Toward a Global Ocean System for Measurements of Optical … · 2002-05-13 · on optical...

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1 Toward a Global Ocean System for Measurements of Optical Properties Using Remote Sensing and In Situ Observations Manual of Remote Sensing, Volume 7, Marine Environment, Chapter 14, ed. J. Gower, John Wiley and Sons, 2002 T. Dickey, M. Lewis, and G. Chang (Version 24: December 21, 2001) Abstract Observations of the optical properties of the ocean are important for several problems, including primary productivity, biogeochemical cycling, health of the ocean (e.g., harmful algal blooms), upper ocean heating, and underwater visibility. The present review describes some of the enabling technologies that involve a variety of optical sensors and systems, and ocean observing platforms. These technologies can be used to greatly increase the variety and quantity of optical observations and to expand their time-space sampling domains. Remote sensing of ocean color from aircraft- and satellite-borne instruments is vital to obtain regional- and global-scale optical data synoptically . In situ observations are essential for calibration and validation of remotely sensed data as well as for algorithm development. In situ systems also provide complementary subsurface and high temporal resolution data sets. An important challenge is to develop a framework for synthesizing optical data obtained from state-of-the-art and emerging optical sensors and oceanographic platforms and for utilizing these data in predictive models. Introduction The temporal and spatial variability of oceanic optical and bio-optical properties is fundamental to many physical, biological, and chemical processes in the sea (Dickey and Falkowski, 2002). The subdiscipline of ocean optics concerns the study of light and its propagation through the ocean, whereas bio-optics connotes biological effects on optical properties and vice versa. For example, light is essential for the primary formation of biomass by oceanic phytoplankton, which form the base for all higher trophic level organisms in the sea. Light is also crucial to the ecology of the upper ocean and biogeochemical cycling; the production and destruction of many chemical compounds are affected as well through photochemical reactions. Heating and stratification of the upper ocean are driven by the penetration of solar radiation. Absorption and scattering of light by water and its particulate and dissolved constituents modulate local heating and represent significant sources of variability in the heat budget of the upper ocean. In addition, optical properties are important indicators of the health of the ocean in the form of changing water clarity, diversity, and distributions of species (indigenous and non-indigenous), and harmful algal blooms (e.g., red tides) and associated toxins. Underwater visibility also depends on optical properties with practical applications ranging from diver visibility to coastal bathymetric mapping. Introductions to the subdisciplines 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 is given by Ackleson (2001). Several recent papers focus on new technologies applied to ocean optics and bio-optics (Dickey, 2001a,b,c; Dickey and Chang, 2001; Maffione, 2001; Yoder et al., 2001). The present chapter is intended to provide 1) a brief review of relevant concepts and definitions for ocean optics, 2) an introduction to some of the important sensor and platform technologies

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

Manual of Remote Sensing, Volume 7, Marine Environment,

Chapter 14, ed. J. Gower, John Wiley and Sons, 2002

T. Dickey, M. Lewis, and G. Chang (Version 24: December 21, 2001)

Abstract Observations of the optical properties of the ocean are important for several problems, including primary productivity, biogeochemical cycling, health of the ocean (e.g., harmful algal blooms), upper ocean heating, and underwater visibility. The present review describes some of the enabling technologies that involve a variety of optical sensors and systems, and ocean observing platforms. These technologies can be used to greatly increase the variety and quantity of optical observations and to expand their time-space sampling domains. Remote sensing of ocean color from aircraft- and satellite-borne instruments is vital to obtain regional- and global-scale optical data synoptically. In situ observations are essential for calibration and validation of remotely sensed data as well as for algorithm development. In situ systems also provide complementary subsurface and high temporal resolution data sets. An important challenge is to develop a framework for synthesizing optical data obtained from state-of-the-art and emerging optical sensors and oceanographic platforms and for utilizing these data in predictive models. Introduction The temporal and spatial variability of oceanic optical and bio-optical properties is fundamental to many physical, biological, and chemical processes in the sea (Dickey and Falkowski, 2002). The subdiscipline of ocean optics concerns the study of light and its propagation through the ocean, whereas bio-optics connotes biological effects on optical properties and vice versa. For example, light is essential for the primary formation of biomass by oceanic phytoplankton, which form the base for all higher trophic level organisms in the sea. Light is also crucial to the ecology of the upper ocean and biogeochemical cycling; the production and destruction of many chemical compounds are affected as well through photochemical reactions. Heating and stratification of the upper ocean are driven by the penetration of solar radiation. Absorption and scattering of light by water and its particulate and dissolved constituents modulate local heating and represent significant sources of variability in the heat budget of the upper ocean. In addition, optical properties are important indicators of the health of the ocean in the form of changing water clarity, diversity, and distributions of species (indigenous and non-indigenous), and harmful algal blooms (e.g., red tides) and associated toxins. Underwater visibility also depends on optical properties with practical applications ranging from diver visibility to coastal bathymetric mapping. Introductions to the subdisciplines 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 is given by Ackleson (2001). Several recent papers focus on new technologies applied to ocean optics and bio-optics (Dickey, 2001a,b,c; Dickey and Chang, 2001; Maffione, 2001; Yoder et al., 2001). The present chapter is intended to provide 1) a brief review of relevant concepts and definitions for ocean optics, 2) an introduction to some of the important sensor and platform technologies

