toward a global ocean system for measurements of optical

C h a p t e r

9Toward a Global Ocean System for

Measurements of Optical PropertiesUsing Remote Sensing and

In Situ Observations

Authors: G. C. Chang, T. Dickey, and M. Lewis

Observations of the optical properties of the ocean are important for resolving several is-sues, including primary productivity, biogeochemical cycling, health of the ocean (e.g.,harmful algal blooms), upper ocean heating, and underwater visibility. The present reviewdescribes some enabling technologies that can be used to greatly increase the variety andquantity of optical observations and to expand their time-space sampling domains. Thetechnologies explained here involve a variety of optical sensors and systems and oceanobserving platforms. Remote sensing of ocean color from aircraft- and satellite-borne in-struments is vital to obtain regional- and global-scale optical data synoptically. In situ ob-servations are essential for calibration and validation of remotely sensed data as well as foralgorithm development. In situ systems also provide complementary subsurface and hightemporal resolution data sets. Developing a framework for synthesizing optical data ob-tained from state-of-the-art and emerging optical sensors and oceanographic platforms andfor utilizing these data in predictive models is an important challenge.

9.1 INTRODUCTION

The temporal and spatial variability of oceanic optical and bio-optical properties is funda-mental to many physical, biological, and chemical processes in the sea (Dickey and Falkowski2002). The subdiscipline of ocean optics concerns the study of light and its propagationthrough the ocean, whereas bio-optics connotes biological effects on optical properties andvice versa. For example, light is essential for the primary formation of biomass by oceanicphytoplankton, which forms the base for all higher trophic level organisms in the sea. Lightis also crucial to the ecology of the upper ocean and biogeochemical cycling; the produc-tion and destruction of many chemical compounds are affected through photochemical re-actions. Heating and stratification of the upper ocean are driven by the penetration of solarradiation. Absorption and scattering of light by water and its particulate and dissolved con-

306 Toward a Global Ocean System for Measurements of Optical Properties Using Remote Sensing and In Situ Observations

stituents modulate local heating and represent significant sources of variability in the heatbudget of the upper ocean. In addition, optical properties are important indicators of thehealth of the ocean in the form of changing water clarity, diversity, distributions of species(indigenous and non-indigenous), and harmful algal blooms (e.g., red tides) and associatedtoxins. Underwater visibility also depends on optical properties with practical applicationsranging from diver visibility to coastal bathymetric mapping. Introductions to the subdisci-plines of ocean optics and bio-optics are provided in books by Kirk (1994), Mobley (1994),and Spinrad et al. (1994); a brief overview of the present state of ocean optics research isgiven by Ackleson (2001) and Cleveland (2004). Several recent papers focus on new tech-nologies applied to ocean optics and bio-optics (Dickey 2001a, b, 2002; Dickey and Chang2001; Maffione 2001; Chang et al. 2003).

This chapter is intended to provide 1) a brief review of relevant concepts and defini-tions for ocean optics, 2) an introduction to some of the important sensor and platformtechnologies that can facilitate development of an integrated optical measurement systemfor the world ocean, 3) a description of opportunities for initiating and implementing thesystem, 4) ideas for optimizing the utility of the system, and 5) examples of recent andongoing optical programs.

9.2 CONCEPTS IN OCEAN OPTICS

Before describing modern optical instrumentation and methodologies, a few concepts anddefinitions are introduced (see Kirk 1994; Mobley 1994; Dickey 2002; or Chapter 4 of thisvolume for definitions, diagrams, and mathematical formulations). Readers familiar withthe fundamental principles of ocean optics may wish to proceed to the next section.

9.2.1 Apparent Optical Properties

It is convenient to classify bulk optical properties of the ocean as either inherent or appar-ent. Apparent optical properties (AOPs)—radiance and irradiance—depend on the con-stituents of the aquatic medium (pure seawater, phytoplankton, detritus, and gelbstoff), theangular distribution of solar radiation (i.e., the geometry of the subsurface ambient lightfield), and the wavelength of the electromagnetic radiation. Radiance, L(2,M,8), is definedas the radiant flux at a specified point in a given direction per unit solid angle, per unit areaperpendicular to the direction of light propagation, per unit wavelength (units of W [orquanta s-1] m

-2 sr-1 nm-1). Zenith angle, 2, is the angle between a vertical line perpendicularto a flat plate and an incident light beam, and azimuthal angle, M, is the angle with respectto a reference line in the plane of the flat plate.

Irradiance is the radiant flux per unit surface area per unit wavelength (units of W m-2

nm-1 or quanta [or photons] m-2 s

-1 nm-1 or mol quanta [or photons] m-2

s-1

nm-1). Downwellingirradiance, Ed(8), is the irradiance of a downwelling light stream impinging on the top faceof a horizontal surface (ideally, a flat light collector oriented perpendicular to the localgravity vector), and upwelling irradiance, Eu(8), is the irradiance of an upwelling lightstream impinging on the bottom face of a horizontal surface. Net downward irradiance isthe difference Ed(8) - Eu(8), or the integral of L(2,M,8) cos 2 over the entire sphere (fullsolid angle 4B). Scalar irradiance, E0(8), is defined as the integral of L(2,M,8) over theentire sphere. It should be noted that all definitions given above are for a specific wave-length of light. If one integrates scalar irradiance over the visible wavelengths (400 to 700nm; visible range is sometimes defined to be 350 to 700 nm), then the biologically impor-tant quantity called photosynthetically available radiation, or PAR, is obtained. Irradiance

9.2 Concepts in Ocean Optics 307

measurements are fundamentally important for quantifying the amount of light availablefor photosynthesis and for radiative transfer theory, i.e., the theory that relates the AOPs tothe inherent optical properties (IOPs; see below).

Other AOPs of great practical interest for remote sensing are the irradiance reflectance,R(8) = Eu(8)/Ed(8), and the so-called spectral radiance reflectance, rrs(8) = Lu(8)/Ed(8) (sr-1,generally at nadir, see Morel and Gentili 1996). When taken just above the sea-surface, thisis often termed the remote sensing reflectance, Rrs(8); multiplication by the extraterrestrialsolar spectral irradiance yields the normalized water leaving radiance, Lwn(8).

9.2.2 Inherent Optical Properties (IOPs)

Inherent optical properties (IOPs) are dependent only on the aquatic medium and are inde-pendent of the ambient light field and its geometrical distribution. Photons in an aquaticmedium can only be absorbed or scattered. The absorption and scattering properties aredistinguished in terms of the IOPs: spectral absorption coefficient, a(8); spectral scatteringcoefficient, b(8); and the spectral volume scattering function, $(R,8), where 8 is light wave-length and R is the scattering angle. The sum of the absorption and scattering coefficients isdefined as the spectral beam attenuation coefficient, c(8) = a(8) + b(8) (sometimes denotedas beam c). The IOPs a(8), b(8), and c(8) all have units of m-1. $(R,8) represents the scat-tered intensity of light per unit incident irradiance per unit volume of water at some angle Rinto solid angle element )S. Units for $(R,8) are m-1 sr-1. The spectral scattering coeffi-cient, b(8), is obtained by integrating the volume scattering function over all directions(solid angles). The forward scattering coefficient, bf, is obtained by integrating over theforward-looking hemisphere (R = 0 to B/2) and the backward scattering coefficient, bb, iscalculated by integrating over the backward-looking hemisphere (R = B/2 to B). The spec-tral volume scattering phase function normalizes the volume scattering function by the totalscattering and is the ratio of $(R,8) to b(8) with units of sr-1. The proportion of light that isscattered versus absorbed is characterized by the spectral single-scattering albedo, T0(8) =b(8)/c(8).

Variations in light absorption and scattering can generally be attributed to variability infour constituents of the aquatic medium in natural waters: pure seawater (w), phytoplank-ton (ph), detritus (d), and gelbstoff (g) (e.g., Roesler et al. 1989; Bricaud et al. 1998), al-though in many situations, mineralogenic or biogenic particles, or bubbles, can dominatethe scattering field in particular (Zhang et al. 1998). Therefore, it is useful to partition thetotal absorption, total scattering, and total beam attenuation coefficients, at(8), bt(8), andct(8), respectively, in terms of the contributing constituents such that:

at(8) = aw(8) + aph(8) + ad(8) + ag(8),bt(8) = bw(8) + bph(8) + bd(8)

and

ct(8) = cw(8) + cph(8) + cd(8) + cg(8).

