwater to measure CDOM absorption. The primary advantage of this method is the longeﬀective pathlength and the fact that the measurement of total absorption is independent of scattering and can thus be made on unﬁltered seawater without the need for acorrection factor.
Values of absorption coeﬃcients can also be obtained indirectly from spectral valuesof downward irradiance (Ed ), upward irradiance (Eu ) and scalar irradiance (E0 ) of the lightﬁeld measured within the water column, using the Gershun equation
−a =

µ
dE 1
dE
,
=
dz (Ed − Eu ) dz E0(4.2)

where E is the net downward irradiance (Ed − Eu ) and µ is the average cosine of the net
irradiance at a given depth, which can be deﬁned as µ = (Ed − Eu )/E0 , with E0 = E0d + E0u .
This method has been used by a number of workers over the years, and an instrument
package speciﬁcally designed to measure in situ absorption in this manner has been developed recently (O’Mongain et al., 1997; see also Højerslev, 1975; Spitzer and Wernand,1981). The primary limitation of this method is that it measures only the total absorption
coeﬃcient; there is no way to separate the eﬀect of the diﬀerent components using thismethod. Gordon and Boynton (1998) have also presented algorithms for estimation ofabsorption and back-scattering coeﬃcients from proﬁles of upwelling and downwelling
irradiance.
For all of these methods, it is important to make the measurements over the spectral
range from 380 to 800 nm. Absorption from 380 to 420 nm is particularly useful for estimating absorption by yellow substances. At longer wavelengths, many Case 2 waters areMeasurement Requirements for Case 2 Waters•

87

so turbid that there is signiﬁcant emergent ﬂux in the NIR. It follows that the common
assumption that seawater is eﬀectively ‘black’ at wavelengths > 700 nm does not hold.
Furthermore, organic detritus and suspended mineral material, can also absorb signiﬁcantly beyond 700 nm. As a routine practice, therefore, absorption measurements madefor remote sensing purposes should be carried out not only in the visible but also in the
NIR, at least to ∼800 nm, and beyond if possible (see also Chapter 3).
With the array of absorption measurement methods now available, it would be valuable for model development if a library of spectra of speciﬁc absorption coeﬃcients
could be assembled for a variety of the colour-producing agents that are likely to occur
in various Case 2 locations (e.g. diﬀerent dominant phytoplankton species, especiallybloom- or ‘red-tide’-forming species, resuspended bottom particle types and soluble andparticulate humic materials). This is a critical next step towards developing algorithms
for particular components regardless of the location of the measurement.
4.3.3 Measurement of scattering and back-scattering
At present there is no commercially-available total scattering meter, i.e. an instrumentthat can measure directly the total scattering coeﬃcient, b. However, values of b can
be obtained indirectly by measuring the beam attenuation coeﬃcient, c, in a speciﬁedwaveband. Since c = a + b, the value of b can be derived by subtracting the value ofa (the absorption coeﬃcient) from c, in that same waveband, obtained by a separate
measurement. This can be done, for example, with the dual purpose ac-9 reﬂective tube
absorption meter.
In highly turbid Case 2 waters, especially where the scattering is due mainly to mineralparticles, the turbidity in Nephelometric Turbidity Units (NTU) can be measured with anephelometric turbidimeter, and advantage taken of the approximate numerical equalitythat has been observed to exist between scattering coeﬃcient values in m−1 , and turbidity
values in NTU.
For remote sensing purposes, the back-scattering coeﬃcient, bb , is of particular relevance. Although no instrument is currently available for measurement of bb , as total
scattering in the 90 to 180◦ range, an instrument has been developed (Maﬃone andDana, 1997) that yields an estimated value based on measurement at a certain angle(120◦ ) where the ratio of speciﬁc angular scattering to total back-scattering is relatively
constant for diﬀerent scattering phase functions (Oishi, 1990). This is a particularly valuable measurement for Case 2 waters where the ratio of back-scattering to total scattering
varies as a function of the diﬀerent components in the water column.
We have very little information on complete scattering phase functions for Case 2
waters, and most workers are still forced to rely on the data set obtained by Petzold(1972) for San Diego Harbour. Scattering phase functions for a range of Case 2 watertypes are sorely needed. However, at present there is no straight-forward method orcommercially-available instrument for making these measurements.

88 • Ocean Colour in Optically-Complex Waters
We also have to bear in mind that small-volume measurements may not capture a
representative distribution of suspended particles. In this context, indirect methods of
estimating inherent optical properties from measurements of the in situ light ﬁeld (e.g.
Gordon and Boynton, 1998) present an advantage.
4.3.4 ReﬂectanceAn essential task that must be carried out by any ocean-colour remote-sensing algorithm, even if not always explicitly, is the calculation of the spectral distribution of theemergent ﬂux, by the computed removal of all the atmospheric path radiance. Any given
algorithm can thus be put to the test by ﬁrst determining, before proceeding to test its
predictions of actual water composition, how well it reproduces the actual spectral distribution of water-leaving radiance. What is needed, however, if such tests are to be carriedout routinely, is an optically-satisfactory standardized protocol for the measurement of‘remote-sensing reﬂectance’ just above the sea surface (spectral upwelling radiance divided by downwelling irradiance, i.e. Lu (λ)/Ed (λ)). The standard approach for estimating
this value is to measure proﬁles of Lu (λ) and Ed (λ) and use values of Ku and Kd calculated from these proﬁles to extrapolate the proﬁles back up to the surface. A standard
approximation is then used to extrapolate across the sea surface (Mueller and Austin,
1995; Siegel et al., 1995). A wind-roughened sea surface, partly cloudy skies, ship shadows and other common diﬃculties make this a diﬃcult measurement and calculation.
Additionally, in turbid or highly absorbing Case 2 waters, the validity of the measurements can be compromised where the radiance meter measuring Lu causes signiﬁcantself-shading (Gordon and Ding, 1992). Radiance meters with a narrow cross-section need
to be used, especially in strongly absorbing waters; still, a correction will be required forwavelengths longer than 600 nm.
An alternative approach is to measure remote-sensing reﬂectance using a hand-held
spectrometer pointed down at the sea surface (Carder and Steward, 1985, as modiﬁedby Rhea and Davis, 1997). Measurements are made of the sea surface, as well as ofa gray reﬂectance standard (10% reﬂectance Spectralon) at a 30◦ nadir viewing angle
and of the sky at a 30◦ zenith angle. All of the measurements are made in the samevertical plane at 90◦ to the plane of the sun. The sky measurement is used to correct
for the skylight reﬂected oﬀ the sea surface. This approach is eﬀective regardless of the
optical properties of the water, and can be a good reference for developing algorithms
in experiments prior to the launch of the remote-sensing system. The measurement isdiﬃcult to make under rough sea conditions, and is impractical when there is signiﬁcantdisturbance by whitecaps and foam.
The third approach to estimating remote-sensing reﬂectance at the surface is to use
the measured values of a and bb and a radiative transfer model to calculate remotesensing reﬂectance from the inherent optical properties of the water. For many remotesensing experiments, two or more of these calculations are made to compare results andassure continuity in the data sets.

Measurement Requirements for Case 2 Waters•

89

Case 2 waters are often shallow, and if the water is clear enough, bottom reﬂection cancontribute ﬂux to the water-leaving radiance. This can be of value if it is hoped to extract
bathymetric information from the remotely-sensed data. To carry out the necessary
calculations in such a case, spectral-reﬂectivity data for the bottom are required. Heretoo, we have a need for the creation of a library of such data for various kinds of Case2 environments. A standardized, optically satisfactory, protocol for the acquisition ofbenthic reﬂectivity data needs to be developed.

4.4 Validation Procedures and Error Estimation
As noted in the introduction, in the absence of validated algorithms for Case 2 waters, each data collection should be treated as an algorithm-development experiment.
Therefore, a minimal set of measurements will be required to support validation of algorithms for atmospheric correction, chlorophyll, yellow substances, suspended sediments, bathymetry and bottom reﬂectance. Additional measurements may be required
for a particular environment, or to test algorithms for the development of new products. The recommended sampling for validation of Case 2 algorithms for ocean-colour
remote-sensing data is indicated below.
i. Atmospheric and weather data to be taken at the time of the overﬂight:❖ wind speed❖ barometric pressure❖ relative humidity❖ temperature❖ sun photometric measurements
ii. Above-water measurements to be taken at the time of the overﬂight:❖ remote-sensing reﬂectance❖ spectral downwelling irradiance Ed (λ)❖ 180◦ photographs of the sea and sky conditions
iii. In water optical properties:❖ total absorption and total volume attenuation coeﬃcients from an in situ instrument with spectral capabilities❖ spectrally-resolved absorption and total volume attenuation coeﬃcients of the dissolved component of seawater measured using an in situ instrument❖ back-scattering coeﬃcient (spectrally resolved)❖ chlorophyll and CDOM ﬂuorescence using an in situ ﬂuorometer90 • Ocean Colour in Optically-Complex Waters❖ remote-sensing reﬂectance (RRS ) using an instrument which minimises self-shading
avoiding ship-shadow eﬀects.
❖ spectral Ed and Lu (also, spectral Eu for Q if available), preferably using somedevice that scans the entire spectral domain of interest at high spectral resolution;otherwise discrete measurements at multiple wavelengths
iv. Water samples from two to three depths in the upper two optical depths taken within
one hour of the overﬂight to be analyzed for:❖ chlorophyll (ﬂuorometric or preferably HPLC)❖ absorption coeﬃcient of particulate material retained on ﬁlters (or PSICAM measurements)❖ absorption coeﬃcient of material on ﬁlter after pigment extraction❖ absorption coeﬃcient of ﬁltered seawater measured using path lengths of 10 cm
or longer (or PSICAM measurements)
v. Additional measurements recommended for optically-shallow waters:❖ bathymetry (within a couple of weeks of the overﬂight)❖ bottom reﬂectance (within a week or the same day if bottom is changing)❖ bottom characterization (sediment type, coral type, etc. within a couple of weeks)
Spaceborne imagers can take advantage of ship-based experiments for the detailed characterization of the validation sites made prior to launch, and periodically after launch.
However, it is unrealistic to plan a cruise for every overﬂight. Therefore, for spaceborne
instruments, validation sites will require moored instruments to measure a minimum setof physical, optical and biological properties that can be used to validate remote-sensing
reﬂectance, water-leaving radiance, water clarity and chlorophyll products. These instruments will need to be routinely cleaned and maintained, and calibrated against shipboard
measurements to assure the accuracy of their measurements. At least one mooring will
be required for each validation site, two or more would be preferred to provide redundancy and to assess on-shore oﬀ-shore gradients.
Recommended mooring measurements:❖ above water spectral Ed❖ near surface spectral Lu , or Lu at several depths to calculate Ku and extrapolate to
the surface to get above-water Lu❖ RRS above the sea surface calculated from the above measurements or other measurements (from inherent optical properties)❖ chlorophyll ﬂuorescenceMeasurement Requirements for Case 2 Waters•