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that can facilitate development of an integrated optical measurement system for the world ocean, 3) a description of opportunities for initiating and implementing the system, 4) ideas for optimizing the utility of the system, and 5) examples of recent and on-going optical programs. Concepts in Ocean Optics Before describing modern optical instrumentation and methodologies, a few concepts and definitions are introduced (see Kirk, 1994; Mobley, 1994; or Dickey, 2001c for definitions, diagrams, and mathematical formulations). Readers familiar with the fundamental principals of ocean optics may wish to proceed to the next section. Apparent Optical Properties It is convenient to classify bulk optical properties of the ocean as either inherent or apparent. Apparent optical properties (AOPs), radiance and irradiance, depend on both the constituents of the aquatic medium (pure seawater, phytoplankton, detritus, and gelbstoff), the angular distribution of solar radiation (i.e., the geometry of the subsurface ambient light field) and the wavelength of the electromagnetic radiation. Radiance, L(θ,Φ,λ), is defined as the radiant flux at a specified point in a given direction per unit solid angle, per unit area perpendicular to the direction of light propagation, per unit wavelength (units of W (or quanta s-1) m-2 sr-1 nm-1). Zenith angle, θ, is the angle between a vertical line perpendicular to a flat plate and an incident light beam, and azimuthal angle, Φ, is the angle with respect to 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). Downwelling irradiance, Ed (λ), is the irradiance of a downwelling light stream impinging on the top face of a horizontal surface (ideally, a flat light collector oriented perpendicular to the local gravity vector) and upwelling irradiance, Eu(λ), is the irradiance of an upwelling light stream impinging on the bottom face of a horizontal surface. Net downward irradiance is the difference Ed(λ) - Eu(λ), or the integral of L(θ,Φ,λ) cos θ over the entire sphere (full solid angle 4π). Scalar irradiance, E0(λ), is defined as the integral of L(θ,Φ,λ) over the entire sphere. It should be noted that all definitions given above are for a specific wavelength of light. If one integrates scalar irradiance over the visible wavelengths (400 to 700 nm; visible range is sometimes defined to be 350 nm to 700 nm), then the biologically important quantity called photosynthetically available radiation, or PAR, is obtained. Irradiance measurements are fundamentally important for quantifying the amount of light available for photosynthesis and for radiative transfer theory. Other AOPs of great practical interest for remote sensing are the irradiance reflectance, R(λ) = Eu(λ)/Ed(λ), and the so-called spectral radiance reflectance, rrs(λ) = Lu(λ)/Ed(λ) (dimensional, sr-

1; generally at nadir, see Morel and Gentilli, 1996). When taken just above the sea-surface, this is often termed the remote sensing reflectance, Rrs(λ); multiplication by the extraterrestrial solar spectral irradiance yields the normalized water-leaving radiance, Lwn(λ). Inherent Optical Properties (IOPs) Inherent optical properties (IOPs) are dependent only on the medium and are independent of the ambient light field and its geometrical distribution. Photons in an aquatic medium can only be absorbed or scattered. The absorption and scattering properties are distinguished in terms of the IOPs: spectral absorption coefficient, a(λ), spectral scattering coefficient, b(λ), and the spectral volume scattering function, β(ψ,λ), where λ is light wavelength and ψ is the scattering angle.

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The sum of the absorption and scattering coefficients is defined as the spectral beam attenuation coefficient, c(λ) (c(λ) = a(λ) + b(λ); sometimes denoted as beam c). The IOPs a(λ), b(λ), and c(λ) all have units of m-1. β(ψ,λ) represents the scattered intensity of light per unit incident irradiance per unit volume of water at some angle ψ into solid angle element ∆Ω. Units for β(ψ,λ) are m-1 sr-1. The spectral scattering coefficient, b(λ), is obtained by integrating the volume scattering function over all directions (solid angles). The forward scattering coefficient, bf, is obtained by integrating over the forward-looking hemisphere (ψ = 0 to π/2) and the backward scattering coefficient, bb, is calculated by integrating over the backward-looking hemisphere (ψ = π/2 to π). The spectral volume scattering phase function normalizes the volume scattering function by the total scattering and is the ratio of β(ψ,λ) to b(λ) with units of sr-1. The proportion of light which is scattered versus absorbed is characterized by the spectral single-scattering albedo, ω0(λ) = b(λ)/c(λ). Variations in light absorption and scattering can generally be attributed to variability in four constituents of the aquatic medium in natural waters: pure seawater (w), phytoplankton (ph), detritus (d), and gelbstoff (g) (e.g., Roesler et al. 1989; Bricaud et al. 1998), although in many situations, mineralogenic or biogenic particles, or bubbles, can dominate the scattering field in particular (Zhang et al. 1998). Therefore, it is useful to partition the total absorption, total scattering, and total beam attenuation coefficients, at(λ), bt(λ), and ct(λ), respectively, in terms of the contributing constituents such that: at(λ) = aw(λ) + aph(λ) + ad(λ) + ag(λ), bt(λ) = bw(λ) + bph(λ) + bd(λ) and ct(λ) = cw(λ) + cph(λ) + cd(λ) + cg(λ). Detritus is the term representing non-chlorophyll containing particles of organic or inorganic 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 organic material. The contribution of gelbstoff to scattering is small relative to the other terms, and is hence normally neglected. The spectral absorption coefficient of pure seawater is well characterized 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 absorption varies significantly in relation to community composition and environmental changes. Characteristic peaks are typically found near wavelengths of 440 nm and 683 nm and are related to chlorophyll a. Bio-optical research often focuses on the spectral, temporal, and spatial variability of aph(λ). Quasi-inherent Optical Properties The vertical diffuse attenuation coefficients for radiance, KL(λ), and irradiance, Kd(λ), are defined as the logarithmic derivative of the radiometric quantity with respect to depth (Kirk, 1994). These coefficients are referred to as quasi-inherent optical properties since they are properties that are dependent on the ambient light field, yet behave similarly to the IOPs, particularly a(λ). For example, Kd(λ) (here we suppress depth dependence notation for convenience), the vertical spectral diffuse attenuation coefficient of downwelling irradiance, can

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be separated into the four contributions of the various constituents (e.g., water, phytoplankton, detritus, and gelbstoff) in analogy to the absorption coefficients defined above (see Morel, 1988, Gordon et al. 1988; Morel and Maritorena 2001). Kd(λ) has the same units (m-1) as the IOPs a(λ), b(λ), and c(λ). At the same time, Kd(λ) is dependent on the angular distribution of the light field since it is defined as a property of the radiation field Relationships Between IOPs and AOPs Relationships between IOPs and AOPs are central to developing quantitative models of spectral 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 the geometric 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 this research is to predict AOPs given environmental forcing conditions and actual measurements or best estimates of IOPs. Another related, inverse goal is to be able to estimate or determine the vertical and ideally horizontal structure of IOPs and their temporal variability given normalized spectral water-leaving radiance, Lwn(λ), measured remotely from satellite or airplane color sensing imagers. The extrapolation of subsurface values of Lu(λ) to the surface has been the subject of considerable research because a major requirement of ocean color remote sensing is to match in situ determinations to those from satellite- or airplane-based spectral radiometers that necessarily must account as well for the effects of clouds, aerosols, and surface reflectances (e.g., Hooker and McClain, 2000; Fargion and Mueller, 2000). Exact relations between inherent and apparent optical properties can be derived from the Gershun equation (e.g., Mobley, 1994), but can be simplified and validated against numerical solutions to the radiative transfer equation. For example, the reflectance, R(λ), has been found to be a function of bb/(a+ bb) or bb/a from radiative transfer theory (e.g., Gordon et al., 1975) and confirmed from Monte Carlo calculations for waters characterized by b/a values ranging from 1.0 to 5.0 (Kirk, 1994). The simple relationships between IOPs and AOPs break down during periods of low sun angle and/or when highly reflective organisms or their products (coccolithophores and coccoliths) are present (Stramska and Frye, 1997; Zheng et al., 2002). Optical Sensor and Platform Technologies In this section, several sensors and systems used for observing the subsurface light field and optical properties are described. One of the earliest cruises dedicated to optical oceanographic research was conducted by Otto von Kotzebue during a circumnavigational voyage in 1817 (Hojerslev, 1994). von Kotzebue first used a lowered red cloth and later a simple white plate to determine the depth of penetration of light. Analogous studies using a Secchi disk, a white disk 25 cm in diameter whose depth of disappearance is defined as the Secchi depth, began in the late 1860’s. More recently, research utilizing Secchi disk data have been used to describe regional differences and long-term changes in the optical clarity of the world oceans. For example, Lewis et al. (1988) used an empirical formula to translate global Secchi depth data into estimates of chlorophyll concentration. The inferred concentrations were within a few percent of Coastal Zone Color Scanner (CZCS) satellite-based estimates. Falkowski and Wilson (1992) used a 90-year record of Secchi depth observations in the North Pacific Ocean to determine that small systematic increases in phytoplankton have occurred on the edges of the central ocean gyre, while the gyre core has undergone a small depletion of phytoplankton. Many of the earlier