Detritus is the term representing non-chlorophyll containing particles of organic orinorganic origin (e.g., sediment, fecal material, plant and animal fragments, etc.) and gelbstoff(sometimes called yellow matter or gilvin) is the term for optically active dissolved organicmaterial. The contribution of gelbstoff to scattering is small relative to the other terms, andis hence normally neglected. The spectral absorption coefficient of pure seawater is wellcharacterized with greater absorption in the red than blue portions of the visible spectrum.Detrital and gelbstoff absorption spectra tend to decrease monotonically with increasing


wavelength and can be modeled using an exponential function. Phytoplankton spectral ab-sorption varies significantly in relation to community composition and environmentalchanges. Characteristic peaks are typically found near wavelengths of 440 nm and 683 nmand are related to chlorophyll-a. Other peaks and troughs are controlled by phytoplanktonpigments and pigment packaging, which vary according to community composition and thelight field. Bio-optical research often focuses on the spectral, temporal, and spatial variabil-ity of aph(8).

9.2.3 Quasi-inherent Optical Properties

The vertical diffuse attenuation coefficients for radiance, KL(8), and irradiance, Kd(8), aredefined as the logarithmic derivative of the radiometric quantity with respect to depth (Kirk1994). These coefficients are referred to as quasi-inherent optical properties because theyare properties that are dependent on the ambient light field, yet behave similarly to theIOPs, particularly a(8). For example, Kd(8) (here we suppress depth dependence notationfor convenience), the vertical spectral diffuse attenuation coefficient of downwelling irra-diance, can be separated into the four contributions of the various constituents (e.g., water,phytoplankton, detritus, and gelbstoff) in analogy to the absorption coefficients definedabove (see Morel 1988; Gordon et al. 1988; Morel and Maritorena 2001). Kd(8) has thesame units (m-1) as the IOPs a(8), b(8), and c(8). At the same time, Kd(8) is dependent onthe angular distribution of the light field since it is defined as a property of theradiation field.

9.2.4 Relationships Between IOPs and AOPs

Relationships between IOPs and AOPs are central to developing quantitative models ofspectral irradiance in the ocean (Morel 1988; Gordon et al. 1988; Kirk 1994; Mobley 1994).Radiative transfer theory is used to provide a mathematical formalism to link IOPs and thegeometric radiance distribution (as influenced by the surface radiance distribution, and sea-surface and bottom characteristics) to the AOPs of the water column. One motivation of thisresearch is to predict AOPs, given environmental forcing conditions and actual measure-ments or best estimates of IOPs. Another related, inverse goal is to estimate or determinethe vertical and ideally horizontal structure of IOPs and their temporal variability givennormalized spectral water leaving radiance, Lwn(8), measured remotely from satellite orairplane color sensing imagers. The extrapolation of subsurface values of Lu(8) to the sur-face has been the subject of considerable research because a major requirement of oceancolor remote sensing is to match in situ determinations to those from satellite- or airplane-based spectral radiometers that necessarily must account for the effects of clouds, aerosols,and surface reflectances as well (e.g., Hooker and McClain 2000; Fargion and Mueller2000; Pinkerton et al. 2003).

Exact relations between inherent and apparent optical properties can be derived fromthe Gershun equation (e.g., Mobley 1994), but can be simplified and validated against nu-merical solutions to the radiative transfer equation. For example, the reflectance, R(8), hasbeen found to be a function of bb/(a + bb) or bb/a from radiative transfer theory (e.g., Gordonet al. 1975) and confirmed from Monte Carlo calculations for waters characterized by b/avalues ranging from 1.0 to 5.0 (Kirk 1994). The simple relationships between IOPs andAOPs break down during periods of low sun angle and/or when highly reflective organismsor their products (coccolithophores and coccoliths) are present (Stramska and Frye 1997;Zheng et al. 2002).

9.3 Optical Sensor Technologies 309

9.3 OPTICAL SENSOR TECHNOLOGIES

In this section, several sensors and systems used for observing the subsurface light field andoptical properties are described. One of the earliest cruises dedicated to optical oceano-graphic research was conducted by Otto von Kotzebue during a circumnavigational voyagein 1817 (Højerslev 1994). von Kotzebue first used a lowered red cloth and later a simplewhite plate to determine the depth of penetration of light. Analogous studies using a Secchidisk, a white disk 25 cm in diameter whose depth of disappearance is defined as the Secchidepth, began in the late 1860s. More recently, research utilizing Secchi disk data have beenused to describe regional differences and long-term changes in the optical clarity of theworld oceans. For example, Lewis et al. (1988) used an empirical formula to translateglobal Secchi depth data into estimates of chlorophyll concentration. The inferred concen-trations were within a few percent of Coastal Zone Color Scanner (CZCS) satellite-basedestimates. Falkowski and Wilson (1992) used a 90-year record of Secchi depthobservations in the North Pacific Ocean to determine that small systematic increases inphytoplankton have occurred on the edges of the central ocean gyre, while the gyre core hasundergone a small depletion of phytoplankton. Many of the earlier optical devices for oceanmeasurements are described by Højerslev (1994), Kirk (1994), and Jerlov (1976). Jerlovfirst compiled optical data for the world ocean. Here we focus on sensors and systems andplatforms that are presently in use or are being developed (also see Dickey 2001a, b, 2002;Maffione 2001).

9.3.1 In Situ Measurements of AOPs

Instrument packages designed for measuring AOPs from moorings are shown in Figures9-1, 9-2, and 9-3. One of the more commonly used instruments for measuring AOPs is arelatively simple broadband scalar irradiance sensor (E0 or PAR) because of the need fordetermining the availability of light for phytoplankton. A spherical light collector made ofdiffusing plastic or opal glass receives the light from approximately 4B steradians and aphotodetector records the output voltage. Analogous sensors use flat plate cosine or hemi-spherical collectors. Spectroradiometers are radiance, irradiance, or scalar irradiance metersthat use a variable monochromator placed between a light collector and a photodetector(e.g., Smith et al. 1984). Light separation can be achieved by using sets of interferencefilters (usually 10 nm in bandwidth) selected for particular purposes such as investigatingabsorption peaks and hinge points for pigment analyses. Higher spectral resolution(hyperspectral; <10 nm resolution from 350 to 800 nm) (Chang et al. 2004) can be achievedby using grating monochromators. Cosine (flat-plate), spherical, or hemispherical collec-tors are used for several different measurements or calculations of the AOPs describedabove (Ed, Lu, Kd, etc.). Irradiance cosine collectors are designed such that their responsesto parallel radiant flux are proportional to the cosine of the angle between the normal to thecollector surface and the direction of the radiant flux. Radiance sensors are designed toaccept light over a small solid angle (typically a viewing angle of 10° to 15°). Irradianceand radiance instruments are calibrated using well-characterized, standard light sources(e.g., National Institute of Standards and Technology). Spectral radiometric measurementsof AOPs have been far more commonly made than measurements of IOPs, and form thebasis for compilations on which new algorithms relating optical and biological quantitiescan be developed (e.g., Lewis and McLean 1999; Morel and Maritorena 2001). Because ofthe growing concern about UV radiation, a number of instruments are designed to measureinto the UV portion of the electromagnetic spectrum as well.


Figure 9-1 Schematic diagrams illustrating several different optical and bio-optical systems that have been deployedfrom moorings. Courtesy: Derek Manov, UCSB.

Figure 9-2 Photograph of a moored optical system that is used to measure chlorophyll fluorescence, the volume scatter-ing function, upwelling radiance (3 wavelengths), and downwelling irradiance (3 wavelengths). Insert photograph shows aspecial copper shutter device for minimizing biofouling of the sensors. Courtesy: Derek Manov, UCSB.

9.3 Optical Sensor Technologies 311

Figure 9-3 Photograph of a moored optical system used to measure PAR (scalar and vector), upwelling radiance (7wavelengths), downwelling irradiance (7 wavelengths), pressure, temperature, tilt, and dissolved oxygen. Courtesy: DerekManov, UCSB.

9.3.2 In Situ Measurements of IOPs

Systems for obtaining IOP data from moorings are illustrated in Figures 9-1 and 9-2. Beamtransmissometers measure the beam attenuation coefficient, beam c, to derive suspendedsediment volume, phytoplankton biomass, and concentrations of particulate organic carbon(POC) (Bishop 1999) and productivity in the form of POC. The principle of operation of abeam transmissometer involves the measurement of the proportion of an emitted beam thatis lost through both absorption and scattering as it passes to a detector through a pre-deter-mined pathlength. Beam transmissometers have often used a red light emitting diode (660nm) for the collimated light source. The wavelength of 660 nm was originally selected inorder to minimize attenuation by gelbstoff, which attenuates strongly at shorter wavelengthsbut minimally in the red.