91❖ CDOM ﬂuorescence❖ beam transmission❖ water temperature❖ salinity❖ wind speed❖ air temperature❖ relative humidity❖ barometric pressure
The optical properties of coastal waters vary greatly over time and space. There is anerror associated with each in situ and remote-sensing measurement, but there is typicallya larger error associated with the time and space correlation of those measurements.
Strong coastal features, such as plumes and fronts can move rapidly, driven by tides,
longshore currents and variable winds associated with storm passage (e.g. Rennie et al.,1999). Tidal currents of 2 to 5 km h−1 or higher are typical of many coastal areas. Thus
it is essential that validation data be taken as close as possible to the exact time of theoverﬂight. Even a diﬀerence of one hour could invalidate the comparisons if there issigniﬁcant natural variation in the local waters.
Even if the data are taken simultaneously, scaling from a point sample to a 1-km
pixel is diﬃcult in any coastal waters where water type can change dramatically overthat distance. The remote-sensing system sees a weighted average of the remote-sensing
reﬂectance of the pixel, and that is often hard to relate to a single point sample. Under
way ship sampling can help the situation, and under-ﬂying satellite imagers with aircraft
imagers with 1 to 20 m spatial resolution can provide a data set to bridge the gap in scalefrom the in situ measurements to the scale of the satellite measurements.

92 • Ocean Colour in Optically-Complex Waters

Case 2 Ocean-Colour Applications•

93

Chapter5
Case 2 Ocean-Colour Applications
J. S. Parslow, N. Hoepﬀner, R. Doerﬀer, J. W. Campbell, P. Schlittenhardt,
S. Sathyendranath

5.1 Introduction
Case 2 ocean-colour algorithms are primarily (though not solely) designed for use in
the coastal zone. Human populations and activities are concentrated near coasts (Table 5.1). Over 60% of the human population lives in the coastal zone, deﬁned as theregion from 200 m above to 200 m below mean sea level by Pernetta and Milliman (1995).
Patterns of runoﬀ and the delivery of nutrients and sediments to coastal waters are
modiﬁed through human activities in catchments (e.g. land clearing, agriculture, mining, urban and industrial development). Coastal development leads to modiﬁcation offoreshores, loss of key habitats such as mangroves and seagrasses, changes to ﬂushingrates, resuspension of sediments, and direct inputs of nutrients and toxicants throughoutfalls.
Coastal waters are also a major resource for human populations. They contribute a
disproportionate share of world ﬁsheries catch (Table 5.1), and support a rapidly-growing
mariculture industry. Human recreational activities and tourism are concentrated incoastal waters and often depend on the maintenance of high water quality and valued
habitats such as coral reefs, seagrass and kelp communities, and associated fauna.

• 60% of human population• 2/3 of the world’s large cities• 8% of ocean surface• 14% of global ocean primary production• 90% of world ﬁsh catch• 75 – 90% of global sink of suspended river load
Table 5.1 Coastal zone at a glance (from Pernetta and Milliman, 1995).

94 • Ocean Colour in Optically-Complex Waters
As a result of this collision of impacts and uses, managing human activities and impacts
in the coastal zone is a high priority for all coastal nations. At the global scale, it is the
subject of a core IGBP project on Land-Ocean Interactions in the Coastal Zone (LOICZ)(Pernetta and Milliman, 1995). Many nations have one or more major regional projects
developing methods for monitoring, predicting and managing change in coastal systems.
Bio-optical constituents such as phytoplankton pigments, suspended sediments anddissolved organic matter can vary independently in the coastal zone as a result of terrestrial inputs and bottom resuspension. Coastal waters can also be optically shallow, sothat water-leaving radiance is aﬀected by bottom reﬂectance. Case 2 algorithms need tobe more sophisticated to resolve these eﬀects on water-leaving radiance spectra. However, they will also produce additional products, supporting an even more diverse set of
applications.
In this chapter, we consider the role ocean-colour products can play in monitoring andmanaging the coastal zone. We emphasize at the outset that ocean colour and remotesensing generally should not be seen in isolation as a stand-alone solution for monitoring
the coastal zone. There are many properties of interest that cannot be measured remotely, and other products that can only be derived by combining remotely-sensed data within situ or ancillary data. Researchers and managers are starting to develop integrated
approaches based on assimilating remotely-sensed data and in situ data into diagnostic
and prognostic models. Similar approaches have been used already with great successin operational meteorology.
Remotely-sensed products are obviously complementary to in situ data in terms ofspatial and temporal coverage. They provide synoptic views of spatial distribution unachievable by other means, and are ideally suited to cover the broad range of space
and time scales associated with coastal applications. The sequence of satellite sensors
planned by major space agencies (IOCCG, 1999), combined with programmes for intercalibration and merging (e.g. SIMBIOS, McClain and Fargion, 1999), promise to provide
long-term consistent data sets which may not be available from other sources.
Many of the applications discussed below depend on access to time-series of images.
Cloud cover can interfere signiﬁcantly with temporal coverage, and geostatistical techniques or assimilation into dynamical models are being developed to interpolate among
cloud-free images and improve short-term temporal coverage.
Marine scientists have recognised the need for an internationally coordinated, operational, ocean monitoring programme to support management of marine environments,
resources and industries. Plans for a Global Ocean Observing Program (GOOS) have beendeveloped under the auspices of the Intergovernmental Oceanographic Commission (IOC)over the last decade (IOC, 1998). Remote sensing is seen as playing a critical role in
GOOS, and in particular in the GOOS Coastal Module a pilot project has been proposed
to demonstrate the application of ocean-colour remote sensing for coastal monitoring(IOC, 1999).

Case 2 Ocean-Colour Applications•

95

Because they provide consistent agreed indicators across national boundaries, remotelysensed products may be critical for monitoring performance under international agreements and conventions. For example, the European Union has established environmental
directives which require monitoring of coastal water quality indicators, including transparency, colour and suspended solids (Table 5.2). Ocean-colour sensors are recognised
as oﬀering a standard, cost-eﬀective way to monitor compliance.

Directive

Subject of concern

Variables to be monitored

76/160/EEC

Quality of bathing water

Coliforms, thermo-tolerant colibacteria,transparency, pH, faecal Streptococcus

79/923/EEC
91/271/EEC

Quality required ofSalinity, oxygen, pH, temperature, colour

shellﬁsh waters

suspended material, hydrocarbons, coliforms,....

Urban waste treatment

Biological Oxygen Demand, suspended solids,
total Phosphorus, total Nitrogen

Table 5.2 Short example list of European Directives and required monitoring variables (adaptedfrom EEA, 1997). Potential ocean-colour applications are indicated in bold face.

Although the costs of sensors and algorithm development are high, the marginal costsof remotely-sensed products are very low, and many global and regional products are
distributed virtually free. Thus, remotely-sensed products can be very cost-eﬀective forenvironmental monitoring by developing countries. The IOCCG has recognised this inorganising a series of regional ocean-colour training workshops for developing countries(see http://www.ioccg.org).
In this chapter, we begin by reviewing brieﬂy the ocean-colour products that are currently available, or likely to become available in the next few years. We then discuss insome detail the application of these products to each of a set of major coastal zone sectors or issues. We conclude with a brief discussion of the role of ocean-colour productsin the development of integrated regional management strategies for the coastal zone.

5.2 Case 2 Ocean-Colour Products
The basic, Case 2 ocean-colour algorithms, described in Chapter 3, will deliver estimates of concentrations of phytoplankton pigments (chlorophyll-a), suspended sediments, yellow substances, and light attenuation coeﬃcients, in optically deep waters.
Some additional products of interest for various applications are brieﬂy discussed here.
These include chlorophyll ﬂuorescence yield, pigment composition or bloom type, andbottom depth or spectral reﬂectance in optically shallow waters. We also consider derived

96 • Ocean Colour in Optically-Complex Waters
products such as water column and benthic primary production, horizontal exchanges,
and sedimentation rates that may be obtained by combining ocean-colour products withancillary data and models.
5.2.1 Light attenuation and water colour
Case 2 ocean-colour algorithms will provide direct quantitative estimates of light attenuation (Kd ) at speciﬁc wavelengths in coastal waters (see Fig. 5.1). These can be related
to more traditional measures of water transparency (e.g. Secchi depth) and colour. Transparency and colour strongly aﬀect our perception of the suitability of coastal waters forrecreational use, and are speciﬁcally identiﬁed as water quality indicators. For example,
the European Directive 76/160/EEC on Bathing Water Quality requires Member States
to identify bathing areas, and to monitor these during the bathing season at fortnightlyintervals for water quality properties including colour and transparency.
The colour and transparency in coastal and turbid waters result from the opticalproperties of at least three components: the organic biomass (chlorophyll and otherpigments), the dissolved substances (yellow substances), and the mineral particles. The
retrieval of these components from optical remote sensing will thus provide a direct andcost-eﬀective way to assess and interpret water clarity in support of the implementationand veriﬁcation of governmental policies.
5.2.2 Chlorophyll ﬂuorescence
Advanced multispectral and hyperspectral scanners provide suﬃcient spectral and radiometric resolution to measure the passive solar ﬂuorescence peak near 685 nm, at least
for chlorophyll concentrations above 0.5 mg chlorophyll-a m−3 under favourable conditions (Babin et al., 1996; Letelier and Abbott, 1996). For high-chlorophyll environments
which are more common in coastal waters than in ocean waters, this could turn out to bea particularly useful feature (Gower et al., 1999). The ﬂuorescence measurement can beused as an independent biomass measure, or in combination with standard chlorophyll-a
products as an indicator of phytoplankton physiological state, which may help to improve
the accuracy of primary production algorithms (see below). The complementarity of theabsorption-based and ﬂuorescence-based techniques for deriving information on phytoplankton from ocean-colour data has been discussed at some length in an earlier IOCCG
report (IOCCG, 1999).
5.2.3 Pigment composition and bloom typeDiﬀerences in pigment composition across algal classes are well-known, and methods
exist to convert HPLC pigment composition, for example, to algal composition. These
diﬀerences in pigment composition can also translate into diﬀerences in the shape of
pigment absorption spectra (Sathyendranath et al., 1987; Bidigare et al., 1990; Babin etCase 2 Ocean-Colour Applications