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optical devices for ocean measurements are described by Hojerslev (1994), Kirk (1994), and Jerlov (1976). Jerlov (1976) first compiled optical data for the world ocean. Here we focus on sensors and systems and platforms that are presently in use or are being developed (also see Dickey, 2001a,b,c; Maffione, 2001). In Situ Measurements of AOPs Instrument packages designed for measuring AOPs from moorings are shown in Figures 1 and 2. One of the more commonly used instruments for measuring AOPs is a relatively simple broadband scalar irradiance, E0 or PAR, sensor because of the need for determining the availability of light for phytoplankton. A spherical light collector made of diffusing plastic or opal glass receives the light from approximately 4π steradians and a photodetector records the output voltage. Analogous sensors use flat plate cosine or hemispherical collectors. Spectroradiometers are radiance, irradiance, or scalar irradiance meters that 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 interference filters (usually 10 nm in bandwidth) selected for particular purposes such as investigating absorption peaks and hinge points for pigment analyses. Higher spectral resolution (hyperspectral; ~1-3 nm resolution from 350 to 800 nm) can be achieved by using grating monochromators. Cosine (flat-plate), spherical, or hemispherical collectors are used for several different measurements or calculations of the AOPs described above (Ed, Lu, Kd, etc.). Irradiance cosine collectors are designed such that their responses to parallel radiant flux are proportional to the angle between the normal to the collector surface and the direction of the radiant flux. Radiance sensors are designed to accept light over a small solid angle (typically a viewing angle of a few steradians). Irradiance and radiance instruments are calibrated using well-characterized, standard light sources. Spectral radiometric measurements of AOPs have been far more commonly made than measurements of inherent optical properties (IOPs), and form the basis for compilations on which new algorithms relating optical and biological quantities can be developed (e.g., Lewis and McLean, 1999; Morel and Maritorena 2001). Because of the growing concern about UV radiation, a number of instruments are designed to measure into the UV portion of the electromagnetic spectrum as well. In Situ Measurements of IOPs Systems for obtaining IOP data from moorings are illustrated in Figures 1 and 3. Beam transmissometers measure the beam attenuation coefficient, beam c, to derive suspended sediment volume, phytoplankton biomass, and concentrations of particulate organic carbon (POC) and productivity in the form of POC. The principal of operation of a beam transmissometer involves the measurement of the proportion of an emitted beam that is lost through both absorption and scattering as it passes to a detector through a pre-determined pathlength. Beam transmissometers have often used a red light emitting diode (LED; at 660 nm) for the collimated light source. The wavelength of 660 nm was originally selected in order to minimize attenuation by gelbstoff, which attenuates strongly at shorter wavelengths but minimally in the red. Within the past decade, in situ spectral absorption-attenuation meters have become commercially available (Moore et al., 1992). These concurrently measure spectral absorption and attenuation coefficients at three or nine wavelengths for spectral signatures of both particulate and dissolved

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material. The devices use two tubes through which seawater is pumped. The inside 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 internal reflection to better estimate absorption. Similar instruments have recently been developed to measure a(λ) and c(λ) at 100 wavelengths from 400 to 750 nm with 3.3 nm resolution. The spectral scattering coefficient can be computed from ac-meter data by simply performing the difference b = c - a. Absorption-attenuation meter data and spectral decomposition models can be used to provide in situ estimates of aph(λ), ad(λ), and ag(λ) (Roesler et al., 1989; Bricaud and Stramski, 1990; Chang and Dickey, 1999). Radiative transfer models and interpretation of remote sensing images rely upon absorption, backscattering, and volume scattering function data to estimate the propagation of light (Gordon, 1994; Mobley, 1994). Measurements of absorption have been discussed above. However, until recently, few instruments existed for the measurement of the volume scattering function (VSF). Historical data collected in the 1970's (Petzold, 1972) were commonly used in lieu of direct measurements. Some recently developed VSF instruments measure at fixed numbers of angles with the functional form being determined using theory and polynomial curve fits (Moore et al. 2001; Dana and Maffione 2000). Another new instrument resolves the entire VSF from 0.5 to 179 degrees with an angular resolution of 0.3 degrees (Lee and Lewis, 2001) and has been used to predict the observed radiance distribution far more accurately than with the use of a single VSF (Mobley et al., 2001). The total scattering (b) and the backscattering coefficient, bb, is then estimated through direct integration of the measured VSF over the appropriate angular range. Backscattering can also be estimated using instruments that measure scattering over a single fixed angular range (e.g., 140 +/-10 degrees) at 6 different wavelengths, with the backscattering coefficient estimation based on scattering theory and statistical relationships relating scattering at a given angle to the integral over the backward direction (Maffione and Dana, 1997). Backscattering variations are related to the changes in the size distributions, shapes, and compositions of particles. In situ particle size distributions can be measured using laser (Frauenhofer) diffraction instruments (Agrawal and Pottsmith, 1994) that employ CCD array photodetectors with linear or circular ring geometries. Particles in the range of 5 to 500 microns can be measured with resolution dependent upon the number of individual detector rings. Modified versions of these instruments measure particle settling velocities. Above-water Sensors for Measurement of Oceanic Optical Properties Measurements of oceanic optical properties from above the surface, from shipboard, aircraft, or satellite platforms (see below), involve both passive observations (e.g., measurements which rely on the Sun for illumination of the ocean surface), and active methods, which generally employ laser illumination. The former are largely (imaging) spectrometers that measure the radiance entering the aperture of the sensor, with a variety of scanning mechanisms used to generate two-dimensional fields or images (e.g., Yoder et al., 2001). For these, significant complications arise from the specular reflection from the sea surface (glint), the high degree of variability in atmospheric scattering and absorption, and the bidirectionality of both the downwelling and the upwelling radiance distributions, all of which must be corrected in order to retrieve the desired radiance leaving the ocean surface. As well, the surface downwelling irradiance must be measured or estimated to generate the normalization necessary to compare different oceans at