Within the past decade, in situ spectral absorption-attenuation meters have becomecommercially available (Moore et al. 1992). These concurrently measure spectral absorp-tion and attenuation coefficients at three or nine wavelengths (ac-3 or ac-9) for spectralsignatures of both particulate and dissolved material. Similar ac-instruments have recentlybeen developed to measure a(8) and c(8) at 90 wavelengths from 400 to 730 nm with ~3.3nm resolution (ac-s). These devices use two tubes through which seawater is pumped. Theinside of the beam attenuation coefficient tube (c-tube) is flat black to minimize reflections,whereas the absorption coefficient tube (a-tube) is reflective in order to maximize internalreflection to better estimate absorption. Several investigators have utilized 0.2 :m filters onthe intake of the absorption tube to directly measure absorption by gelbstoff. Absorption-attenuation meter data and spectral decomposition models can be used to provide in situestimates of aph(8), ad(8), and ag(8) (Roesler et al. 1989; Bricaud and Stramski 1990; Changand Dickey 1999; Gallegos and Neale 2002; Schofield et al. 2004). The spectral scatteringcoefficient can be computed from ac-meter data by simply performing the differenceb = c - a.

Radiative transfer models and interpretation of remote sensing images rely upon ab-sorption, backscattering, and volume scattering function data to estimate the propagation oflight (Gordon 1994; Mobley 1994). Measurements of absorption have been discussed above.However, until recently, few instruments existed for the measurement of the volume scat-tering function (VSF). Historical data collected in the 1970s (Petzold 1972) were com-monly used in lieu of direct measurements. Mobley et al. (2002) showed that radiativetransfer model-derived AOPs are sensitive to the shape of the scattering phase function;therefore, the use of standard VSF measurements can lead to erroneous results. Some re-cently developed VSF instruments measure at fixed numbers of angles, with the functionalform being determined using theory and polynomial curve fits (Moore et al. 2000; Danaand Maffione 2000). Another new instrument resolves the entire VSF from 0.5° to 179°with an angular resolution of 0.3° (Lee and Lewis 2003) and has been used to predict theobserved radiance distribution far more accurately than with the use of a standard VSF. Thetotal scattering (b) and the backscattering coefficient, bb, is then estimated through directintegration of the measured VSF over the appropriate angular range.

Backscattering can also be estimated using instruments that measure scattering over asingle fixed angular range (e.g., 140° +/-10°) at 6 different wavelengths, with the backscat-tering coefficient estimation based on scattering theory and statistical relationships relatingscattering at a given angle to the integral over the backward direction (Maffione and Dana1997; Boss and Pegau 2001). Backscattering variations are related to the changes in the sizedistributions, shapes, and compositions (index of refraction) of particles (Twardowski et al.2001; Boss et al. 2004). In situ particle size distributions can be measured using laser(Frauenhofer) diffraction instruments (Agrawal and Pottsmith 1994) that employ chargedcoupled device array photodetectors with linear or circular ring geometries. Particles in therange of 5 to 500 :m can be measured with resolution dependent upon the number ofindividual detector rings. Modified versions of these instruments measure particlesettling velocities.

9.3.3 Above-water Sensors for Measurement of Oceanic Optical Properties

Measurements of oceanic optical properties from above the surface, from shipboard, air-craft, or satellite platforms (see Section 9.4), involve both passive observations (e.g., mea-surements that rely on the Sun for illumination of the ocean surface), and active methods,which generally employ laser illumination. The former are largely imaging spectrometersthat measure the radiance entering the aperture of the sensor, with a variety of scanning

9.4 Platforms 313

mechanisms used to generate two-dimensional fields or images. For these, significant com-plications arise from the specular reflection from the sea surface (glint), the high degree ofvariability in atmospheric scattering and absorption, and the bidirectionality of both thedownwelling and the upwelling radiance distributions, all of which must be corrected inorder to retrieve the desired radiance leaving the ocean surface. As well, the surfacedownwelling irradiance must be measured or estimated to generate the normalization nec-essary to compare different oceans at different times in a quantitatively meaningful way.Finally, the above-water sensors measure radiances emitted only over the upper opticaldepth (roughly scaled as 1/KL; also weighting decreasing exponentially with depth), typi-cally ranging from <1 m in coastal waters to a maximum on order of 35 m in clear, openocean regions.

The remotely sensed observations of radiances can be used to infer concentrations ofbiological quantities and their dynamics (see chapter 4 of this volume). For example, re-mote sensing estimates of pigment concentrations (typically chlorophyll-a or chlorophyll-a+ phaeopigments), often use empirical relationships involving ratios such as water leavingradiance at a few chosen wavelengths, e.g., Lwn(443 nm)/Lwn(550 nm), or remote sensingreflectance at different wavelengths, e.g., Rrs(490 nm)/Rrs(555 nm), or these ratios withother wavelength combinations (see O’Reilly et al. 1998). Work is progressing in remotesensing of other important oceanic variables including beam attenuation coefficient (Roeslerand Boss 2003), colored dissolved organic matter (CDOM) (Kahru and Mitchell 2001), andsignatures of different phytoplankton taxa (Hill et al. 2000), including those associated withred tides or harmful algal blooms. In addition, remotely sensed optical data are being usedto estimate primary productivity (e.g., Behrenfeld and Falkowski 1997).

Sensors that employ active illumination methods (e.g., LIght Depth And Ranging—lidar) avoid some of the problems associated with reliance on the Sun. Since the illumina-tion geometries are less variable, the systems can operate at night, and the laser power issuch that depth ranges can be probed that are 3–5 times deeper than passive methods. Fur-ther, the lidar systems can be range-gated to examine the vertical structure of the watercolumn, including the depth and nature of the sea bottom. To date, such systems have beenfielded on aircraft and shipboard platforms; they are particularly useful for large-scale bathy-metric surveys and focused high spatial resolution bio-optical studies.

9.4 PLATFORMS

The judicious placement and utilization of a variety of complementary ocean observingplatforms (Figures 9-4 and 9-5) along with numerical models can greatly improve our in-formational databases and ultimately our knowledge and capabilities for making predic-tions of upper ocean ecology, health of the ocean, biogeochemistry, water column visibility,etc. (Dickey 1991, 2001a, b). Some challenges for making these oceanographic predictionsare: 1) there are often tens if not hundreds of relevant variables involved, 2) many of theoceanographic properties are non-conservative, 3) processes are typically nonlinear andcoupled through several different multidisciplinary variables, 4) physiological and behav-ioral aspects are frequently important for ocean biology and indirectly for optics, 5) costs ofdoing ocean measurements from satellites and in situ platforms are relatively high, and 6)the assimilation of optical data into forecast models is in its infancy. Multiple platforms areneeded to fill in the time-space continuum of oceanographic processes because of specificplatform advantages and limitations (Figure 9-4). In the next few paragraphs, some of theplatforms and data telemetry systems that will likely be important elements of a globalobserving system are described.


9.4.1 Remote Sensing from the Air and Space

Above-water optical measurements, from satellites or aircraft, provide nearly synoptic ob-servations over the ocean; they are restricted however, to the upper optical depth. Initialobservations of the spectral reflectance of the ocean, “ocean color,” from airborne sensors

Figure 9-4 Time-horizontal space diagram illustrating physical and biological processes overlain with sampling domainsof various platforms (moorings, satellites, AUVs, etc.). Courtesy: Tommy Dickey.

Figure 9-5 Conceptual diagram showing a variety of sampling platforms. Most of these have been deployed in thevicinity of the island of Bermuda. Courtesy: Frank Spada.

9.4 Platforms 315

Satellite-based sensors are now capable of providing regional and nearly global synop-tic views and data over much larger areas of the oceans and ice than possible from any otherplatform. They are complemented by a large array of specialized aircraft sensors deployedfor specific experiments and high resolution surveys. An example of an airborne hyperspectralimaging system is shown in Figure 9-7. Recently, unmanned aircraft with sensor payloadshave provided new flexibility for these purposes. The quantification and utility of space-based as well as aircraft-based ocean color observations are fundamentally dependent onlaboratory calibrations of sensors, spacecraft calibrations, atmospheric corrections, sea sur-face and subsurface data for groundtruthing satellite sensor data, and algorithm develop-ment (Hooker and McClain 2000; Fargion and Mueller 2000; Pinkerton et al. 2003).