Figure 5.1•

97

SeaWiFS imagery for 1 April, 1999 in the Adriatic Sea. The variability in Kd ,the diﬀuse attenuation coeﬃcient, at 532 nm shows the discharge pattern of the Po River (top,left) along the Italian coast. An anticyclone eddy with increased attenuation is observed in thesouthern basin. Image courtesy of Robert Arnone.

al., 1993; Hoepﬀner and Sathyendranath, 1993), although some pigment changes (e.g. incomposition of carotenoids) may have only minimal eﬀects on absorption peaks. There
may also be diﬀerences among algal groups in back-scattering spectra (Ahn et al., 1992),
although we have much less information on these. It is not so clear to what extent theinverse problem (distinguish pigment composition or algal class based on diﬀerences in
RRS ) is tractable (Cullen et al., 1997; Millie et al., 1997; Gege, 1998). There are certainly98 • Ocean Colour in Optically-Complex Waters
some algal classes and pigment groups, such as the cyanobacterium Trichodesmium sp.
(Subramaniam et al., 1999a,b) and coccolithophorid species (Balch et al., 1996; Tyrrell etal., 1999) that are readily distinguished, at least at elevated bloom densities (see Fig. 5.2
and Fig. 3.4). There may also be signatures in photo-protective pigments in the UV thatcan be used to distinguish taxa (Kahru and Mitchell, 1998).

Figure 5.2

SeaWiFS colour composite image (555, 510 and 443 nm bands) for the North East

Atlantic and UK shelf seas, 18 May, 1998. It shows diverse bio-optical conditions: blue ‘clear’seawater (B), green water (G, phytoplankton plant pigments, chlorophyll-a), white water (C, coccolithophores, calcite covered plants reﬂecting at all wavelengths) and brown water (suspended
sediments) in the Irish Sea (IS). Image provided by SeaWiFS and Plymouth Marine Laboratory
Remote Sensing Group, courtesy of Jim Aiken.

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5.2.4 Bottom depth, benthic reﬂectance and habitat
Where coastal waters are optically shallow, algorithms for water-column constituentsneed to remove contributions from bottom reﬂectance. Algorithms have been developed
to estimate bottom depth and map bathymetry in shallow waters (see bathymetry sectionof Chapter 3). Under favourable conditions, it may also be possible to remove watercolumn eﬀects, and estimate benthic reﬂectance spectra (Lee et al., 1999). With adequate
ground-truth or knowledge of benthic reﬂectance spectra associated with diﬀerent bottom types, it may be possible to produce maps of benthic habitat type. Pioneering workof this type has been carried out using data from hyperspectral airborne sensors by Juppet al. (1996).
Remote sensing of the shallow coastal environment requires high spatial resolution,
30 m or better, to be of practical value for navigation or other uses. Additionally, Maritorena et al. (1994) recommend using spectro-imaging systems to take advantage of thewidely-varying absorption properties of water and bottom reﬂectance that can be used
for bathymetry and bottom characterization. Two hyperspectral satellite systems are under development that would meet the requirements for doing shallow water bathymetry:
ARIES (Australian Resource Information and Environmental Satellite) and the Naval EarthMap Observer (NEMO) (Wilson and Davis, 1998; Davis et al., 1998). In particular, a primary
goal for NEMO is to map shallow water bathymetry and bottom characteristics at 30 m
spatial resolution to meet allied Naval requirements for shallow water navigation.
5.2.5 Water column primary production
Primary production by phytoplankton forms the basis of marine food webs in oceanicand many coastal waters. Knowledge of rates of primary production is critical for improved understanding and management of pelagic and benthic ecosystems, wild ﬁsheries
and aquaculture, and impacts of added nutrients (eutrophication). In both Case 1 andCase 2 waters, there is keen interest in predicting primary production and in generatinga variety of ancillary products from ocean-colour data. Standard algorithms for estimating water-column primary production using photosynthesis-irradiance relationships rely
on remotely-sensed chlorophyll-a, light attenuation and estimated surface irradiance. In
Case 1 waters, light attenuation in the water column can be approximated reasonably
well as a function of chlorophyll-a concentration. In Case 2 waters, on the other hand,the computation of light attenuation will have to account for the role of suspended sediments and dissolved organic matter as well. A sensitivity analysis (Platt et al., 1988) has
shown that computed water-column primary production is extremely sensitive to lightattenuation by substances other than phytoplankton. Thus, improvements to biomass
algorithms for Case 2 waters will beneﬁt computations of primary production in Case 2
waters, both by providing a better estimate of phytoplankton biomass and by improving
the estimate of light attenuation.

100 • Ocean Colour in Optically-Complex Waters
Chlorophyll ﬂuorescence yield (ﬂuorescence per unit chlorophyll) may be used as
an index of physiological state to help resolve variation in photosynthetic parameters(Topliss and Platt 1986; IOCCG, 1999). Primary production algorithms need to extrapolate from surface chlorophyll concentrations to vertical chlorophyll proﬁles: this may
be based on historical in situ data, or ancillary remotely-sensed data (e.g. SST), or on
integration of remotely-sensed data with physical models predicting mixed layer depth.
5.2.6 Benthic primary production
In shallow coastal waters, benthic plants may also make an important contribution toprimary production, as well as forming critical habitats for a range of fauna, includingcommercially important species. The role of benthic macrophytes such as seagrasses
and macroalgae, and of symbiotic algae in corals and sponges, has long been recognised.
There is growing awareness that microphytobenthos (e.g. benthic diatoms) can also makea major contribution to column production (Beardall and Light, 1996). There are goodprospects for using remotely-sensed surface irradiance and attenuation coeﬃcients topredict bottom light intensity, and to use this in turn, along with plant biomass distributions obtained either via remote sensing or in situ sampling, to predict benthic primary
production (Zimmerman and Mobley, 1996). Bottom light intensity is also a key determinant of the distribution of benthic plant communities such as seagrasses.
5.2.7 Suspended sediments and contaminants
Suspended sediment concentration (or particle back-scattering) is discussed in detailin Chapter 3 as a “standard” Case 2 ocean-colour product. Suspended sediment concentrations may be used to track turbid river plumes, or study resuspension of bottom
sediments in shallow waters due to tidal action, wind and waves. Many contaminants(organics and heavy metals) adsorb strongly to ﬁne sediments, which act as a carrier andserve to remove contaminants from the water column (Kersten et al., 1994). In this casethe horizontal distribution, transport and sinks and sources of contaminants, as wellas their biological uptake into the food web, are linked to the dynamics of suspended
particulate matter (SPM). In particular the ﬁne fraction (< 0.63 µm) of suspended matter,with its relatively large surface, has a strong inﬂuence on some contaminants. Other
contaminants such as copper are mainly introduced via micro-organisms including phytoplankton into the particulate fraction, and show a strong relationship to the carbon
content of SPM.
The SPM-related contaminant fraction is diﬃcult to monitor by conventional techniques, because the variability of suspended particulate matter is much larger than thevariability of the dissolved fractions (Kammann et al., 1994). In many cases, it is diﬃcult
or impossible to determine trends from data based on a few ship cruises per year due tothe variability of suspended particulate matter. There is a strong demand by monitoringCase 2 Ocean-Colour Applications•

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authorities to include remote-sensing data on suspended particulate matter distribution
for a better interpretation of the data on particle-bound contaminants.
5.2.8 Yellow substances
Yellow substances are also dealt with in detail in Chapter 3 as a standard Case 2 product. Coloured dissolved organic matter (CDOM) has been shown to play an importantrole in ecological and photochemical processes. Because it absorbs harmful ultraviolet
radiation eﬃciently, it can function as a bio-protectant in aquatic ecosystems. In marine
environments it can act as an energy absorption medium during the production of hydrogen peroxide (Moore et al., 1993) and as a carbon source for production of inorganiccarbon species (Miller and Zepp, 1995). Dissolved organic carbon (DOC), of which CDOM
is its coloured fraction, is an important participant in the global carbon cycle, and riverine delivery of DOC represents the largest land to ocean carbon ﬂux. Currently-available
algorithms for Case 2 waters yield information on yellow substances. These algorithms
do not attempt to distinguish between CDOM and similarly-coloured detrital particles.
Nevertheless, remotely-sensed data can provide valuable insights into the dynamics of
CDOM-like substances.
Although it is presently unclear to what extent CDOM in the ocean is the remnantof terrestrial or marine in situ production, several researchers have found CDOM to be
an excellent conservative tracer of riverine inputs. In these cases, conservative behavior is demonstrated as an inverse linear correlation between CDOM and salinity. (Laane,
1982; Mantoura and Woodward; 1983, Blough et al., 1993). Weaker correlations havealso been established between CDOM absorption and DOC mass (Ferrari and Mingazzini,
1995; Vodacek et al., 1997; Del Castillo et al., 1999), and CDOM absorption and biological productivity in selected aquatic environments (Prakash et al., 1973; Vinebrooke andLeavitt 1998).
5.2.9 Physical dynamics
Physical dynamics can be studied using images from diﬀerent types of satellite sensors, each of which has its advantages and disadvantages. Ocean circulation and its
variability are manifested in the sea topography, and satellite altimetry is used to derive
the geostrophic component of the ocean circulation (Koblinsky, 1993). However, there
are some diﬃculties in reconstruction of the dynamic height from altimetry data for regions with depths less than 50 m. Synthetic aperture radar can image current boundaries
by surface roughness signature but such signatures are strongly aﬀected by wind conditions. Sea surface temperature (SST) and ocean-colour images can be used to study theadvection of surface layers by mesoscale circulation. SST images derived from thermal
infrared sensors reﬂect the temperature of a very thin surface layer and can be aﬀectedby diurnal solar heating or wind mixing. Ocean-colour images, reﬂecting optical properties of a thicker layer, are more stable, and several investigators have pointed out that102 • Ocean Colour in Optically-Complex Waters

Figure 5.3(a) Various circulation features of an algal bloom on 27 February, 2000, in

open-ocean waters of the Arabian Sea oﬀ the Gujarat coast of India. Data collected by the
Indian OCM sensor on board the IRS-P4 satellite. Image courtesy of Shailesh Nayak. (b) Attenuation coeﬃcient at 532 nm measured by SeaWiFS on 6 November, 1999 shows various circulation
features in the northern Arabian Sea. The high optical variability of the coastal and oﬀshore
waters is observed. Data supplied by NASA/SeaWiFS project; image courtesy of Robert Arnone.