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different times in a quantitatively meaningful way. Finally, the above-water sensors measure radiances emitted only over the upper optical depth (roughly scaled as 1/KL; also weighting decreasing exponentially with depth), typically ranging from <1 meter in coastal waters to a maximum of order 35 meters in clear, open ocean regions. The remotely sensed observations of radiances can be used to infer concentrations of biological quantities and their dynamics (e.g., Yoder et al., 2001). Remote sensing estimates of pigment concentrations for example (typically chlorophyll a or chlorophyll a + phaeopigments), often use empirical relations involving ratios such as water-leaving radiance at a few chosen wavelengths, e.g., Lwn(443nm)/Lwn(550nm), or remote sensing reflectance at different wavelengths, e.g., Rrs(490nm)/Rrs(555nm), or these ratios with other wavelength combinations (see O’Reilly et al., 1998). Work is progressing in remote sensing of other important oceanic variables including colored dissolved organic materials and signatures of different phytoplankton taxa (Hill et al., 2000), including those associated with red tides or harmful algal blooms (Ciotti et al., 1999). In addition, remotely sensed optical data are being used to estimate primary productivity (e.g., Behrenfeld and Falkowski, 1997). Sensors that employ active illumination methods (e.g., LIght Detection And Ranging, LIDAR) avoid some of the problems associated with reliance on the Sun, since the illumination geometries are less variable, the systems can operate at night, and the laser power is such that depth ranges can be probed that are 3-5 times deeper than passive methods (e.g., Yoder et al., 2001). Further, the LIDAR systems can be range-gated to examine the vertical structure of the water column, including the depth and nature of the sea bottom. To date, such systems have been fielded on aircraft and shipboard platforms; they are particularly useful for large-scale bathymetric surveys and focused high spatial resolution bio-optical studies. Platforms The judicious placement and utilization of a variety of complementary ocean observing platforms (Figures 4 and 5) along with numerical models can greatly improve our informational databases and ultimately our knowledge and capabilities for making predictions of upper ocean ecology, health of the ocean, biogeochemistry, water column visibility, etc. (Dickey, 1991, 2001a, b). Some challenges for making these oceanographic predictions are: 1) there are often tens if not hundreds of relevant variables involved, 2) many of the oceanographic properties are non-conservative, 3) processes are typically nonlinear and coupled through several different multidisciplinary variables, 4) physiological and behavioral aspects are frequently important for ocean biology and indirectly for optics, 5) costs of doing 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 will be needed to fill in the time-space continuum of oceanographic processes because of specific platform advantages and limitations (Figure 4). In the next few paragraphs, some of the platforms and data telemetry systems that will likely be important elements of a global observing system are described. Remote Sensing from the Air and Space Above-water optical measurements, from satellite or aircraft, provide nearly synoptic observations over the ocean; they are restricted however, to the upper optical depth (e.g., Yoder et al., 2001). Initial observations of the spectral reflectance of the ocean, ‘ocean color’, from

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airborne sensors (Yentsch, 1960; Lorenzen, 1969) provided the basis for satellite measurement systems initiated in 1978 by the Coastal Zone Color Scanner (CZCS; Gordon et al., 1985), which provided uneven coverage until its demise in 1986. Satellite-based ocean color was not obtained again until the launch of the Japanese Ocean Color and Temperature Sensor (OCTS) in August 1996, which provided global ocean color data until its early demise in June 1997. Thus far, the most outstanding time series of global ocean color has come from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS; Figure 6) launched in 1997, which continues to function to the present. There are a handful of satellites that collect ocean color data now and it is expected that within the next decade several more ocean color satellites will be placed in orbit (e.g., see IOCCG, 1998; Yoder et al., 2001; Table 1 in Bissett et al., 2001). A critical issue will be the ability to merge the data sets from these different sensors to produce a consistent spatial, time series of oceanic bio-optical properties. Satellite-based sensors are now capable of providing regional and nearly global synoptic views and data over much larger areas of the oceans and ice than possible from any other platform. They are complemented by a large array of specialized aircraft sensors deployed for specific experiments and high resolution surveys. An example of an airborne hyperspectral imaging system is shown in Figure 7. Recently, unmanned aircraft with sensor payloads have provided new flexibility for these purposes. The quantification and utility of space-based as well as aircraft-based ocean color observations are fundamentally dependent on laboratory calibrations of sensors, spacecraft calibrations, atmospheric corrections, sea surface and subsurface data for groundtruthing satellite sensor data, and algorithm development (Hooker and McClain, 2000; Fargion and Mueller, 2000). Remote sensing data are typically empirical inferences of surface signals and are often founded on groundtruth data sets obtained from ocean-based platforms. Electromagnetic radiation only penetrates to very shallow ocean depths (infrared to millimeters and visible to meters (an optical depth); Gordon and McCluney, 1975; Barnard et al., 2000; Frette et al., 2001). In addition, visible wavelengths cannot effectively penetrate water vapor, clouds, and dust. The nature of orbital mechanics as well imposes restrictions on both revisit frequencies and the nature of the illumination and viewing geometries, while aircraft sensors are restricted in 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 described below, to provide continuous time series and to characterize important subsurface ocean properties. Ships Ships are valuable assets for: 1) direct, detailed process-oriented observations for specific research activities, 2) for data collection at depth and over long distances during transects, and 3) deployment of other sampling platforms such as moorings, drifters, floats, and autonomous underwater vehicles (AUVs). Useful modes of ship sampling include: 1) on-station vertical profiling of instruments, 2) underway sampling of surface waters using flow-through systems (e.g., Balch et al., 2001), and 3) underway sampling using towed undulating or fixed depth bodies or chains that act as platforms for sensor suites. These various modes of sampling are especially useful for regional process-oriented studies and for long transect sampling programs designed to provide important spatial maps. The Atlantic Meridional Transect (AMT) program (described by Aiken et al., 2000 and Hooker and McClain, 2000) and JGOFS Global Survey