Remote sensing data are typically empirical inferences of surface signals and are oftenfounded on groundtruth data sets obtained from ocean-based platforms. Electromagneticradiation only penetrates to very shallow ocean depths (infrared to millimeters and visibleto meters—an optical depth) (Gordon and McCluney 1975; Barnard et al. 2000; Frette et al.2001). In addition, visible wavelengths cannot effectively penetrate water clouds, and dust.The nature of orbital mechanics as well imposes restrictions on both revisit frequencies andthe nature of the illumination and viewing geometries, while aircraft sensors are restrictedin terms of spatial coverage and are not able to be deployed for long periods. Therefore,satellite and aircraft information must be complemented with in situ observations, as de-scribed in Section 9.4.2, to provide continuous time series and to characterize importantsubsurface ocean properties.

(Yentsch 1960; Lorenzen 1969) provided the basis for satellite measurement systems initi-ated in 1978 by the CZCS (Gordon et al. 1985), which provided uneven coverage until itsdemise in 1986. Satellite-based ocean color was not obtained again until the launch of theJapanese Ocean Color and Temperature Sensor in August 1996, which provided globalocean color data until its early demise in June 1997. Thus far, the most outstanding timeseries of global ocean color has come from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) (Figure 9-6) launched in 1997, which continues to function to the present. Ahandful of satellites collect ocean color data now, and it is expected that within the nextdecade several more ocean color satellites will be placed in orbit (e.g., see InternationalOcean-Colour Coordinating Group (IOCCG 1998) (Table 9-1). The ability to merge thedata sets from these different sensors to produce a consistent spatial time series of oceanicbio-optical properties will be a critical issue.

Figure 9-6 Artist’s conception of the satellite (left) and photograph of the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) for ocean color imaging at seven wavelengths (right). Courtesy: NASA Goddard Space Flight Center.


Table 9-1 Current and future (planned) ocean color satellites (information from IOCCG website: http://www.ioccg.org/).

Figure 9-7 Photograph of the Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS), ahyperspectral imaging spectrometer operating over the wavelength range 400 to 1000 nm with 1.7 nm spectral resolution.Courtesy: HyCODE Photo Gallery.

9.4 Platforms 317

9.4.2 Ships

Ships are valuable assets for the following functions: 1) direct, detailed process-orientedobservations for specific research activities; 2) data collection at depth and over long dis-tances during transects; and 3) deployment of other sampling platforms such as moorings,drifters, floats, and autonomous underwater vehicles (AUVs). Useful modes of ship sam-pling include: 1) on-station vertical profiling of instruments; 2) underway sampling of sur-face waters using flow-through systems (e.g., Balch et al. 2001); and 3) underway samplingusing towed undulating or fixed depth bodies or chains that act as platforms for sensorsuites. These various modes of sampling are especially useful for regional process-orientedstudies and for long transect sampling programs designed to provide important spatial maps.The Atlantic Meridional Transect program (described by Aiken et al. 2000 and Hooker andMcClain 2000) and Joint Global Ocean Flux Study (JGOFS) Global Survey program (Marraet al. 1991) are examples of research-driven long transect sampling programs that havebeen devoted to or included optical measurements.

One of the advantages of ship sampling is that calibrations and cleaning of the opticalwindows or tubes of instruments can be performed between profile casts to provide moreaccurate, freshly calibrated, non-biofouled optical and bio-optical data. A second advantageof ships is that advanced analytical instrumentation that cannot presently be routinely de-ployed from other in situ platforms can be utilized. Examples of such instrumentation in-clude: flow cytometers, mass spectrometers, “clean” methods for ocean chemistry, and ra-dioactive measurement systems. In addition, ships remain the only viable platform capableof collecting biological samples with net tows and large volumes of water for optical andpigment analyses using spectrophotometers, fluorometers, and High Performance LiquidChromatography. Ships have limitations in terms of their high cost, limited availability, andrestricted synopticity in sampling. As well, they are often precluded from working in heavyweather and sea-state conditions, which are of considerable interest for the study of air-seainteractions and biological processes. Nonetheless, physical oceanographers andmeteorologists have developed effective Volunteer Observing Ship and Ship-of-Opportu-nity programs to make basic measurements. More recently, carbon dioxide measurementshave been supported through such voluntary programs and optical measurements could beadded with relatively little difficulty.

9.4.3 Fixed (Eulerian) Platforms: Moorings, Bottom Tripods, and Offshore Platforms

Researchers and environmental agencies are using mooring, bottom tripod, and offshoreplatform measurement systems and sensors to study environmental changes in the ocean ontime scales from minutes to years (Figure 9-4). An increasing number of optical, bio-opti-cal, chemical, geological, and acoustical parameters are being measured from these plat-forms (example mooring shown in Figure 9-8). This work has led to discoveries of newinterdisciplinary processes such as primary production variability associated with El Niño-Southern Oscillation (ENSO) and equatorial longwaves (Foley et al. 1997; Chavez et al.1999; Turk et al. 2001), sediment resuspension through internal solitary waves and storms(Bogucki et al. 1997; Dickey et al. 1998d; Chang et al. 2001; Chang and Dickey 2001),cloud-induced and diel fluctuations in phytoplankton biomass (Stramska and Dickey 1992,1998), phytoplankton blooms associated with incipient seasonal stratification (Dickey et al.1994), frontal- and eddy-trapped inertial waves (Granata et al. 1995), and mesoscale vari-ability (e.g., reviews by Dickey 2001b; Dickey and Chang 2001; Dickey and Falkowski2002; Lewis 2001). Several different oceanic regions have been studied using interdiscipli-nary moored systems. These regions include both open ocean and coastal settings that range


from the equatorial Pacific to high latitude areas south of Iceland and in the Southern Ocean(Abbott et al. 2000). Locations where moorings have been used specifically for calibratingand validating ocean color sensors have included sites off Lanai, Hawaii (Clark et al. 1997),in Monterey Bay (Chavez et al. 1997), in the Sea of Japan/East Sea (Mitomi et al. 1998), inthe equatorial Pacific (Chavez et al. 1999), off the south coast of Plymouth, England(Pinkerton and Aiken 1999), and off Bermuda (Figure 9-8) (Dickey et al. 1998a, 2001a) (asdescribed in Section 9.6.1). Because of biofouling of sensors, useful data from mooringshave often been limited to a few months in the open ocean and less in coastal waters (Daviset al. 2000). However, work is underway to mitigate this problem (Chavez et al. 2000;Dickey et al. 2001b; Manov et al. 2004).

Benthic processes may be studied and monitored using optical instrumentation de-ployed on bottom tripods. Bottom tripods and their instrumentation may be placed in virtu-ally the same environments as moorings using similar suites of sensors and samplersdeployable from moorings. Beam transmissometers, particle sizing devices, optical settlingtubes, and photographic cameras mounted on tripods have been used to investigate sedi-ment resuspension and settling, bedform formation and movement, bioturbation, and floc-culation/deflocculation (Trowbridge and Nowell 1994; Lampitt and Antia 1997; Chang etal. 2001; Hill et al. 2001).

Figure 9-8 Site locator map and schematic diagram of the Bermuda Testbed Mooring (BTM). Courtesy: Frank Spada.

9.4 Platforms 319

Figure 9-9 Line drawing of the K-SOLO profiling float that is used to measure vertical profiles of the spectral diffuseattenuation coefficient. Courtesy: B. Gregory Mitchell, Ocean Optics/ONR. Source: Mitchell et al. 2000.

Offshore platforms, including research-dedicated and oil production platforms, pro-vide opportunities for conducting oceanic and meteorological research and monitoring.These often large and very stable platforms typically have space and facilities for mannedresearch laboratories and are equipped with adequate power, making them ideal for oceano-graphic observations. They offer several advantages over shipboard platforms, includingabsolute stability in high sea states, suitability for time series measurements, and capabilityfor housing personnel. It should be possible to launch AUVs and other mobile samplingdevices from these platforms for spatial sampling as well. Zibordi et al. (1999) have re-ported the use of a tower offshore of Venice, Italy, for optical remote sensing calibrationand validation. Coastal sites with piers may also be used. Unfortunately, instrument shad-ing is problematic for many of these large platforms.

9.4.4 Lagrangian Platforms: Drifters and Profiling Floats

Drifters and profiling floats can provide spatial data by effectively following water parcels(Foley et al. 1997; Landry et al. 1997; Abbott et al. 1990; Moisan et al. 2001). Drifters andprofiling floats (Figure 9-9) can collect optical data in regions of the world oceans that arerarely visited by oceanographic cruises and effectively provide data in portions of the time-space domain that are inaccessible by satellites and other in situ platforms (Figure 9-4).Drifters and profiling floats now utilize the Global Positioning System, giving very accu-rate position data (less than tens of meters down to a few meters). Drifter data allow de-tailed examinations of bio-optical processes on short time and space scales and the evalua-tion of de-correlation scales of chlorophyll and physical variables (e.g., Abbott and Letelier1998).