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ocean-colour imagery deﬁnes dynamical features better than infra-red imagery (Arnone
and La Violette, 1986; Ahlnas et al., 1987; Sur et al., 1996).
Various circulation features including river plumes, fronts, unstable meanders, ﬁlaments, coherent dipole and monopole eddies and jets, can be studied using ocean-colour
images (see Fig. 5.3). These features are often clearly visible in ﬁelds of chlorophyll concentration, even though phytoplankton pigments measured are not conservative tracers(Yentsch, 1984). Other products such as yellow substances and suspended sediments
may be better tracers than chlorophyll-a. Figure 5.4 shows the mean winter distribution (November 1997 – March 1998) of the absorption coeﬃcient of yellow substances at
440 nm in the Mediterranean Sea derived from SeaWiFS data (Kopelevich et al., 1998b).
The important sources of yellow substances and their dispersal over distances extending
many miles from the sources are clearly visible.
Depending on the platform, ocean-colour remote sensing may provide time series oftracer ﬁelds (chlorophyll, yellow substances, suspended solids) at intervals ranging fromhours to days. These time series may be analysed to provide estimates of horizontal
velocity ﬁelds, using direct correlation techniques or through assimilation into circulation models. By comparing the evolution of ﬁelds of yellow substances and suspended
particulate material from a point source, it may be possible to estimate sedimentation
rates. More generally, suspended sediment ﬁelds may be used to calibrate or validate
models of sediment transport, resuspension and sedimentation.

Figure 5.4

Mean winter distribution (November 1997 – March 1998) of the absorption co-eﬃcient of yellow substances at 440 nm in the Mediterranean Sea, derived from SeaWiFS data.
Image courtesy of Oleg Kopelevich.

104 • Ocean Colour in Optically-Complex Waters
5.3 Applications by Issue or Sector
5.3.1 Impacts of land use and river runoﬀ
Human activities in catchments (e.g. grazing, agriculture, forestry, mining, dams, irrigation schemes, urban and industrial development) can all result in major changes inthe volume and pattern of runoﬀ, and in increased loads of nutrients and suspended
sediments into estuaries and adjacent coastal waters. Changes in patterns of freshwater runoﬀ due to catchment modiﬁcation may profoundly aﬀect circulation and salinity
regimes in estuaries and adjacent coastal regions, with impacts on key habitats (e.g. mangroves, salt marshes) and on ﬁsh recruitment.
LOICZ is concerned with the impact of these changes on biogeochemical cycles atregional and global scales (Pernetta and Milliman, 1995 – see below). Environmental
managers are concerned with impacts on water and sediment quality and coastal ecosystems at local to regional scales. Common problems or concerns include loss of benthic
communities due to direct disturbance, increases in turbidity and sediment deposition,and nuisance algal blooms due to eutrophication. Catchment managers are increasingly
required to take account of downstream impacts on estuarine and coastal systems.
At present, downstream estuarine and coastal impacts are often poorly understoodand monitored, especially in large or remote ecosystems, because of the diﬃculty andexpense of monitoring marine indicators at appropriate time and space scales. Oceancolour products such as suspended sediments, light attenuation and chlorophyll-a concentration, are key indicators of cause and eﬀect in these systems. There is often concern
and uncertainty about the spatial extent of impacts associated with major ﬂood events.
Concentrations of suspended sediment and yellow substances can be used to track plume
distribution and dilution, and sedimentation, calibrate or validate dispersion models, andidentify interactions with benthic ecosystems (e.g. coral reefs) or exchanges with oﬀshore
waters.
Catchment runoﬀ events involve space scales ranging from hundreds of metres tokilometres, and time scales from hours to weeks.
5.3.2 Impacts of coastal development
Coastal development (e.g. urban and tourist development, port and harbour development, dredging, and outfalls) alter coastal catchments and directly impact on coastal environments through modiﬁcation of foreshores, removal or disturbance of coastal habitats
such as mangroves and seagrass and changes to ﬂushing rates.
The development and maintenance of coastal infrastructure, such as harbours andshipping channels, often resuspends sediments. This can result in temporary or permanent increases in light attenuation, loss of benthic habitat through shading or smothering, and remobilization of contaminants and organic matter (Wakeman et al., 1975;

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Moore, 1977; Appleby and Scarrat, 1989). Again, ocean colour can be used to monitor
the distribution and fate of suspended sediments, and changes in bathymetry.
Sediment dynamics may be dominated by tidal forcing at time scales of hours, withplumes and fronts involving space scales of order 100 m or less. For these purposes,
airborne sensors may be particularly valuable because of their high spatial resolution,
and ability to target surveys to resolve tidal cycles.
5.3.3 Eutrophication
Eutrophication is the phenomenon of enrichment of natural systems through increased
loads of nutrients. In coastal marine systems, it can result both from catchment land use
and increased river inputs, and from direct urban inputs from sewage treatment plants,
industry and stormwater drains. Eutrophication generally results in increases in algal
biomass, to a point where harmful algal blooms result. In naturally oligotrophic coastal
systems, it often results in replacement of seagrasses by benthic macroalgae and thenby phytoplankton blooms.
Eutrophication represents one of the most severe and widespread environmental
problems for coastal zone managers. Chlorophyll-a is a standard indicator for eutrophication. By providing a cost-eﬀective capability to monitor chlorophyll levels on a regularbasis, Case 2 ocean-colour remote sensing will have substantial beneﬁts for the management of eutrophication, especially in remote areas.
5.3.4 Global change and regional biogeochemical cycles
Within this century, the eﬀects of climate change and the pressures of human population growth are expected to have signiﬁcant impacts on coastal communities and thecoastal ocean environment. These forces will call for coordinated, integrated coastal management strategies and monitoring systems. Predicted eﬀects of climate change include
sea-level rise and the increased frequency and intensity of storms with accompanying
coastline erosion, river discharge, and sedimentation. Sea-level rise will ﬂood humansettlements and invade coastal aquifers with salt water. Greater attention will be paid toemergency preparedness for hurricanes and tsunamis.
In recent years, humans have altered river discharges and their chemical composition.
The total discharge of freshwater into the coastal ocean has decreased by 15% during thepast 30-40 years, while the average nitrogen concentration in the world’s rivers has increased linearly with human population growth. Such nutrient loading, combined with
global overﬁshing and climate-induced changes in upper-ocean temperature and stratiﬁcation, alters the fundamental dynamics of marine ecosystems and the role of continental
margins in the global carbon cycle.
Atmospheric transport of nutrients and pollutants oﬀshore in the form of aerosol
and rainwater is another link between the continents and the oceans. Biomass burning106 • Ocean Colour in Optically-Complex Waters
contributes a signiﬁcant amount of nitrogen to the coastal ocean, in eﬀect, fertilizing
phytoplankton growth and altering primary productivity in coastal waters.
Land-derived materials (carbon, nutrients, sediment) are massively injected into thecoastal zone at speciﬁc locations such as river mouths and estuaries, and therefore anychanges in these inputs from human activities within the catchment basin have major
consequences for the entire sediment and carbon balance of the coastal seas.
The global ocean is considered a major sink for fossil and biogenic carbon released tothe atmosphere by human activities. One mechanism is by thermodynamic equilibriumwith increasing CO2 in the atmosphere. A second mechanism involves the ﬁxation ofdissolved carbon by phytoplankton and the subsequent export of organic material tothe deeper layers of the oceans and in sediment (the “biological pump”, Longhurst andHarrison, 1989). Both mechanisms occur in the coastal zone where the biological pump
may be enhanced or reduced on a short-term basis in response to changes in land-use
practices.
According to Smith and Hollibaugh (1993), carbon burial in the coastal zone varies
between 0.03 to 1.68 Gt C y−1 , with a “best estimated” mean value of 0.13 Gt C y−1 . When
compared with mean values between 0.3 and 0.4 Gt C y−1 for river carbon discharge, thecoastal areas would act globally as a net source of CO2 in the atmosphere.
In addition to large uncertainties surrounding these estimates (± 0.1 Gt C y−1 ), theglobal tendency of coastal areas to act as a source term may not apply everywhere at
the regional (national) scale. Large diﬀerences exist between river systems, and thoseadjacent to industrialised regions with high nutrient loads probably sequester more inorganic carbon than is released to the atmosphere by remineralisation in the upper layer.
The coastal seas of Europe have changed in recent years from a net source to a net sink
for atmospheric CO2 (Kempe, 1995).
The coastal marine environment, therefore, represents an essential component in thedetermination of the carbon baseline. Ocean colour can contribute to attempts to monitor
the carbon balance following sequestration projects based on changes in land-use, in thecontext of the Framework convention on Climate Change (Kyoto, December, 1997). The
biogeochemical variables of importance associated with carbon cycling in coastal waters
include biomass (in mg chlorophyll-a m−3 ), primary production (mg carbon m−2 d−1 ), andthe so-called “new production” directly linked to the biological pump. Based on a decadeof experience with satellite data processing, the oceanographic community is using ocean
colour and thermal imagery to retrieve eﬃciently these parameters on global scales andat a spatial and temporal resolution appropriate to resolve coastal dynamics.
To investigate the complex mechanisms contributing to coastal blooms and physicalprocesses requires a suite of measurements and models integrating biological, meteorological, hydrological, physical and chemical processes. Long-term operational remotesensing measurements, including ocean colour, will play a critical role in assessing how