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program (Marra et al., 1991) are examples of research-driven long transect sampling programs that have been devoted to or included optical measurements. One of the advantages of ship sampling is that calibrations and cleaning of the optical windows or tubes of instruments can be performed between profile casts to provide more accurate, freshly calibrated, non-biofouled optical and bio-optical data. A second advantage of ships is that advanced analytical instrumentation that cannot presently be routinely deployed from other in situ platforms can be utilized. Examples of such instrumentation include: flow cytometers, mass spectrometers, ‘clean’ methods for ocean chemistry, and radioactivity measurement systems. In addition, ships remain the only viable platform capable of collecting biological samples with net tows and large volumes of water for optical and pigment analyses using spectrophotometers and fluorometers. Ships have limitations in terms of their high cost, limited availability, and restricted synopticity in sampling. As well, they are often precluded from working in heavy weather and sea-state conditions, which are of considerable interest for the study of air-sea interactions and biological processes. Nonetheless, physical oceanographers and meteorologists have developed effective Volunteer Observing Ship (VOS) and Ship-of-Opportunity programs to make basic measurements. More recently, carbon dioxide measurements have been supported through such voluntary programs and optical measurements could be added with relatively little difficulty. Fixed (Eulerian) Platforms: Moorings, Bottom Tripods, and Offshore Platforms Researchers and environmental agencies are using mooring, bottom tripod, and offshore platform measurement systems and sensors to study environmental changes in the ocean on time scales from minutes to years (Figure 4). An increasing number of optical, bio-optical, chemical, geological, and acoustical parameters are being measured from these platforms (example mooring shown in Figure 8). This work has led to discoveries of new interdisciplinary 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 variability (e.g., reviews by Dickey, 2001b; Dickey and Chang, 2001; Dickey and Falkowski, 2002; Lewis, 2001). Several different oceanic regions have been studied using interdisciplinary 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. Locations where moorings have been used specifically for calibrating and 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), in the equatorial Pacific (Chavez et al. 1999), off the south coast of Plymouth, England (Pinkerton and Aiken, 1999), and off Bermuda (Figure 8; Dickey et al., 1998a, 2001a as described below). Because of biofouling of sensors, useful data from moorings have often been limited to a few months in the open ocean and less in coastal waters (Davis et al., 2000). However, work is underway to mitigate this problem (Chavez et al., 2000; Dickey et al., 2001b).

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Benthic processes may be studied and monitored using optical instrumentation deployed on bottom tripods. Bottom tripods and their instrumentation may be placed in virtually the same environments as moorings using similar suites of sensors and samplers deployable from moorings. Beam transmissometers, particle sizing devices, optical settling tubes, and photographic cameras mounted on tripods have been used to investigate sediment resuspension and settling, bedform formation and movement, bioturbation, and flocculation/deflocculation (Trowbridge and Nowell, 1994; Lampitt and Antia 1997; Chang et al., 2001; Hill et al., 2001). Offshore platforms, including research-dedicated and oil production platforms, provide opportunities for conducting oceanic and meteorological research and monitoring. These often large and very stable platforms typically have space and facilities for manned research laboratories and are equipped with adequate power, making them ideal for oceanographic observations. They offer several advantages over shipboard platforms, including absolute stability in high sea states, suitability for time series measurements, and capability for housing personnel. It should be possible to launch autonomous underwater vehicles (AUVs) and other mobile sampling devices from these platforms for spatial sampling as well. Zibordi et al. (1999) have reported the use of a tower offshore of Venice, Italy for optical remote sensing calibration and validation. Coastal sites with piers may also be used. Unfortunately, instrument shading is problematic for many of these large platforms. Lagrangian Platforms: Drifters and Floats Drifters and 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 and floats (Figures 9 and 10) can collect optical data in regions of the world oceans that are rarely 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 4). Drifters and floats now utilize the Global Positioning System (GPS), giving very accurate position data (less than 10's of meters down to a few meters). Drifter data allow detailed examinations of bio-optical processes on short time and space scales and the evaluation of de-correlation scales of chlorophyll and physical variables (e.g., Abbott and Letelier, 1998). 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 move vertically, have provided data collected during their rise and descent through the water column after surfacing using satellite data telemetry. Some investigators are already planning and testing profiling floats adapted for bio-optical measurements (Zaneveld, 2001). For example, Mitchell et al. (2000) have instrumented a Sounding Oceanographic Lagrangian Observer (SOLO; Davis et al., 2001) profiling float (K-SOLO, Figure 10) to measure diffuse attenuation coefficient at three wavelengths and have collected data from the Sea of Japan/East Sea. Also, optical instruments for determining particulate organic carbon (POC) and particulate inorganic carbon (PIC) are being developed and tested using SOLO floats (called C-Argo floats) by Bishop et al. (2001a). 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 concurrent bio-optical, meteorological, physical, and chemical time series measurements in the North Pacific sub-tropical gyre (34o N, 142o W) in 1982 during the Optical Dynamics Experiment (ODEX; e.g.,

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Dickey et al., 1986). Profiled optical instruments included an early version of a spectroradiometer, beam transmissometer (660 nm), fluorometer, and PAR sensor. Thus, time series of Kd(λ,z), chlorophyll(z), beam c(z), and physical variables including temperature(z), salinity(z), and currents(z) were obtained. R/P FLIP is ~108 m in length and is roughly 85% submerged during operations, resulting in minimal vertical or pitching motion because of its low center of gravity. For ODEX, R/P FLIP was not moored and allowed to drift. R/P FLIP was not designed to be a Lagrangian drifter and should optimally be moored for Eulerian measurements. Thus, ODEX measurements were somewhat more difficult to interpret, especially for time scales greater than a few days. However, relatively high temporal (down to 15-min) and vertical spatial (~1 m) data were obtained and gave early evidence of important bio-optical variability at scales of less than an hour to a day as well as results showing vertical step-like structure in optical properties that often were well correlated with physical variables. Future specialized experiments could benefit from use of platforms such as R/P FLIP. Autonomous Underwater Vehicles (AUVs) Autonomous underwater vehicles (AUVs) originated in the 1970's. A description of the history 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 number of recent programs have begun to exploit these vehicles for scientific studies including optical measurements (Griffiths et al., 1999; Yu et al., 2002; Figure 11, top). This has become possible because of the development of new optical sensors and systems that are relatively small in size, consume moderate power, and can be interfaced to the AUVs. 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 or typhoon conditions and under ice), good spatial coverage and sampling over repeated sections, capability of feature-based or adaptive sampling, and potential deployment of several vehicles from moorings, ships, offshore platforms, and coastal stations (Glenn et al., 2000a). A special AUV, called a glider (Figure 11, bottom), has been developed recently (Eriksen et al., 2001; Griffiths et al., 2001). The glider concept uses variable buoyancy control, lift surfaces (wings), a hydrodynamic shape, and trajectory control using internal moving mass to control its motion. With typical forward speeds of 0.25 m s-1, gliders may be used as virtual moorings or for long transects. Already, researchers are placing optical instruments on proto-type gliders (Eriksen et al., 2001). Developing and Implementing a Global Observing System for Ocean Optics This section explores some of the aspects that must be considered in developing and implementing a system for global observations of optical variability, including a brief discussion of opportunities and challenges. System Objectives The overall objective of the global observing system for ocean optics is to provide optical data that will be useful for a variety of problems such as water column visibility and ocean ecology, biogeochemistry, and heating. In situ optical and bio-optical measurements that are of highest priority to these problems include: surface and subsurface spectral irradiance and/or PAR, spectral absorption, scattering, and attenuation coefficients, volume scattering function, backscattering coefficient, chlorophyll fluorescence, and photosynthetic rates (and related