Early floats typically moved at pre-determined depths, and often at the sea-surface(e.g., Foley et al. 1997). More recently, profiling floats, using buoyancy changes to movevertically, have provided data collected during their rise and descent through the watercolumn after surfacing using satellite data telemetry. Some investigators are testing profil-ing floats adapted for bio-optical measurements (Zaneveld 2001). For example, Mitchell etal. (2000) instrumented a Sounding Oceanographic Lagrangian Observer (SOLO) (Davis etal. 2001) profiling float (K-SOLO) (Figure 9-9) to measure diffuse attenuation coefficientat three wavelengths and collected data from the Sea of Japan/East Sea. Also, optical instru-ments for determining particulate organic carbon and particulate inorganic carbon weredeveloped and tested using SOLO floats (called C-Argo floats) by Bishop et al. (2001).These profiling floats were designed as part of the Argo profiling float program (http://www.argo.ucsd.edu/), which is currently deploying 3,000 free-drifting profiling floatsthroughout the world’s oceans (see Section 9.5.2 for more information about Argo floats).The routine deployment of optical sensors on floats faces several obstacles, e.g., biofouling,length of deployment, sampling restrictions, validation of data, etc.

Finally, the ultra-stable manned research platform R/P FLIP was used for concurrentbio-optical, meteorological, physical, and chemical time series measurements in the NorthPacific sub-tropical gyre (34°N, 142°W) in 1982 during the Optical Dynamics Experiment(ODEX) (e.g., Dickey et al. 1986). Profiled optical instruments included an early version ofa spectroradiometer, beam transmissometer (660 nm), fluorometer, and PAR sensor. Thus,time series of Kd(8,z), chlorophyll(z), beam c(z), and physical variables includingtemperature(z), salinity(z), and currents(z) were obtained. R/P FLIP is ~108 m in lengthand is roughly 85% submerged during operations, resulting in minimal vertical or pitchingmotion because of its low center of gravity. For ODEX, R/P FLIP was not moored andallowed to drift. R/P FLIP was not designed to be a Lagrangian drifter and should optimallybe moored for Eulerian measurements. Thus, ODEX measurements were somewhat moredifficult to interpret, especially for time scales greater than a few days. However, relativelyhigh temporal (down to 15-minute) and vertical spatial (~1-m) data were obtained and gaveearly evidence of important bio-optical variability at scales of less than an hour to a day, aswell as results showing vertical step-like structure in optical properties that often were wellcorrelated with physical variables. Future specialized experiments could benefit from useof platforms such as R/P FLIP.

9.4.5 Autonomous Underwater Vehicles (AUVs)

Autonomous underwater vehicles (AUVs) originated in the 1970s. A description of thehistory and present and future capabilities of AUVs is provided by Griffiths et al. (2001).Most of the activities have focused on engineering design and testing. However, a numberof recent programs have begun to exploit these vehicles for scientific studies, includingoptical measurements (Griffiths et al. 1999; Yu et al. 2002; Brown et al. 2004) (Figure 9-10,top). This has become possible because of the development of new optical sensors andsystems that are relatively small in size, consume moderate power, and interface to theAUVs. Some of the advantages of the autonomous platforms include cost per deployment,capability to sample in environments generally inaccessible to ships (e.g., in hurricane ortyphoon conditions and under ice), good spatial coverage and sampling over repeated sec-tions, capability of feature-based or adaptive sampling, and potential deployment of severalvehicles from moorings, ships, offshore platforms, and coastal stations (Glenn et al. 2000a).

A special AUV, called a glider (Figure 9-10, bottom), has been developed recently(Eriksen et al. 2001; Griffiths et al. 2001). The glider concept uses variable buoyancy con-trol, lift surfaces (wings), a hydrodynamic shape, and trajectory control using internal mov-

9.5 Developing and Implementing a Global Observing System for Ocean Optics 321

ing mass to control its motion. With typical forward speeds of 0.25 m s-1, gliders may beused as virtual moorings or for long transects. Already, researchers are placing optical in-struments on proto-type gliders (Eriksen et al. 2001).

9.5 DEVELOPING AND IMPLEMENTING A GLOBAL

OBSERVING SYSTEM FOR OCEAN OPTICS

This section explores some of the aspects that must be considered in developing and imple-menting a system for global observations of optical variability, including a brief discussionof opportunities and challenges.

9.5.1 System Objectives

The overall objective of the global observing system for ocean optics is to provide opticaldata that will be useful for a variety of problems, such as water column visibility and oceanecology, biogeochemistry, and heating. In situ optical and bio-optical measurements thatare of highest priority to these problems include surface and subsurface hyperspectral irra-diance and/or PAR; hyperspectral absorption, scattering, and attenuation coefficients; vol-ume scattering function; backscattering coefficient; chlorophyll fluorescence; and photo-synthetic rates (and related parameters). Remote sensing of ocean color provides the bestopportunity for inferring many of these variables regionally and globally by using algo-rithms based on in situ data sets. A major system goal is to provide four-dimensional data(horizontal, vertical, and temporal) for the upper ocean with temporal and spatial resolutioncapable of describing and quantifying the primary and secondary processes relevant to thevarious problems of interest (Figure 9-4). For the system to be useful for ocean forecasts,

Figure 9-10 (Top) Photograph of the Odyssey autonomous underwater vehicle (AUV) used to measure optical variabilityin Massachusetts Bay and Monterey Bay. Courtesy: Derek Manov. (Bottom) AUV glider, known as Seaglider, used to measureoptical and physical properties in Puget Sound and Monterey Bay. Courtesy: Charles Eriksen.


communications and latency issues must be addressed to enable assimilation of these datainto models in a near-real-time or real-time sense.

9.5.2 Assets and Implementation

A multi-platform, nested sensor sampling approach that fully utilizes a large number ofsampling assets is necessary to fully address questions regarding physical, bio-optical, chemi-cal, and geological effects on ocean ecology and other such oceanographic problems (Dickey1991). Presently, costs of remote and in situ platforms and sensors and their deployments,as well as personnel costs for analyses, prevent widespread use of such intense sampling.However, it is possible to build a network of optical sensors and systems that utilizes sharedplatforms or platforms-of-opportunity. The use of shared platforms for bio-optical, physi-cal, and biogeochemical measurements will become much easier as instrumentation be-comes smaller, less power demanding, more robust, easier to manufacture, and much lowerin cost. Breakthroughs in this direction are being made in the development of micromachines,microelectromechanical systems (MEMS), and nanotechnology (Kaku 1997; Bishop et al.2001). Some of the important applications of MEMS are in the area of optical devicesinvolving fibers, optical switches, and arrays of tiltable mirrors. Ideas concerning new de-vices for ocean applications are discussed by Tokar and Dickey (2000). Development ofmodular, multi-disciplinary sensor, data acquisition, and data telemetry systems that can bemounted on a variety of platforms (similar to weather station modules) should be ahigh priority.

An additional design factor for new platforms will be sampling flexibility. In particu-lar, it should be possible to direct mobile sampling assets (ships, AUVs, gliders, etc.) tocritical sampling areas based on other remote and in situ data and model predictions. Atpresent, several groups are developing and using autonomous vehicles and the numbers areexpected to grow as mission length capabilities increase, costs decline, reliability improves,operations become more routine, and more sensors become available for various samplingneeds. Creative uses of the vehicles will involve networking and informational feedbackloops to guide sampling programs involving predictive models and responses to extremenatural and anthropogenically driven events (e.g., Curtin et al. 1993).

A new approach for designing observational systems involves Observational SystemSimulation Experiments (OSSEs) (see Robinson et al. 1998). OSSEs can be utilized todesign optimal configurations of observational assets to maximize information content.Field estimates of decorrelation scales of optical and physical variables are useful for OSSEs(e.g., Abbott and Letelier 1998; Chang et al. 2001, 2002).

Reviews of observational assets and organizational efforts for developing observa-tional systems for the twenty-first century have been written recently (see Koblinsky andSmith 2001; Dickey 2001b, 2002; Dickey and Chang 2001). Many organizational activitiesare important for ocean optics and bio-optics, e.g., the Global Ocean Observing System(GOOS, organized by the Intergovernmental Oceanographic Commission). GOOS is anintergovernmental effort directed toward establishing a permanent global system for obser-vations, analysis, and modeling of ocean variables to support operational ocean services.GOOS is being designed to provide information concerning the present state and for pre-dictions of future ocean (coastal and open) conditions including the ocean’s health, livingresources, and role in climate change.