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climate change and human population pressures will impact marine resources and biogeochemical cycles.
5.3.5 Harmful algal blooms
Algal blooms may be classiﬁed as harmful when the algae involved produce toxins thatkill fauna or endanger human health, or simply when the high biomass involved in dense
algal blooms results in anoxia, shading or smothering and loss of fauna and ﬂora. Thereis evidence that the incidence of harmful algal blooms has increased worldwide over thelast few decades (Anderson, 1989). This may be due to increases in nutrient loads or tochanges in nutrient ratios in coastal waters (e.g. Smayda, 1990), or to introductions of exotic toxic strains or species e.g. through ballast water discharge (Hallegraeﬀ, 1991). Some
toxic strains can cause ﬁsh kills or endanger human health at quite low concentrations,and there is evidence in some regions of a long history of toxic blooms.
Harmful algal blooms (HAB) are a particular problem for mariculture. They cause
mass mortality of caged ﬁsh, and long closures of shellﬁsh farms due to contamination
by toxins. They also force closures of wild shellﬁsh ﬁsheries, endanger human health,and can result in closure of recreational beaches and loss of tourism. The economic
impacts of all these eﬀects are substantial.
Identiﬁed management responses fall into one of three categories: prevention, mitigation and control (Boesch et al., 1997). Prevention measures are concerned with understanding and changing factors such as nutrient loads and ﬂushing, which encouragealgal bloom formation. As discussed above for catchment loads and coastal development,
ocean-colour indicators may be used to monitor and guide attempts to reduce loads orchange ﬂushing rates.
Control measures are still in their infancy, and most practical measures are related
to mitigation. In the case of mariculture, this may involve early harvesting of shellﬁsh,
or movement of ﬁsh cages. Mitigation measures are critically dependent on monitoringand especially on early detection. International experts have recognised that bio-optical
approaches in general and ocean-colour sensors in particular have an important role toplay in detection and monitoring (SCOR, 1998). Ocean-colour sensors may be able to detect increases in chlorophyll levels well before blooms become obvious to the naked eye.
Remote sensing can also serve to track blooms moving within discrete water masses. Examples of the use of satellite-derived SST in such applications are provided by Anderson(1994; 1995).
The development of algorithms to discriminate among bloom types is still an active
research area (Cullen et al., 1997). As discussed earlier, where algal groups containing
pigments with unusual spectral signatures may be identiﬁed (Kahru and Mitchell, 1998)
there may be a means of distinguishing harmful blooms from other blooms. In general,
however, ocean-colour sensors will not be able to discriminate arbitrarily between toxic

108 • Ocean Colour in Optically-Complex Waters
and non-toxic species, but with additional local knowledge and ancillary data, it may bepossible to use ocean-colour products to identify high-risk blooms, and target in situ
sampling.
It should also be noted that vertical distribution may also limit the use of ocean-colour
sensors to monitor blooms. Some blooms either develop subsurface, or undergo verticalmigrations (Clementson et al., 1998), rendering ocean-colour biomass estimates unreliable. Again, local knowledge of the species involved may be essential for interpreting the
remote-sensing data.
Mitigation responses depend on an ability to predict bloom dynamics. It is very useful
to distinguish between bloom initiation areas, and subsequent bloom development andadvection. Time series of spatial maps provided by ocean-colour sensors can be used to
identify initiation sites, even after the event. They can also be used to understand andmodel the role of upwelling or frontal zones, or runoﬀ events, in bloom initiation (Millie etal., 1997). Even where bloom dynamics are not well understood, linking knowledge of thespatial distribution of plumes with circulation models can allow short-term predictionof bloom advection, which can be critical for decisions about moving ﬁsh farms (SCOR,
1998).
Harmful algal blooms typically involve length scales from hundreds of metres to tensof km, and time scales from days to weeks. Time series of products with a spatial resolution of 30 m to 1 km at intervals of one to a few days are highly desirable. This samplingis readily accomplished with polar-orbiting satellites.
5.3.6 Conservation
There has been a tendency in the past to ignore the conservation value and biodiversity of marine ecosystems, except for some high proﬁle systems such as coral reefs.
Other benthic and pelagic communities, subject to heavy modiﬁcation through activities
such as demersal trawling, have been neglected: essentially out-of-sight, out-of-mind.
Coastal environmental managers are now seeking to address marine biodiversity andconservation through spatial zoning and systems of reserves.
The implementation of a Community Biodiversity Strategy in Europe [COM(1998)42]
includes the development of action plans for the conservation of natural resources, agriculture, ﬁsheries and development and economic cooperation. The research objectivesof the Biodiversity Strategy have been fully incorporated and further developed within the relevant thematic and horizontal programmes and key actions of the European
5th Framework Programme on Research and Development (in particular within the key
action “Global Change, Climate and Biodiversity”). Development of indicators is a priority theme, and the European Environmental Agency is now developing a European-widesystem for monitoring the status of, and trends in, biological diversity. The project identiﬁes the need for a continuity in the Earth Observation space programme to monitor aCase 2 Ocean-Colour Applications•

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wide range of biospheric changes and biodiversity studies, including studies on overallbiomass production levels.
Conservation of marine ecosystems and biodiversity involves a variety of informationneeds. In deciding where to locate reserves, managers need to know the kinds of ecological communities present, and their spatial distribution. The distribution and conditionof most pelagic and some benthic marine ecosystems may change rapidly in response tointerannual climate and ocean forcing, to extreme events such as cyclones and ﬂoods,
and to impacts from adjacent catchment and coastal development. It is not clear that thetraditional terrestrial approach of ﬁxed, isolated, national parks will serve to conserve
marine habitats and biodiversity. In any case, it is important in planning and managing
reserves that managers are informed about the spatial and temporal patterns of environmental forcing, and the dynamical response of marine ecosystems.
Ocean-colour products such as suspended solids, chlorophyll, light attenuation andbottom light intensity, and water column and benthic primary production, provide a
direct window on some of the key environmental forcing agents, and on broad levelecosystem responses. The dynamics of recovery of benthic marine systems often depend on larval recruitment, and patterns of advection and connectivity among isolated
spatial units. Analysis of time series of ocean-colour tracers such as yellow substances,suspended sediment and pigment can be used to estimate velocity ﬁelds directly, or tocalibrate models of circulation and connectivity.
To demonstrate that marine reserves are meeting conservation goals, indicators of
ecosystem status must be identiﬁed and monitored both inside and outside of the reserves. The standard water quality indicators (chlorophyll, suspended sediment, attenuation) provide a measure of both the status of the pelagic system, and threats to benthic
communities. In optically-shallow waters, hyperspectral ocean-colour sensors may provide maps of the distribution, productivity and health of key benthic communities suchas seagrass (e.g. Mumby et al., 1997). These are particularly important because habitat
is often used as a pragmatic surrogate for biodiversity in conservation management.
As well as habitat and biodiversity, there are conservation issues associated with key
or endangered species, including sea turtles and marine mammals such as dugongs andwhales. In combination with other techniques such as satellite tags, ocean colour cancontribute to understanding of the movement and ecology of these species, and help toimprove management and reduce bycatch (Polovina et al., 2000). For many of the larger
species, aerial spotting is used to monitor population abundance. Maps of visibility may
be used to establish suitable conditions for spotting, or to evaluate the eﬃciency ofspotting procedures.
Introduction of exotic marine organisms, ranging from toxic phytoplankton (Hallegraeﬀ, 1991) to invertebrates, is increasingly recognised as a major conservation issue.
Ballast water is a principal medium for introductions, and many nations have introduced

110 • Ocean Colour in Optically-Complex Waters
regulations requiring oﬀshore exchange of ballast water. Ocean-colour products may be
used to establish low-risk areas for ballast water exchange.
Conservation involves a very broad range of spatial scales, from metres for benthic
habitat to hundreds of km for climate and oceanographic forcing. Similarly, time scales
range from short-term extreme events such as ﬂoods and cyclones to interannual variability in ocean forcing.
5.3.7 Tourism
Consistent with the movement of the global economy towards service industries,
tourism has assumed an ever-increasing importance in national and regional economies
in both developed and developing counties. In many countries, coastal environments are
critical in attracting visitors (bathing and surf beaches, ecosystems such as coral reefs,
recreational ﬁshing). Monitoring and maintaining a healthy coastal environment is thencritical to the regional economy. Ironically, in cases of poor planning and unregulated
development, tourist development itself can be the major threat to coastal environments.
As noted above, ocean-colour products such as chlorophyll, turbidity and transparency can be used to monitor coastal water quality and thereby form the basis of national
or international environmental regulation. Similarly, ocean-colour products can be used
to manage and conserve key marine ecosystems such as coral reefs and seagrasses.
5.3.8 Wild ﬁsheries
To date, most ﬁsheries have been managed using stock assessments based on single
species population models. A series of high proﬁle stock collapses, associated in somecases with suspected or known environmental changes, along with changes in public
attitudes and concerns about issues such as bycatch, eﬀects of trawling, and trophic
interactions, have led to attempts to manage ﬁsheries on an ecosystem basis (Sherman etal., 1996). However, the understanding and tools for doing this are still poorly developed.
Even within single-stock approaches, there has long been concern about processes
aﬀecting stock recruitment, including larval advection, and conservation of nursery habitats such as mangroves and seagrasses. For coastal ﬁsheries, there is concern about the
impacts of degradation of coastal water quality and loss of habitats through increased
nutrient and sediment loads, and coastal development. There is also concern about the
impacts of some ﬁshing methods, especially benthic trawling, on benthic habitat andsuspended sediment levels. As noted above, ocean-colour sensors can provide a suite ofproducts to address many of these issues. The Advanced Fisheries Management Information System (Rothschild et al., 1998) is an example of a modern approach that involves
linking observations from remote and in situ sensors with information from the ﬁshingﬂeet. The data are assimilated into dynamic models that are used for assessment andforecasts of stock abundances.