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parameters). Remote sensing of ocean color provides the best opportunity for inferring many of these variables regionally and globally by using algorithms 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 resolution capable of describing and quantifying the primary and secondary processes relevant to the various problems of interest (Figure 4). For the system to be useful for ocean forecasts, communications and latency issues must be addressed to enable assimilation of these data into models in a near real-time or real-time sense. Assets and Implementation A multi-platform, nested sensor sampling approach that fully utilizes a large number of sampling assets is necessary to fully address questions regarding physical, bio-optical, chemical, and geological effects on ocean ecology and other such oceanographic problems (Dickey, 1991). 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 shared platforms or platforms-of-opportunity. The use of shared platforms for bio-optical, physical, and biogeochemical measurements will become much easier as instrumentation becomes smaller, less power demanding, more robust, easier to manufacture, and much lower in cost. Breakthroughs in this direction are being made in the development of micromachines, microelectromechanical systems (MEMS), and nanotechnology (Kaku, 1997; Bishop et al., 2001b). Some of the important applications of MEMS are in the area of optical devices involving fibers, optical switches, and arrays of tiltable mirrors. Ideas concerning new devices for ocean applications are discussed by Tokar and Dickey (2000). Development of modular, multi-disciplinary sensor, data acquisition, and data telemetry systems that can be mounted on a variety of platforms (similar to weather station modules) should be a high priority. An additional design factor for new platforms will be sampling flexibility. In particular, it should be possible to direct mobile sampling assets (ships, AUVs, gliders, etc.) to critical sampling areas based on other remote and in situ data and model predictions. At present, several groups are developing and using autonomous vehicles and the numbers are expected to grow as mission length capabilities increase, costs decline, reliability improves, operation becomes more routine, and more sensors become available for various sampling needs. Creative uses of the vehicles will involve networking and informational feedback loops to guide sampling programs involving predictive models, and responses to extreme natural and anthropogenically driven events (e.g., Curtin et al., 1993). A new approach for designing observational systems involves Observational System Simulation Experiments (OSSEs; see Robinson et al., 1998). OSSEs can be utilized to design 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 observational systems for the 21st century have been written recently (see Koblinsky and Smith, 2001; Dickey, 2001b,c; Dickey and Chang, 2001). Many organizational activities are important for ocean optics and bio-optics, e.g., the Global Ocean Observing System (GOOS, organized by the Intergovernmental

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Oceanographic Commission, IOC). GOOS is an intergovernmental effort directed toward establishing a permanent global system for observations, analysis, and modeling of ocean variables to support operational ocean services. GOOS is being designed to provide information concerning the present state and for predictions of future ocean (coastal and open) conditions including the ocean’s health, living resources, and role in climate change. One of the working groups focusing on comprehensive ocean observations is the Time Series Working Group subcommittee (Send et al., 2001), whose objectives include time series measurements of meteorological, physical, bio-optical, and biogeochemical properties from a variety of measurement systems in different oceanic regions (Figure 12). 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 the world ocean such that 100,000 profiles of temperature, salinity, and velocity measurements can be made to 2000 m each year. Profiling cycles would be every 10 days with profiles being taken at nominal 3-degree spacing (Figure 13). The use of Argo floats for optical, bio-optical, and biogeochemical measurements is under study at present. In addition, the Global Drifter Program has operated a network of mixed layer drifter buoys to provide sea surface temperature, currents, and surface meteorological data for several applications including ENSO forecasts and hurricane predictions. Up to 1000 drifters may be in operation in the near future. A number of these drifters are being planned for optical and bio-optical measurements. Finally, the coordination of international efforts for ocean color measurements from space is receiving special attention through the International Ocean-Colour Coordinating Group (IOCCG). The IOCCG provides information about on-going and planned ocean color missions; calibration and validation of remote sensing data; and remote sensor wavelengths, coverage, and resolution (IOCCG Report Number 1, 1998; IOCCG Report Number 2, 1999). Data Synthesis, Utilization, and Modeling In situ and remotely sensed data will need to be systematically integrated with physical and biogeochemical databases (e.g., SeaBASS, Hooker et al. 1994; Worldwide Ocean Optical Data system, Smart, 2000) to optimize their collective utility for solving oceanographic problems such as those described in the introduction. These databases should be compiled using comprehensive data from international oceanographic programs and should have common 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 region-to-region diversity of phytoplankton species as well as differing levels of colored dissolved organic material (CDOM) and particulates. The boundaries of provinces are clearly not stationary because of the dynamic nature of the oceans. Nonetheless, the idea of using biogeography of the ocean has considerable appeal as a means for formulating empirical relationships between ocean color products 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 modeling is being recognized for many applications such as prediction of harmful algal blooms, eutrophication