One of the working groups focusing on comprehensive ocean observations is the TimeSeries Working Group subcommittee (Send et al. 2001), whose objectives include timeseries measurements of meteorological, physical, bio-optical, and biogeochemical proper-ties from a variety of measurement systems in different oceanic regions (Figure 9-11). One


of the programs that is presently being implemented is the Argo Profiling Float program(Wilson 2000; ARGO Science Team 2001) that plans to deploy 3,000 profiling floats in theworld ocean such that 100,000 profiles of temperature, salinity, and velocity measurementscan be made to 2,000 m each year. Profiling cycles would be every 10 days with profilesbeing taken at nominal 3° spacing (Figure 9-12). The use of Argo floats for optical, bio-optical, and biogeochemical measurements is under study at present. In addition, the Glo-bal Drifter Program has operated a network of mixed layer drifter buoys to provide seasurface temperature, currents, and surface meteorological data for several applications, in-cluding ENSO forecasts and hurricane predictions. Up to 1,000 drifters may be in operationin the near future. A number of these drifters are being planned for optical and bio-opticalmeasurements.

Figure 9-12 Global map depicting planned global distribution of Argo profiling floats. After ARGO Science Team 2001.

Figure 9-11 Draft global map of planned or existing time series observatories. After Send et al. 2001.


Finally, the coordination of international efforts for ocean color measurements fromspace is receiving special attention through the International Ocean-Colour CoordinatingGroup (IOCCG). The IOCCG provides information about ongoing and planned ocean colormissions; calibration and validation of remote sensing data; and remote sensor wavelengths,coverage, and resolution (IOCCG 1998; IOCCG 1999).

9.5.3 Data Synthesis, Utilization, and Modeling

In situ and remotely sensed data will need to be systematically integrated with physical andbiogeochemical databases (e.g., SeaBASS, Hooker et al. 1994; Worldwide Ocean OpticalData system, Smart 2000) to optimize their collective utility for solving oceanographicproblems such as those described in the introduction. These databases should be compiledusing comprehensive data from international oceanographic programs and should includemetadata files and have common data formats with a consistent set of units. Importantly,data need to be available to all interested scientists via the World Wide Web.

There has been considerable interest in subdividing the ocean into “biological or bio-optical provinces” (Longhurst 1995, 1998; Harrison et al. 2001) because of the great re-gion-to-region diversity of phytoplankton species, as well as differing levels of CDOM andparticulates. The boundaries of provinces are clearly not stationary because of the dynamicnature of the oceans. Nonetheless, the idea of using biogeography of the ocean has consid-erable appeal as a means for formulating empirical relationships between ocean color prod-ucts and bio-optical variables such as chlorophyll-a and primary production.

The need for near-real-time optical and bio-optical data for data assimilation modelingis being recognized for many applications such as prediction of harmful algal blooms,eutrophication from run-off, and water column visibility (e.g., Hofmann and Lascara 1998;Robinson et al. 1998, 1999; Spitz et al. 1998; Bissett et al. 1999; Dickey et al. 2001b;Dickey 2003). Telemetry of optical data from coastal and open ocean moorings using radiofrequency, cell phone technology, and/or fiber optic links is necessary for real-time datainput into these data assimilation and prediction models (Dickey et al. 1993, 1998a; Glennet al. 2000a, b). It may be possible to more fully exploit commercial fiber optic communi-cations cables in many coastal areas. Additionally, a limited number of open ocean moor-ings may be able to utilize existing underwater communication cables. Unfortunately, openocean data telemetry using communication satellites is currently bandwidth-limited andnew satellite systems with greatly improved bandwidth are needed for all of the in situplatforms described here. Transmission of aircraft- and satellite-borne hyperspectral sensordata requires large bandwidth also, and thus special methodologies (e.g., data compression)(Davis et al. 1999a).

Prediction of optical and bio-optical properties and their distributions requires linkageof data to forecast models. Predictions of physical fields have been done for several yearsusing data assimilation models (e.g., Robinson et al. 1998), but assimilation of bio-opticaland chemical data is still evolving. Data assimilation has several goals: 1) to control errorsand provide model initialization (predictability and model); 2) to estimate parameters; 3) toidentify and elucidate actual ocean processes; 4) to design experimental networks (OSSEs);and 5) to monitor and predict specified variables of interest. It is beyond the scope of thisreview to detail this methodology (see Robinson et al. 1998). However, some of the prin-ciples and key steps in common for physical and interdisciplinary predictive modeling canbe summarized as follows. Estimation of a particular variable through data assimilationrequires both observational data and dynamical governing equations (e.g., conservationequations, rate process equations, etc.) for the physical and bio-optical state variables thatare functions of space and time. The goal is to accurately predict the distributions of selected


state variables given their values at some initial time, t0. A limiting problem concerns thequality (accuracy and resolution) and perhaps more importantly, the quantity (sparse inspatial resolution) of the observational data at t = t0. Thus, the available initial data fieldsneed to be interpolated and gridded before the governing equations can be solved (inte-grated). The data assimilation process is conceptualized in Figure 9-13 (after Robinson et al.1998). Briefly, the observed multi-disciplinary state variables are input to the interdiscipli-nary model that acts to adjust the data dynamically on the basis of the equations to generatenew unobserved state variables, and to improve the quality and accuracy of the estimates.Dynamics serve to link the different state variables and thus further extend the influenceand value of the measurements. Data are dynamically interpolated by the model and pro-vide constraints on model forecasts. Field estimates are then used for model improvementand adaptive sampling. Adaptive sampling in the form of redirecting sampling assets (e.g.,AUVs) would be suggested, for example, if interesting processes were predicted to occur inan area that was formerly poorly sampled (e.g., a front or eddy). Clearly, data assimilationmodels have remarkable potential, but will be of limited value without high volumes ofinterdisciplinary data derived from networks of sampling platforms as suggested in Figures9-5 and 9-14. Additional discussion of this important topic may be found in recent papersby Hofmann and Friedrichs (2001), Robinson and Lermusiaux (2002), and Dickey (2002).

Figure 9-13 Schematic diagram illustrating the input of multi-disciplinary data into an interdisciplinary data assimilationmodel along with feedbacks for model improvement and adaptive sampling. Lower portion of diagram is after Figure 20.1of Robinson et al. 2000.


Finally, the utility of optical and bio-optical (as well as other oceanographic) data andmodel products depends on their presentation to scientists, as well as to the general public.Major advances are being made in the visualization of observational data and model results(e.g., see examples in Dickey 2002). Meteorologists have led the way in creating both stilland motion picture graphics that enable scientists and the public to optimally utilize com-plex information and to better understand weather and atmospheric dynamics and thermo-dynamics. Ocean color images from space are now presented via the World Wide Web, andin the future, interactive four-dimensional motion pictures or loops of optical and bio-opti-cal properties and related biological fields (red tides, sediment movement, etc.)can be added.

9.6 CASE STUDIES

In this section, we highlight two examples of integrated programs for interdisciplinarymeasurements including optical and bio-optical properties. The first example highlightsopen ocean observations made off the island of Bermuda. The second focuses on a programconducted in nearshore waters off the New Jersey, Florida and Bahama Islands coasts.These programs have obtained comprehensive data sets using a host of platforms includingships, moorings, AUVs, aircraft, and satellites. They have also been used for interdiscipli-nary model development and testing.

Figure 9-14 Schematic diagram illustrating the various platforms used for the HyCODE experiments at the LEO-15 studysite off the New Jersey coast in the summers of 2000 and 2001. Courtesy: Scott Glenn.