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The more traditional use of remote sensing of SST and ocean colour in ﬁsheries has
been an operational one: to help ﬁshers target areas with high ﬁsh densities, particularly
for schooling pelagics such as tuna and mackerel. Many of these ﬁsh species aggregate
along fronts or around eddies or upwelling zones that are visible in SST and ocean-colour
images.
Features often coincide in simultaneous SST and colour images, revealing eddies andfronts in phytoplankton biomass and primary production associated with changes in nutrient supply or mixed-layer depth. To the extent that higher plant biomass is associatedwith zooplankton and food ﬁsh, one might expect to gain additional information on stock
distribution from ocean-colour pigment ﬁelds.
A study by Laurs and coworkers (Laurs et al., 1984) concluded that colour patterns
may actually provide better information regarding the location of Albacore tuna. It was
originally believed that tuna prefer to stay within certain restricted temperature ranges,
thus explaining their aggregation at a temperature front. In instances where colour and
SST fronts were spatially separated, however, they found that tuna actually tend to aggregate on the clear side of a colour front. Laurs had previously found that the temperature
range traversed by tuna during vertical dives is much larger than the range across a horizontal front. Their association with colour fronts is attributed to the fact that tuna arevisual feeders and thus need to see their prey. On the clear side of a colour front, they
are able to prey upon species found grazing within the greener waters on the other sideof the front.
Much of this work to date has been on pelagic ﬁsh stocks which are distributed on theouter shelf or open ocean, and it could be argued that these are really Case 1 applications.
However, as Case 2 products become available, and especially as these are combined witha better understanding of the distribution of benthic habitats, it should be possible toextend these stock distribution models to a wider variety of coastal and shelf ﬁsheries.
Many ﬁsh stocks are either fully or over-exploited, and it might be argued that using
remote sensing to increase ﬁshing eﬃciency will exacerbate the problem. However, thisargument is naïve. Most ﬁshing ﬂeets already have suﬃcient technical capacity to overﬁsh stocks: stocks are managed increasingly by imposing catch quotas rather than limits
to vessel numbers. Management failures stem largely from ignorance: a better understanding of stock distribution and its response to varying ocean conditions should allow
better interpretation of interannual variation in catchability, and more reliable stock management. The economic viability of the industry may still depend on the eﬃciency withwhich those quotas are obtained.
By-catch and dumping are major issues for the ﬁshing industry. By-catch can include
non-commercial threatened species, as well as unwanted species. Where catch quotas are
set on individual stocks in a multispecies ﬁshery, ﬁshers may catch species for which they
have no quota, and be forced to dump them. Better understanding of the distribution of

112 • Ocean Colour in Optically-Complex Waters
both target and non-target species would allow ﬁshers to reduce by-catch and dumping(Polovina et al., 2000).
5.3.9 Aquaculture
The aquaculture industry is growing rapidly worldwide, and represents an increasingly
important source of high-value seafood. Key issues facing the industry include the identiﬁcation of suitable sites for expansion, and the management of environmental impacts
of, and on, the industry. The industry can be divided broadly into macroalgal/microalgal
culture, shellﬁsh culture, crustacean/ﬁsh pond culture, and ﬁsh cage culture.
Macroalgal culture is obviously dependent on water quality, including light attenuation and nutrient availability. Ocean-colour products can be used to obtain both direct
and indirect information about macroalgal growth conditions.
Shellﬁsh culture typically relies on “natural” phytoplankton as food, and is therefore
strongly dependent on phytoplankton biomass and composition, and its seasonal andinterannual variation. As noted above, shellﬁsh accumulate toxins from harmful algal
blooms, and shellﬁsh farms incur signiﬁcant economic losses (as well as health risks
and monitoring costs) as a result of harmful algal blooms. There is also concern thatlicensing of too many adjacent sites can lead to local depletion of phytoplankton, withadverse ecological and economic consequences. Case 2 ocean-colour products are particularly well-suited for selection and monitoring of waters around shellﬁsh sites. Shellﬁsh
culture is also particularly vulnerable to impacts of other particle bound contaminants,
so that techniques for tracking and predicting the distribution of particulate matter areimportant (Karbe et al., 1994).
Crustacean/ﬁsh pond culture is intensive. Most ponds are highly eutrophic, andgrowth of farmed organisms relies on a combination of added feed and in situ production. There are usually moderate to high exchange rates between ponds and adjacentwaters, and there is concern about the eﬀects of pond discharge on adjacent waters, andthe eﬀects of adjacent water quality on ponds (e.g. adjacent algal communities may act
as seed populations, leading to non-productive or harmful blooms in the ponds).
Fish cage culture is highly intensive, being eﬀectively the marine equivalent of feedlots. Many are directed at the production of high-value ﬁsh, and maintenance of a highquality environment is critical both for production and marketing. Caged ﬁsh are vulnerable to mortality from harmful algal blooms, and development of anoxic conditions
in sediments and bottom waters. Fish farms represent a large source of recycled nutrients and are often located in sheltered waters with low ﬂushing rates. Uncontrolled
development in some locations has led to eutrophication.
Most aquaculture activities depend on maintenance of high environmental quality,
and on routine monitoring of that quality for operational purposes. At the same time,
there is concern about eﬀects of aquaculture on water quality and adjacent ecosystems.

Case 2 Ocean-Colour Applications•

113

Ocean-colour products can potentially meet operational and conservation monitoringneeds cost-eﬀectively, with spatial and temporal coverage unachievable by other methods. There are extensive in situ operational monitoring programmes already in placearound many aquaculture sites, and good prospects for synergy between in situ measurements (including bio-optical measurements) and ocean-colour measurements (Sathyendranath et al., 1997b).
The space scales associated with aquaculture applications are generally small, requiring spatial resolution of 100 m or better for water quality, and 30 m or better for benthic
habitat. Time scales associated with algal blooms and runoﬀ events are days to weeks,achievable by sampling from a polar-orbiting satellite.
5.3.10 Maritime operations
Ocean-colour products such as visibility, bathymetry, and bottom type can be used tosupport navigation, shipping, diving and hazard detection in coastal waters. These haveboth civilian and military applications. Velocity ﬁelds obtained through analysis of timeseries of tracers may also be used to assist with ship routing. Airborne and shipborne
LIDAR systems are also used for mapping bathymetry and hazard detection, and visibility
indices from ocean-colour sensors can be used to predict the performance and guide thedeployment of these systems.
5.3.11 Integrated coastal zone management
The combination of advanced multi-spectral and hyperspectral sensors, increases infundamental knowledge of inherent optical properties, and powerful new forward andinverse model techniques, amount to a revolutionary advance in ocean-colour remotesensing of Case 2 waters. As we have seen in this chapter, the new core products andderived products will have an equally powerful impact on coastal zone management
across a very wide set of applications and issues.
There is a large degree of overlap among the various issues and sectors discussed
above, in terms of both the potential ocean-colour indicators and the underlying processes and interpretation. This is not surprising, as these uses and issues interact strongly
through coastal ecosystems. Because of these interactions, attempts to manage individual sectors independently have been both ineﬃcient and ineﬀective, and managers are
increasingly adopting integrated multiple-use regional management approaches. This
concept is still evolving, but most approaches involve the development of integrated
databases, models and monitoring programmes for regional ecosystems and uses, andthe development of multiple-use management strategies, often based around spatial zoning. Depending on the focus, the scope of system models and monitoring programmes
may span catchments, rivers, estuaries and adjacent shelf waters.

114 • Ocean Colour in Optically-Complex Waters
Integrated regional management oﬀers an opportunity to consolidate the various roles
which ocean-colour products such as chlorophyll, suspended sediments, light attenuation, algal bloom types and benthic habitat distribution can play as both indicators, andfor assimilation into system models. They also oﬀer an ideal opportunity to design andimplement complementary in situ and remote monitoring programmes, and to integrate
remotely-sensed products, including ocean colour, with in situ data sets, including insitu bio-optical data. Because they bring together in situ data, remotely-sensed data andmodels, regional management programmes oﬀer an ideal opportunity for further development and application of both direct and derived ocean-colour products.

5.4 Summary and Conclusions
The heavy pressures placed on coastal environments and ecosystems through growthand concentration of human population, activity and resource utilisation in the coastalzone pose enormous challenges for coastal managers. Impacts are now commonly observed at regional rather than local scales, and transcend sectors and nations. Monitoring
marine systems has always been diﬃcult and expensive: many of these impacts to datehave simply gone unrecorded. Adequate monitoring at appropriate time and space scales
is essential if these impacts are to be managed and mitigated.
In this context, the development of ocean-colour science and technology, with its
ability to monitor a range of key indicators at a wide range of time and space scales, canplay a critical role. When these advances in remote sensing are combined with advancesin automated in situ instrumentation, and with advances in our ability to model coastal
systems and assimilate satellite and in situ data, we can speak without exaggeration of arevolution in knowledge and management of the coastal ecosystem.
Experimental satellites planned for the next ﬁve years will provide an opportunity todevelop and test algorithms, models and management strategies. However, in the longrun, coastal management programmes will depend for their success on access to consistent long-term data sets. The importance of long-term continuity has been recognised by
the space agencies and the ocean-colour community (IOCCG, 1999). As Case 2 products
move from a research to an operational status over the next few years, the users, oceancolour community, and space agencies must work together to ensure that future satellite
programmes and ground-processing systems provide long-term time series of products
matched to applications.