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from runoff, and water column visibility (e.g., Hofmann and Luscara, 1998; Robinson et al., 1998, 1999; Spitz et al., 1998; Bissett et al., 1999; Dickey et al. 2001b). Telemetry of optical data from coastal and open ocean moorings using radio frequency and fiber optic links is necessary for real-time data input into these data assimilation and prediction models (Dickey et al., 1993, 1998a; Glenn et al., 2000a,b). It may be possible to more fully exploit commercial coastal fiber optic communications cables in many coastal areas. Additionally, a limited number of open ocean moorings may be able to utilize existing underwater communication cables. Unfortunately, open ocean data telemetry using communication satellites is currently bandwidth limited and new satellite systems with greatly improved bandwidth are needed for all of the in situ platforms described here. Transmission of aircraft- and satellite-borne hyperspectral sensor data 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 linkage of data to forecast models. Predictions of physical fields have been done for several years using data assimilation models (e.g., Robinson et al., 1998), but assimilation of bio-optical and chemical data is still evolving. Data assimilation has several goals: 1) to control errors and provide model initialization (predictability and model), 2) to estimate parameters, 3) to identify 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 this review to detail this methodology (see Robinson et al., 1998). However, some of the principals and key steps in common for physical and interdisciplinary predictive modeling can be summarized as follows. Estimation of a particular variable through data assimilation requires both observational data and dynamical governing equations (e.g., conservation equations, rate process equations, etc.) for the physical and bio-optical state variables that are 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 the quality (accuracy and resolution) and perhaps more importantly, the quantity (sparse in spatial resolution) of the observational data at t = t0. Thus, the available initial data fields need to be interpolated and gridded before the governing equations can be solved (integrated). The data assimilation process is conceptualized in Figure 14 (after Robinson et al., 1998). Briefly, the observed multi-disciplinary state variables are input to the interdisciplinary model that acts to adjust the data dynamically on the basis of the equations to generate new 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 influence and value of the measurements. Data are dynamically interpolated by the model and provide constraints on model forecasts. Field estimates are then used to improve the model and for 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 in an area that was formerly poorly sampled (e.g., a front or eddy). Clearly, data assimilation models have remarkable potential, but will be of limited value without high volumes of interdisciplinary data derived from networks of sampling platforms as suggested in Figures 5 and 18. Additional discussion of this important topic may be found in recent papers by Hofmann and Friedrichs (2001), Robinson and Lermausiaux (2001), and Dickey (2002). Finally, the utility of optical and bio-optical (as well as other oceanographic) data and model products will depend on their presentation to scientists as well as the general public. Major

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advances are being made in the visualization of observational data and model results (e.g., see examples in Dickey, 2001c). Meteorologists have led the way in creating both still and motion picture graphics that enable scientists and the public to optimally utilize complex information and to better understand weather and atmospheric dynamics and thermodynamics. Ocean color images from space are now presented via the World Wide Web, and in the future, fully 4-dimentional motion pictures or loops of optical and bio-optical properties and related biological fields (red tides, sediment movement, etc.) can be added. Case Studies In this section, we highlight two examples of integrated programs for interdisciplinary measurements including optical and bio-optical properties. The first example highlights open ocean observations made off the island of Bermuda. The second focuses on a program conducted in nearshore waters off the New Jersey, Florida and Bahama Islands coasts. These programs have obtained comprehensive data sets using a host of platforms including ships, moorings, AUVs, aircraft, and satellites. They have also been used for interdisciplinary model development and testing. Bermuda Open Ocean Observational Programs Deep waters off Bermuda have long been used for scientific research, including Beebe’s famous bathysphere dives in the 1930’s (Beebe, 1934). Hydrostation S, started in 1954 by Henry Stommel (e.g., Joyce and Robbins, 1996), remains one of the longest continuous open ocean physical oceanographic records in the world. In 1978, deep-ocean sediment trap 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 Bermuda Atlantic Time-series Study (BATS; see Steinberg, 2001), established as part of the U.S. JGOFS program, was begun in 1988 with multi-disciplinary ship-based sampling near the sediment trap mooring site (~80 km southeast of Bermuda). The general objective of BATS is to characterize, quantify, and understand processes that control ocean biogeochemistry, especially carbon, on seasonal to decadal time scales. BATS ship sampling is done monthly and every two weeks during the springtime. Ship-based optical profiles (sampling in concert with BATS) and remotely-sensed ocean color data have been obtained at the BATS site since 1992 by the Bermuda Bio-Optics Program (BBOP) led by David Siegel (e.g., Siegel et al., 1996, 2001). One of the major accomplishments of the BBOP activity has been the demonstration of the importance of colored dissolved or detrital material for biogeochemistry and remote sensing of chlorophyll in the open ocean (e.g., Siegel et al., 1996). The Bermuda Testbed Mooring (BTM; Dickey et al., 1998b, 2001a) began in 1994 near the BATS site in order to develop and test new multi-disciplinary sensors and systems, to provide groundtruthing data for satellite ocean color imagers (including SeaWiFS), and to collect high frequency data for studies and models of upper ocean biogeochemistry and physics. BTM measurements were an important addition as processes (e.g., eddies, wind-events, transient blooms) with time scales of less than a few weeks cannot be resolved with monthly or bi-weekly shipboard observations and are aliased. Other platforms used at the BTM/BATS site have included drifters, floats, moored profilers, an autonomous underwater vehicle (Griffiths et al., 2000), and satellites for sea surface temperature, color, wind vectors, and altimetry. Atmospheric sampling of dust and aerosols is done on the island of Bermuda as well and a ship-of-opportunity program is conducted between Bermuda and the east coast of the U.S. (Rossby, 2001). In fact, most of the platforms illustrated in Figure 5 have been