9.6 Case Studies 327

9.6.1 Bermuda Open Ocean Observational Programs

Deep waters off Bermuda have long been used for scientific research, including Beebe’sfamous bathysphere dives in the 1930s (Beebe 1934). Hydrostation S, started in 1954 byHenry Stommel (e.g., Joyce and Robbins 1996), remains one of the longest continuousopen ocean physical oceanographic records in the world. In 1978, deep-ocean sedimenttrap particle collection was begun about 75 km southeast of Bermuda (depth of ~4500 m)by Werner Deuser and continues to the present (e.g., Conte et al. 2001). The BermudaAtlantic Time-series Study (BATS; see Steinberg et al. 2001), established as part of the USJGOFS program, was begun in 1988 with multi-disciplinary ship-based sampling near thesediment trap mooring site (~80 km southeast of Bermuda). The general objective of BATSis to characterize, quantify, and understand processes that control ocean biogeochemistry,especially carbon, on seasonal to decadal time scales. BATS ship sampling is done monthlyand every two weeks during the springtime. Ship-based optical profiles (sampling in con-cert with BATS) and remotely sensed ocean color data have been obtained at the BATS sitesince 1992 by the Bermuda Bio-Optics Program (BBOP) led by David Siegel (e.g., Siegelet al. 1996, 2001). One of the major accomplishments of the BBOP activity has been thedemonstration of the importance of colored dissolved or detrital material for biogeochem-istry and remote sensing of chlorophyll in the open ocean (e.g., Siegel et al. 1996, 2002).The Bermuda Testbed Mooring (BTM) (Dickey et al. 1998b, 2001a) began in 1994 near theBATS site in order to develop and test new multi-disciplinary sensors and systems, to pro-vide groundtruthing data for satellite ocean color imagers (including SeaWiFS), and tocollect high-frequency data for studies and models of upper ocean biogeochemistry andphysics. BTM measurements were an important addition as processes (e.g., eddies, windevents, transient blooms) with time scales of less than a few weeks cannot be resolved withmonthly or bi-weekly shipboard observations and are aliased. Other platforms used at theBTM/BATS site have included drifters, floats, moored profilers, an autonomous underwa-ter vehicle (Griffiths et al. 1999), and satellites for sea surface temperature, color, windvectors, and altimetry. Atmospheric sampling of dust and aerosols is done on the island ofBermuda as well, and a ship-of-opportunity program is conducted between Bermuda andthe east coast of the US (Rossby 2001). In fact, most of the platforms illustrated in Figure9-5 have been deployed off Bermuda, though not simultaneously. Thus, the multi-platformapproach for obtaining four-dimensional data sets is quite feasible.

Autonomous sampling is clearly one of the major strategies for global optical measure-ments. The BTM program has tested and utilized a broad range of autonomous samplingsystems. The BTM is serviced at roughly 4–6 month intervals, allowing investigators toregularly exchange instruments in order to evaluate sensor and system performance. Time-series data collected from the mooring can be intercompared, intercalibrated, and inter-preted using comprehensive shipboard data sets obtained during monthly or bi-weekly cruisesas part of the BATS and BBOP as well as other related mooring and remote sensing pro-grams. During the BTM project, several new sensors and systems have been tested by USand international groups. These include new measurements of pCO2, dissolved oxygen,nitrate, trace elements (e.g., iron and lead), several spectral inherent and apparent opticalproperties, 14C for primary production, and currents (Dickey et al. 1998b, 2001a). Some ofthe optical systems designed to measure IOPs and AOPs and tested using the BTM areillustrated in Figure 9-1.

Satellite-derived color data are valuable for providing regional estimates of chloro-phyll and for correlating these data with physical data (e.g., sea surface temperature and seasurface height anomalies) (Figure 9-15) in the Bermuda region (e.g., McGillicuddy et al.2001). However, these data sets are confined to the uppermost layers and the number ofviewing days is limited by cloud obscuration. Yet bio-optical measurements made from


moorings can provide critical complementary and virtually continuous information at sev-eral depths. In particular, moored optical systems can be used for groundtruthing and algo-rithm development for ocean color imaging satellites.

BTM optical systems include radiometers for measurements of spectral downwellingirradiance and spectral upwelling radiance at wavelengths coincident with those of theSeaWiFS ocean color satellite and sample at hourly intervals during daytime. Two or moresystems are deployed at different depths and another radiometer system is deployed fromthe surface buoy to collect incident spectral downwelling irradiance. Derived quantitiesinclude time series of several of the AOP quantities introduced earlier. Among these arespectral diffuse attenuation coefficients, and spectral reflectances including remote sensingreflectance and water leaving radiance. The BTM AOP results have been intercomparedwith ship-based (BBOP) measurements (Figure 9-16) (Dickey et al. 2001a). A comparisonof the water leaving radiance data as determined from the BTM radiometers (extrapolationto surface) and SeaWiFS satellite sensors are illustrated in Figure 9-17. Finally, the effectsof solar elevation, time of day, and Raman inelastic scattering on AOPs have been exam-ined using the BTM data sets (Stramska and Frye 1997; Zheng et al. 2002). It is worthnoting that many of the multi-disciplinary sensors and systems developed and tested on theBTM are also suitable for deployment on other platforms such as drifters, floats, autono-mous underwater vehicles, and offshore structures, all of which will likely be used in futureGOOS sampling.

New discoveries and exciting results have been obtained by utilizing bio-optical, bio-geochemical, and physical data obtained from the BTM, satellite sensors, and ships. Forexample, data collected during the passages of cold-core eddies have been used to estimatethe role of such features on new production and carbon flux to the deep ocean (McGillicuddy

Figure 9-15 Concurrent images of sea surface temperature and ocean color (using CZCS color imager for chlorophyll-a) inthe vicinity of Bermuda on 20 April 1984 (Top) and 28 April 1984 (Bottom). Courtesy: Dennis McGillicuddy. Reprinted fromDeep-Sea Research II, 48: 1823–1836. McGillicuddy, D. J., V. K. Kosnyrev, J. P. Ryan and J. A. Yoder. Covariation ofmesoscale ocean color and sea surface temperature patterns in the Sargasso Sea. © 2001, with permission from Elsevier.


et al. 1998; McNeil et al. 1999; Dickey et al. 2001a). One of the features contained thegreatest values of chlorophyll observed during more than a decade of observations at thesite. The high chlorophyll concentrations were at depth and invisible to ocean color imagers.This feature also displayed near-inertial oscillations in beam c and chlorophyll fluorescence.As another example, the dynamics of the upper ocean at the BTM site have been observedduring passages of hurricanes (Dickey et al. 1998c) and other intense storms. Optical prop-erties reflected the deepening of the mixed layer in the wake of Hurricane Felix in 1995.Ongoing research is being devoted to the generation of phytoplankton blooms in the wakesof hurricanes in the region of the BTM. The hurricane observations are unique and areenabling development and testing of models (Zedler et al. 2002). Finally, it should be notedthat the collective data sets provided by the BTM, BATS, and related programs are beingused by numerical modelers to develop and test models designed to simulate physical andbiogeochemical processes (e.g., primary production, carbon flux, etc.).

Figure 9-16 Time series (15-min. averages) of spectral downwelling irradiance at five wavelengths using mooredspectroradiometers deployed from the BTM in the spring/summer of 1997. Source: Dickey et al. 2001a. The three solid curvesrepresent the minimum, maximum, and average values obtained from the radiometers during 20-sec sampling periods. X’sindicate concurrent data collected from ship-based (BBOP) profile observations provided by David Siegel.


9.6.2 Coastal Ocean Observational Programs

The Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) has been conducted atthree locations: New Jersey shelf (at the Long-term Ecological Observatory site [LEO-15]), west Florida shelf (as part of the Ecology and Oceanography of Harmful Algal Blooms[ECOHAB] program), and Lee Stocking Island in the Bahamas (as part of the CoastalBenthic Optical Properties program [CoBOP] [Mazel 2001]). HyCODE was designed toutilize remotely sensed hyperspectral imagery to improve understanding of the diverseprocesses controlling IOPs in the coastal ocean. The program goals also included the devel-opment of operational ocean color algorithms and radiative transfer models in both theoptically-shallow ocean (i.e., where the signal detected by the satellite sensor is affected bythe seafloor) and the optically-deep ocean (i.e., where the signal detected by the satellitesensor is not directly affected by the seafloor). Some other program objectives includedunderstanding of water column visibility; establishing relationships between optical prop-erties and physical, biological, geological, and chemical processes; and developing opticalmeasurement techniques for coastal benthic environments.

9.6.2.1 NEW JERSEY SHELF (LEO-15)The LEO-15 site is one of the most heavily instrumented areas of the world’s coastal oceans(Glenn et al. 2000a, b). Instruments are deployed from diverse platforms, e.g., coastal me-teorological towers, coastally-based and mooring-based high-frequency radars (for surfacecurrents), ships, moorings, tripods, profiling nodes for physics and optics, AUVs, gliders,and aircraft and spaceborne sensors, many of which provide near-real-time telemetereddata (Figure 9-14). The collective HyCODE data are utilized in atmospheric, physical, andoptical oceanographic predictive models to allow adaptive sampling of transient eventssuch as phytoplankton blooms, coastal eddies, jets, fronts, etc. (Glenn et al. 2000a, b).Measurements during HyCODE have included: IOPs (a(8), c(8), b(8), bb(8), VSF)), AOPs

Figure 9-17 Spectral water leaving radiance derived from BTM radiometers and from the SeaWiFS ocean color satellite foreight days of the sampling period from 1 April to 21 July 1999, illustrating the utility of mooring-based groundtruthing ofocean color data. Inserts show surface PAR time series to illustrate periods of cloudy conditions. Daily average wind speedsare also indicated. Good agreement is found at all SeaWiFS wavelengths except 412 nm; satellite-measured radiance at theblue wavelengths is most affected by atmospheric effects. These data can be improved with further atmospheric correction.