Conclusions and Recommendations•

115

Chapter6
Conclusions and Recommendations
S. Sathyendranath

In this IOCCG report we have tried to provide an overview of the problems related toremote sensing of ocean colour in Case 2 waters (Chapter 1); to identify the approaches that have been used to address these problems; and to evaluate the extent to which
they have been solved to date (Chapters 2 and 3). What is immediately obvious fromthis analysis is that quantitative remote sensing of ocean colour in Case 2 waters is amuch more complex problem than in Case 1 waters (Chapter 2). The algorithms thathave been developed for application in Case 2 waters (Chapter 3) reﬂect this complexity.
The requirements for remote sensing of ocean colour in Case 2 waters (Chapter 4) arealso more stringent and exacting than for Case 1 waters. These include higher spectral,radiometric, spatial and temporal resolution for the remote observations, as well as agreater array of in situ observations to complement the remotely-sensed data. The multitude of applications to which remote sensing lends itself (Chapter 5) justiﬁes this extra
eﬀort required to extend and improve the techniques of data collection as well as thealgorithms for data interpretation. It is also important to note that improved algorithms
for Case 2 waters will also beneﬁt remote sensing in Case 1 waters:

6.1 Applications of Results in Case 1 Waters❖ Whereas algorithms for Case 1 waters are not easily adapted for Case 2 waters, theinverse does not hold true. In fact, algorithms developed for complex Case 2 waters
can be readily applied to the simpler problem of Case 1 waters.
❖ The general trend in algorithm development in Case 2 waters is to move towards
model-based techniques that are soundly based on the ﬁrst principles of ocean optics, rather than on purely empirical approaches. The ensuing improvements to ourunderstanding of the factors that inﬂuence ocean colour will also serve to improvealgorithms in Case 1 waters.
❖ There is increasing interest in extracting additional information from Case 1 waters
than just the concentration of the main phytoplankton pigment, chlorophyll-a. For
example, one would like to know the composition of auxiliary pigments, or to havean idea of the species composition of phytoplankton in the water. The approaches

116 • Ocean Colour in Optically-Complex Watersdeveloped for dealing with the multivariate, non-linear system of Case 2 waters canbe easily adapted to multi-variable retrieval in Case 1 waters.
❖ The Case 1 algorithms rely implicitly or explicitly on the idea that everything in thewater that inﬂuences ocean colour covaries with the concentration of chlorophyll-ain the water. Case 2 algorithms require no such simplifying assumptions. Therefore,
the use of Case 2 algorithms in Case 1 waters should in fact improve the accuracy of
retrieved pigment concentrations, in the event that the assumed relationship between
chlorophyll-a and all the other constituents of the water breaks down.
❖ The upwelling signal from water in the near-infrared part of the spectrum is often
non-negligible in Case 2 waters, requiring that we abandon yet another common assumption made in Case 1 waters: that the atmospheric signal can be decoupled fromthe water signal in the near-infrared domain. In Case 1 waters also, it becomes diﬃcult
to decouple the atmosphere from the ocean in the presence of absorbing aerosol (seeChapter 3, section 3.1.1). Thus, in dealing with atmospheric correction issues, both
Case 1 waters and Case 2 waters require algorithms that treat water and atmosphereas a coupled system.
All the above considerations suggest that improvements of Case 2 algorithms will be of
direct beneﬁt to Case 1 waters as well. The algorithms for Case 2 waters, being moregeneral, and more powerful, may well replace Case 1 algorithms in the future. In fact,
development of algorithms that would be equally valid in Case 1 and Case 2 waters
would eliminate the potential problems of demarcating Case 1 and Case 2 waters, and of
integrating the two algorithms seamlessly into a single protocol. However, the desirabilityof not having many disconnected algorithms raises another issue:

6.2 Can we have a Single Case 2 Algorithm Applicable Globally?
This report has stressed the point that ocean colour of Case 2 waters has to be treated as a multi-variable, non-linear problem. The problem is aggravated by the extremely
high ranges in the concentrations of the individual components that contribute to ocean
colour. Development of algorithms requires that there be a unique relationship between
some transformation of the remotely-detected signal and the variable to be retrieved.
Given the diﬃculties associated with solving the problem for all the variables and overthe entire possible ranges of concentrations, it may be more realistic to envisage a series of branching algorithms to deal with the problem, rather than a single, all-purpose
algorithm. The initial algorithm would be a coarse algorithm, merely identifying certain
ranges of concentrations within which the solutions lie. A second algorithm, optimised
for those ranges of concentrations may then be activated, to narrow the solutions into
smaller ranges, and the process may be repeated as many times as necessary. This would
allow the use of algorithms that have been optimised over small ranges of concentrations,with corresponding accuracies in the retrieved quantities.

Conclusions and Recommendations•

117

Seamless integration of multiple algorithms would not be a problem with the use ofsuch branching algorithms; a more serious issue arises if one has to allow some ﬂexibility
in the deﬁnition of the speciﬁc inherent optical properties of the individual componentsthat contribute to ocean colour. We have seen that each of the components of oceancolour models constitutes a group of substances, and one can expect a certain amountof variability in their optical properties, depending on the exact composition of eachgroup. It is clear that algorithms that have been optimised for local conditions will perform better than a general, global algorithm. However, seamless integration of several
regional algorithms would remain a problem. Similar problems can also arise in Case 1
algorithms, if one wishes to use algorithms that have been optimised for diﬀerent typesof phytoplankton blooms.
Thus, the real problem that exists today is perhaps not how to integrate Case 1 andCase 2 algorithms seamlessly, but rather how to couple diﬀerent algorithms that havebeen optimised for special situations, whether they be in Case 1 or Case 2 waters. This
problem merits further attention and study.
Evaluation of the need for, and the performance of, regional algorithms will depend
strongly on local variations in the inherent optical properties of water constituents. This
raises the next issue:

6.3 How well do we Understand Regional Variations in the Inherent Optical Properties of In-Water Constituents that Contribute to Ocean
Colour?
There is still some uncertainty on the appropriate inherent optical properties to use
for the water constituents in Case 2 waters. For example, in the case of phytoplankton,
we have some understanding of how to parameterise their optical properties as a function of chlorophyll-a concentrations in Case 1 waters. These parameterisations exploit
known general trends in species composition and species succession in open-ocean waters. It is questionable whether these trends would hold in Case 2 waters. In fact, it
would be reasonable to anticipate that there may be considerable regional diﬀerencesin the relationships between phytoplankton optical properties and the concentration ofchlorophyll-a in diﬀerent Case 2 waters, depending on regional diﬀerences in the algal
ecology.
Characterising the optical properties of particles other than phytoplankton is evenmore problematical, particularly for the inorganic component, about which we have very
little information. More studies are certainly needed to ﬁll this lacuna. Furthermore, itwould be desirable to set up an international data base of inherent optical properties ofdiﬀerent aquatic substances, with free and easy access to all interested researchers, tofacilitate access to data that is already available. This is important for research into Case118 • Ocean Colour in Optically-Complex Waters
2 waters, since available information often gets published in regional reports of local
interest that are not widely publicised.
A point worth noting in this context is that the inherent optical properties must be
measured well into the near infrared for applications in Case 2 waters, because the waterleaving signal in Case 2 waters is often signiﬁcant in the near infrared. Hence, opticalmodels for Case 2 waters should include the near-infrared domain, in addition to thevisible. This is not a typical requirement for Case 1 waters.
It is expected that the accuracies of retrieved quantities can be improved by increasing
our understanding of the inherent optical properties and by ensuring that the models are
optimised for local conditions. This raises the next question:

6.4 What Accuracy and Precision can we Expect for Quantities Retrieved
from Remotely-Sensed Data on Ocean Colour in Case 2 Waters?
The non-linearity of the system, combined with variabilities in concentrations of constituents that range over several orders of magnitude, dictates that one cannot expect
the same precision and accuracy for the retrieved concentrations over all conditions andconcentrations. In fact, they would depend on a number of things:❖ the concentration of the retrieved quantity and the concentrations of other substancesthat co-exist in the water;❖ availability of suﬃcient wavebands in the remotely-sensed data and the choice of
wavebands appropriate for the problem;❖ choice of inherent optical properties for the components;❖ errors in atmospheric correction; and❖ noise in the collected data.
The error estimates can be arrived at using sensitivity analyses on models, as well as bycomparison with in situ data. Comparison with in situ data would allow us to take into
account certain sources of errors that would remain unaccounted in formal sensitivity
analyses, such as errors in the model itself. However, due caution must be exercised to
avoid poor conclusions based on poorly matched data sets. In particular, when comparing satellite-derived data with in situ data, it is diﬃcult to match temporal and spatial
scales of the two types of observations. Diﬀerences arising from this mismatch must not
be mistaken for errors in algorithms.
Enough work has been done on establishing errors in Case 2 algorithms for us to knowthat the task is a diﬃcult one (see Chapter 3): more remains to be done to ensure thaterror information is routinely available. This is necessary, if we are to avoid that the
remotely-sensed data are not used inappropriately for any application.

119

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133

Acronyms and Abbreviations
ADEOS
ARIES
ATBD
CDOM
CPU
CZCS
DOC
EEA
EEC
GOOS
GLI
HAB
HPLC
ICAM
IFOV
IGBP
IMSL
IOC
IOP
IR
IRS
LIDAR
LOICZ
LUT
MERIS
MODIS
MOS
NEMO
NIR
NN
NTU
OCTS
PAR
PCA
PSICAM
PSU
SCOR

Advanced Earth Observing Satellite
Australian Resource Information and Environmental Satellite
Algorithm Theoretical Basis Document
Coloured Dissolved Organic Matter
Central Processing Unit
Coastal Zone Color Scanner
Dissolved Organic Carbon
European Environment Agency
European Economic Community
Global Ocean Observing Program
Global Imager
Harmful Algal Bloom
High Performance Liquid Chromatography
Integrating Cavity Absorption Meter
Instantaneous Field-of-View
International Geosphere-Biosphere ProgrammeInternational Mathematical and Statistical Library
Intergovernmental Oceanographic Commission
Inherent Optical Properties
InfraredIndian Remote Sensing Satellite
Light Detection And Ranging
Land-Ocean Interactions in the Coastal Zone
Look-up Table
Medium Resolution Imaging Spectrometer
Moderate Resolution Imaging Spectroradiometer
Modular Optoelectric Scanner
Naval EarthMap Observer
Near Infrared
Neural Network
Nephelometric Turbidity Unit
Ocean Colour and Temperature ScannerPhotosynthetically-Active Radiation
Principal Component Analysis
Point Source Integrating Cavity Absorption Meter
Practical Salinity Units
Scientiﬁc Committee on Oceanic Research134 • Ocean Colour in Optically-Complex Waters
SD
SeaWiFSSEISIMBIOS
SPM
sr
SST
TOA
TSRB
UV

Standard Deviation
Sea-viewing Wide Field-of-view Sensor
Special Event Imager
Sensor Intercomparison and Merger for Biologicaland Interdisciplinary Oceanic Studies
Suspended Particulate Matter
steradian (solid angle)
Sea Surface Temperature
Top of the Atmosphere
Tethered Spectral Radiometer Buoy
Ultraviolet

135

Mathematical Notations
SymbolDescription

Dimensions

Typical Unitsaabsorption coeﬃcient

L−1

m−1

A

albedodimensionlessa∗p

speciﬁc absorption coeﬃcient

L2 M−1m2 chl−1

L2 M−1m2 mg−1

speciﬁc absorption coeﬃcient of

L2 M−1 , or dimensionless if

m2 mg−1yellow substances

concentration measured as anof phytoplanktona∗sspeciﬁc absorption coeﬃcient of
inorganic suspended materiala∗yabsorption coeﬃcientap

absorption coeﬃcient of phytoplankton

L−1

m−1

as

absorption coeﬃcient of inorganic

L−1

m−1

L−1

m−1

suspended particles
ayabsorption coeﬃcient of yellow
substancesbscattering coeﬃcient

L−1

m−1

bb

back-scattering coeﬃcient

L−1

m−1

bR

Raman scattering coeﬃcient for water

L−1

m−1

bs

scattering coeﬃcient due to inorganic

L−1

m−1

suspended materialbu

upward scattering coeﬃcient

L−1

m−1

bw

scattering coeﬃcient for pure sea water

L−1

m−1

c

total volume attenuation coeﬃcient

L−1

m−1

E

net downward irradiance (Ed − Eu ),

MT−3

W m−2

energy per unit time and unit area
E0

total scalar irradiance (E0d + E0u )

MT−3

W m−2

Ed

downwelling irradiance

MT−3

W m−2

Eu

upwelling irradiance

MT−3

W m−2

E0d

downwelling scalar irradiance

MT−3

W m−2

E0u

upwelling scalar irradiance

MT−3

W m−2

H

depth of water columnLmdiﬀuse attenuation coeﬃcient for

L−1

m−1

L−1

m−1

Kd

downwelling irradianceKu

diﬀuse attenuation coeﬃcient for
upwelling irradiance136 • Ocean Colour in Optically-Complex Waters
Lradiance, energy per unit time, per

MT−3 sr−1

W m−2 sr−1

unit area, and per unit solid angle
Lmodmodelled radianceMT−3 sr−1

W m−2 sr−1

Lsat

radiance measured by the satellite sensor

MT−3 sr−1

W m−2 sr−1

Lu

spectral upwelling radianceMT−3 sr−1

W m−2 sr−1

R

reﬂectancedimensionless

RRS

remote-sensing reﬂectance

sr−1

sr−1

z

depth

L

m

β

volume scattering function

L−1 sr−1

m−1 sr−1

θzenith angleκattenuation coeﬃcient for upwelling irradiance

L−1

m−1

generated at a certain depth that reaches ashallower depth
λwavelength

L

nm

λem

emission wavelength

L

nm

λex

excitation wavelength

L

nm

µ

mean cosine for the net irradiancedimensionlessµdmean cosine for the downwelling irradiancedimensionlessµµmean cosine for the upwelling irradiancedimensionlessτaaerosol optical thickness

dimensionlessφazimuth angle

φF

ﬂuorescence quantum yield

dimensionlessχ

scattering angle

dimensionlessχ2

statistical chi-square test

dimensionless

137

Appendix: Participants at Case 2 Workshops

B
M
I

Berlin Workshop (20 April, 1999, DLR, Berlin, Germany)
Moss Landing Workshop (17 May, 1999, MBARI, Moss Landing, USA)
Ispra Workshop (14-18 June, 1999, JRC, Ispra, Italy)

M, I Robert A. Arnone
Head Ocean Color Section
Naval Research Laboratory
Stennis Space Center, MS 39529
USA
Email: arnone@nrlssc.navy.mil
I

Marcel Babin
Laboratoire de Physique etChemie Marines
Universite Pierre et Marie Curie et CNRS
B.P. 806238 Villefranche-sur-Mer Cedex
FranceEmail: marcel@obs-vlfr.fr

M William M. Balch
Bigelow Laboratories
180 McKown Point Road
P.O. Box 475
West Boothbay Harbor, ME 04575
USA
Email: bbalch@bigelow.org
B, I Jean-François Berthon
Joint Research Centre (EC)Space Applications InstituteMarine Environment Unit
I-21020 Ispra (VA)
Italy
Email: jean-francois.berthon@jrc.it

M

Juli Berwald
Hancock Institute for Marine Studies
College of Letters, Arts Sciences,
Bldg. AHF 302
University of Southern California
University Park
Los Angeles, CA 90089-0371
USA
Email: jberwald@earth.usc.edu

I

Robert P. Bukata
National Water Research Institute
Canada Centre for Inland Waters
P. O. Box 5050
867 Lakeshore Rd.
Burlington, ON L7R 4A6
CanadaEmail: robert.bukata@cciw.ca

B, M, I

Janet W. Campbell
Ocean Process Analysis Laboratory
Morse Hall
University of New Hampshire
Durham, NH 03824-3525
USA
Email: janet.campbell@unh.edu

138 • Ocean Colour in Optically-Complex Waters
I

Paolo Cipollini
James Rennell Division for
Ocean Circulation & Climate
Southampton Oceanography Centre
Southampton SO14 3ZH
England
Email: cipo@soc.soton.ac.uk

M, I Curtiss O. Davis
Code 7212
Naval Research Laboratory
Washington, DC 20375-5320
USA
Email: Davis@rsd.nrl.navy.mil
I

B,I

B

Roland Doerﬀer
GKSS Forschungszentrum
Institute of Hydrophysics
D-21502 Geesthacht
Germany
Email: doerﬀer@gkss.deMark D. Dowell
Joint Research Centre (EC)Space Applications InstituteMarine Environment Unit
I-21020 Ispra (VA)
Italy
Email: mark.dowell@unh.edu
Peter Gege
DLR, Institute of Optoelectronics
P.O.Box 1116
D-82230 Wessling
Germany
Email: peter.gege@dlr.de

M Jim F. R. Gower
IOS
PO Box 6000
Sidney, BC V8L 4B2
CanadaEmail: gowerj@dfo-mpo.gc.ca

B, I

Nicolas Hoepﬀner
Joint Research Centre of theEuropean Commission
Space Applications InstituteMarine Environment Unit
I-21020 Ispra (VA)
Italy
E-mail: nicolas.hoepﬀner@jrc.it

M

Chuanmin Hu
Dept. of Marine Science
University of South Florida
140 7th Ave. S
St. Petersburg, FL 33701
USA
Email: hu@carbon.marine.usf.edu

M

Dale A. Kiefer
Dept. of Biological Sciences,
Bldg. AHF 342
College of Letters, Arts & Sciences
University of Southern California
University Park
Los Angeles, CA 90089-0371
USA
Email: kiefer@physics.usc.edu

IJohn T. O. Kirk
Kirk Marine Optics
P.O. Box 117
Murrumbateman, NSW 2582
Australia
Email: jtokirk@ozemail.com.au

M, I

Motoaki Kishino
Photosynthesis Research Laboratory
Solar Energy Research GroupThe Institute of Physical andChemical Research (RIKEN)
2-1 Hirosawa, Wako
Saitama 351-0198
Japan
Email: kishino@postman.riken.go.jp

139
M Oleg V. Kopelevich
P.P. Shirshov Institute of Oceanology
Russian Academy of Sciences
Nakhimovsky Prospect, 36,
Moscow 117851
RussiaEmail: oleg@sio.rssi.ru

B, I

Andreas Neumann
German Aerospace Center
Remote Sensing Technology Institute
Rutherfordstr. 2
D-12489 Berlin
Germany
Email: andreas.neumann@dlr.de

B, I Harald Krawczyk
DLR German Aerospace
Research Establishment
Rudower Chaussee 5
D-12489 Berlin
Germany
Email: Harald.Krawczyk@dlr.de

IJohn S. Parslow
CSIRO Division of Fisheries
G. P. O. Box 1538
HobartTasmania 7001
Australia
Email: John.Parslow@marine.csiro.au

M Raphael M. KudelaMonterey Bay Aquarium
Research Institute
PO Box 628
Moss Landing, CA, 95039-0628
USA
Email: kura@mbari.org

B, I

Shubha Sathyendranath
Bedford Institute of Oceanography
P. O. Box 1006
Dartmouth, NS B2Y 4A2
CanadaEmail: shubha@is.dal.ca

B, IPeter Schlittenhardt
Joint Research Centre
Space Applications InstituteI-21020 Ispra (VA)
Italy
Email: peter.schlittenhardt@jrc.org

I

Gerald F. Moore
Plymouth Marine Laboratory
Prospect Place
Plymouth PL1 3DHEngland
Email: GFM@WPO.NERC.AC.UK
M

M James Nelson
Skidaway Instituteof Oceanography
10 Ocean Science Circle
Savannah, GA 31411
USA
Email: nelson@skio.peachnet.edu

Kusiel S. Shifrin
College of Oceanic &Atmospheric Studies
Oregon State University
104 Ocean Admin. Bldg.
Corvallis, OR 97331-5503
USA
Email: shifrink@oce.orst.edu

140 • Ocean Colour in Optically-Complex Waters
M David A. Siegel
Donald Bren School ofEnvironmental Science & Management
Dept. of Geography & Inst. for
Computational Earth System Science
University of California at Santa Barbara
Santa Barbara, CA 93106-3060
USA
Email: davey@icess.ucsb.edu
M Richard P. Stumpf
NOS HQTR. Route: N/SCI1
Bldg. SSMC4 Rm.: 9109
1305 East West Hwy.
Silver Spring, MD 20910-3281
USA
Email: Richard.Stumpf@noaa.gov