(PAR, hyperspectral Lu(8), hyperspectral Ed(8), Kd(8)), particle sizes and settling velocity,chlorophyll fluorescence, dissolved oxygen, currents, temperature, and salinity. Several ofthe optical instruments are newly developed for hyperspectral capabilities: a 90-wavelengtha-c meter, new hyperspectral radiance and irradiance sensors (3.3 nm spectral samplinginterval between 350 and 800 nm), and airborne hyperspectral imaging spectrometers(PHILLS, 400–1000 nm, 1.17 nm resolution) (Davis et al. 1999b).

Processes observed on the New Jersey shelf have included internal solitary waves,upwelling, upwelling fronts, jets, meanders, filaments, eddies, estuarine flows, and storms.Some of these have both surface and subsurface manifestations. For example, inJuly–August 2000, a persistent water mass/turbidity front separated the lower salinity, more-turbid nearshore waters from more-saline, relatively clearer waters offshore (Figures 9-18,9-19, and 9-20). Offshore, total absorption was dominated by phytoplankton and CDOM,each accounting for roughly 50% of all absorbing materials (440 nm). On the other hand,nearshore absorption was mainly influenced by particles (~70% of absorbing material) ascompared to CDOM (~30%). A nearshore jet was observed between 22 July and 25 July2000. It was a fast, southward-moving, low temperature, high salinity, low particulate/bio-mass water mass, which extended about 5–10 km offshore, and was between water depthsof 8–15 m. The effects of this jet could be seen in both nearshore and offshore optical andbio-optical variability (Figure 9-20). Noticeable increases in offshore chlorophyll-a andspectral absorption and attenuation occurred between 22 July and 25 July 2000 due to ad-vection of higher biomass nearshore waters to the offshore region by the coastal jet.

Figure 9-18 Contour plots of transects of (a) temperature, (b) salinity, and (c) chlorophyll fluorescence with depth illus-trating the presence of a water mass/turbidity front located ~12 km offshore; partitioned spectral absorption at (d) themid-shelf mooring (~25 km offshore) and (e) the nearshore node (~5 km offshore); at-w, ap, and ag denote total minuswater, particulate, and gelbstoff absorption, respectively. Source: Chang et al. 2002.


Given this complexity, which is rather common to coastal environments globally, it isclear that both remote sensors and in situ sensors are required to establish a clear four-dimensional picture of the physical, biological, chemical, and geological dynamics. It isunlikely, for example, that remote sensors could detect the presence or establish the effectsof the deep, relatively clear coastal jet, which was determined to be one of the controllingfactors of optical properties at the LEO-15 site during summer 2000. Further, SeaWiFSocean color imagery was not useful during most of the time period of intensive sampling forthe summer 2000 HyCODE field experiment because of cloud contamination (Gould 2001).More details on HyCODE/LEO-15 results are given by Chang et al. (2002).

9.6.2.2 COASTAL BENTHIC OPTICAL PROPERTIES (COBOP)

While the LEO-15/HyCODE experiment was largely focused on pelagic processes, theCoBOP experiments represent the first detailed examination of shallow benthic optical prop-erties. The program was concerned with remote sensing and underwater imaging with em-

Figure 9-19 The inshore/offshore variation in the diffuse attenuation coefficient for upwelling radiance at 555 nm, asdetermined from optical sensors deployed on an underwater autonomous vehicle (REMUS, WHOI) at the LEO-15 HyCODE sitein summer of 2000. The dotted lines represent the individual data points for radiance measurements, and the color scalerepresents the diffuse attenuation coefficient (m-1). Courtesy: Marlon Lewis. For further details, see Brown et al. 2004.

Figure 9-20 Time series contour plots of temperature at the (a) mid-shelf mooring and (b) nearshore node, showing thedeep, lower temperature, lower biomass coastal jet; time series of chlorophyll fluorescence at the (c) mid-shelf mooring and(d) nearshore node showing the effects of the coastal jet. Note the increase in chlorophyll at the mid-shelf mooring from thedisplacement of higher biomass waters from nearshore to offshore. Source: Chang et al. 2002.


phasis on the interaction of light with coral reefs, sea grasses, and associated marine sedi-ments (Mazel 2001; Ackleson 2003). The primary site of CoBOP was Lee Stocking Islandin the Bahamas. Measurements similar to those at LEO-15 (a(8), b(8), bb(8), c(8), R(8),Lu(8), Ed(8), PAR, chlorophyll fluorescence, etc.) were made in optically-shallow waters.A variety of platforms was utilized, such as aircraft, ships, moorings, tripods, tethered buoys,AUVs, and diver-mounted instrument packages.

Detailed measurements of the spectral reflectance of various bottom types (coral, seagrasses, sand, etc.) revealed surprising variability over very small spatial scales (Mazel2001). The general open ocean assumption of plane parallel, horizontally homogenous,ocean optical fields clearly fails in this area. The first measurements of the bi-directionalreflectance distribution function for benthic substrates were made during CoBOP (Voss etal. 2000). These observations revealed complex interactions between solar illuminationgeometry and the resulting reflected upwelling radiance field. Transpectral processes, par-ticularly fluorescence, take on a much more important role in these regions, with the addedpossibility of species discrimination based on fluorescence imaging systems (e.g., Mazel1995; Jaffe et al. 2001). An example of a fluorescence image, which was obtained with oneof these systems (Jaffe et al. 2001) and processed for bottom classification, is shown inFigure 9-21.

Figure 9-21 Processed image obtained from a fluorescence imaging laser line scan instrument deployed during the CoBOPexperiment on a coral reef off Lee Stocking Island in the Bahamas. Bottom features in the image are classified as follows:hard corals and zoanthids – white; macroalgae – brown; soft corals – red; red algae and cyanobacteria – blue; vasesponges – purple; sand – green; shadowed pixels and non-fluorescing targets – black; and ‘other’ – pink. Such imagesenable computation of percent cover by type and size and spatial distributions. Courtesy: Charles Mazel.


Regional algorithms have been developed to retrieve chlorophyll-a, CDOM, and otherbio-optical properties from remotely sensed data for various areas of the coastal oceanincluding those described above (e.g., Gould and Arnone 1998). However, these algorithmshave not yet been rigorously tested for the HyCODE sites because of the limited set ofconcurrent remotely sensed and in-water data. A goal of future coastal experiments is tocollect a much larger set of coincident airborne hyperspectral imaging spectrometer, satel-lite color imager, and in situ data for this purpose. Some hyperspectral (few nm resolution)color satellites with spatial resolutions on order of tens of meters are being planned forcoastal observations (Davis et al. 1999a). The sampling methods utilized during the HyCODEfield experiments provide a template for future interdisciplinary nearshore coastal oceanprograms, which can exploit the rich informational content of new high spectral and spatialresolution optical observations.

9.7 SUMMARY

The last two decades have been marked by the emergence of a host of new optical instru-ments, systems and observing platforms. Looking toward the future, it is anticipated that insitu optical instrumentation will be 1) more capable in spectral resolution and numbers ofmeasured variables; 2) smaller and require less power; 3) suitable for deployment from avariety of autonomous platforms; 4) designed for ease in telemetering of data for real-timeapplications; and 5) less costly as demand increases. In addition, there are growing num-bers of ocean color sensing satellites and aircraft imagers with improved capabilities forsampling the optical spectrum with improved spectral and spatial resolution for regionaland global data collection.

In situ observations will continue to be essential for calibration and validation of re-motely sensed data as well as for algorithm development and for providing complementarysubsurface and high temporal resolution data sets. Important challenges remain to developa framework for synthesizing optical data obtained from state-of-the-art and emerging op-tical sensors using a variety of oceanographic platforms and models and to develop predic-tive modeling capabilities (e.g., via data assimilation models). These challenges can be metthrough continued scientific studies and cooperative international and intergovernmentalprograms, several of which are already planning for optical and bio-optical components.

9.8 ACKNOWLEDGEMENTS

The present work has benefited greatly from many years of interactions with numerousinternational collaborators and colleagues who have shared their knowledge through jointresearch activities, publications, and workshops. Support of our research has been providedby the US Office of Naval Research, National Science Foundation, National Aeronauticsand Space Agency, National Oceanic and Atmospheric Administration, the Natural Sci-ences and Engineering Research Council (Canada), the Centre National de la RechercheScientifique (France), Minerals Management Service, the National Ocean Partnership Pro-gram, Satlantic Incorporated, and the University of California, Santa Barbara.

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