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deployed off Bermuda, though not simultaneously. Thus, the multi-platform approach for obtaining 4-dimensional data sets is quite feasible. Autonomous sampling is clearly one of the major strategies for global optical measurements. The BTM program has tested and utilized a broad range of autonomous sampling systems. The BTM is serviced at roughly 4-6 month intervals, allowing investigators to regularly exchange instruments in order to evaluate sensor and system performance. Time-series data collected from the mooring can be intercompared, intercalibrated, and interpreted using comprehensive shipboard data sets obtained during monthly or bi-weekly cruises as part of the BATS and BBOP as well as other related mooring and remote sensing programs. During the BTM project, several new sensors and systems have been tested by U.S. and international groups. These include new measurements of pCO2, dissolved oxygen, nitrate, trace elements (e.g., iron and lead), several spectral inherent and apparent optical properties, 14C for primary production, and currents (Dickey et al., 1998b, 2001a). Some of the optical systems designed to measure IOPs and AOPs and tested using the BTM are illustrated in Figure 1. Satellite-derived color data are valuable for providing regional estimates of chlorophyll and for correlating these data with physical data (e.g., sea surface temperature and sea surface height anomalies; Figure 15) in the Bermuda region (e.g., McGillicuddy et al., 2001). However, these data sets are confined to the uppermost layers and the number of viewing days is limited by cloud obscuration. Yet, bio-optical measurements made from moorings can provide critical complementary and virtually continuous information at several depths. In particular, moored optical systems can be used for groundtruthing and algorithm development for ocean color imaging satellites. BTM optical systems include radiometers for measurements of spectral downwelling irradiance and spectral upwelling radiance at wavelengths coincident with those of the SeaWiFS ocean color satellite and sample at hourly intervals during daytime. Two or more systems are deployed at different depths and another radiometer system is deployed from the surface buoy to collect incident spectral downwelling irradiance. Derived quantities include time series of several of the AOP quantities introduced earlier. Among these are spectral diffuse attenuation coefficients, and spectral reflectances including remote sensing reflectance and water-leaving radiance. The BTM AOP results have been intercompared with ship-based (BBOP) measurements (Figure 16; Dickey et al., 2001a). A comparison of the water-leaving radiance data as determined from the BTM radiometers (extrapolation to surface) and SeaWiFS satellite sensors are illustrated in Figure 17. Finally, the effects of solar elevation, time of day, and Raman inelastic scattering on AOPs have been examined using the BTM data sets (Stramska and Frye, 1997; Zheng et al., 2002). It is worth noting that many of the multi-disciplinary sensors and systems developed and tested on the BTM are also suitable for deployment on other platforms such as drifters, floats, autonomous underwater vehicles, and offshore structures, all of which will likely be used in future GOOS sampling. New discoveries and exciting results have been obtained by utilizing bio-optical, biogeochemical, and physical data obtained from the BTM, satellite sensors, and ships. For example, data collected during the passages of cold-core eddies have been used to estimate the role of such features on new production and carbon flux to the deep ocean (McGillicuddy et al., 1998; McNeil et al., 1999; Dickey et al., 2001a). One of the features contained the greatest values of chlorophyll observed during more than a decade of observations at the site. The high

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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 observed during passages of hurricanes (Dickey et al., 1998c) and other intense storms. Optical properties reflected the deepening of the mixed layer in the wake of Hurricane Felix in 1995. On-going research is being devoted to the generation of phytoplankton blooms in the wakes of hurricanes in the region of the BTM. The hurricane observations are unique and are enabling development and testing of models (Zedler et al., 2002). Finally, it should be noted that the collective data sets provided by the BTM, BATS, and related programs are being used by numerical modelers who use the measurements to develop and test models designed to simulate physical and biogeochemical processes (e.g., primary production, carbon flux, etc.). Coastal Ocean Observational Programs The Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) has been conducted at three 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 Coastal Benthic Optical Properties program (CoBOP; Mazel, 2001)). HyCODE was designed to utilize remotely-sensed hyperspectral imagery to improve understanding of the diverse processes controlling IOPs in the coastal ocean. The program goals also included the development of operational ocean color algorithms and radiative transfer models in both the optically-shallow ocean (i.e., where the signal detected by the satellite sensor is affected by the seafloor) and the optically-deep ocean (i.e., where the signal detected by the satellite sensor is not directly affected by the seafloor). Some other program objectives include understanding of water column visibility; establishing relationships between optical properties and physical, biological, geological, and chemical processes; and developing optical measurement techniques for coastal benthic environments. 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 meteorological towers, coastally-based and mooring-based high frequency radars (for surface currents), ships, moorings, tripods, profiling nodes for physics and optics, AUVs, gliders, and aircraft and spaceborne sensors, many of which provide near real-time telemetered data (Figure 18). The collective HyCODE data are utilized in atmospheric, physical, and optical oceanographic predictive models to allow adaptive sampling of transient events such as phytoplankton blooms, coastal eddies, jets, fronts, etc. (Glenn et al., 2000a,b). Measurements during HyCODE have included: IOPs (a(λ), c(λ), b(λ), bb(λ), VSF)), AOPs (PAR, hyperspectral Lu(λ), hyperspectral Ed(λ), Kd(λ)), particle sizes and settling velocity, chlorophyll fluorescence, dissolved oxygen, currents, temperature, and salinity. Several of the optical instruments are newly developed for hyperspectral capabilities: a 100-wavelength a-c meter, new hyperspectral radiance and irradiance sensors (3.3 nm spectral sampling interval between 350 and 800 nm), and airborne hyperspectral imaging spectrometers (PHILLS, 400-1000 nm, 1.17 nm resolution, Davis et al., 1999b).

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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 sub-surface manifestations. For example, in July-August 2000, a persistent water mass/turbidity front separated the lower salinity, more turbid nearshore waters from more saline, relatively clearer waters offshore (Figure 19, 20, 21). 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) as compared to CDOM (~30%). A nearshore jet was observed between July 22 and July 25, 2000. It was a fast, southward moving, low temperature, high salinity, low particulate/biomass water mass, which extended about 5-10 km offshore, and was between water depths of 8-15 m. The effects of this jet could be seen in both nearshore and offshore optical and bio-optical variability (Figure 21). Noticeable increases in offshore chlorophyll a and spectral absorption and attenuation occurred between July 22 and 25, 2000 due to advection of higher biomass nearshore waters to the offshore region by the coastal jet. Given this complexity, which is rather common to coastal environments globally, it is clear 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 is unlikely, for example, that remote sensors could detect the presence or establish the effects of the deep, relatively clear coastal jet, which was determined to be one of the controlling factors of optical properties at the LEO-15 site during summer 2000. Further, SeaWiFS ocean color imagery were not useful during most of the time period of intensive sampling for the summer 2000 HyCODE field experiment because of cloud contamination (R. Gould, pers. comm.). More details on HyCODE/LEO-15 results are given by Chang et al. (2002). Coastal Benthic Optical Properties (CoBOP) While the LEO-15/HyCODE experiment was largely focused on pelagic processes, the CoBOP experiments represent the first detailed examination of shallow benthic optical properties. The program was concerned with remote sensing and underwater imaging with emphasis on the interaction of light with coral reefs, sea grasses, and associated marine sediments (Mazel, 2001). The primary site of CoBOP was Lee Stocking Island in the Bahamas. Measurements similar to those at LEO-15 (a(λ), b(λ), bb(λ), c(λ), R(λ), Lu(λ), Ed(λ), PAR, chlorophyll fluorescence, etc.) were made in optically-shallow waters. A variety of platforms was utilized: aircraft, ships, moorings, tripods, tethered buoys, AUVs, and diver-mounted instrument packages. Detailed measurements of the spectral reflectance of various bottom types (coral, sea grasses, sand etc.) revealed surprising variability over very small spatial scales (Mazel, 2001). The general open ocean assumption of plane parallel, horizontally homogenous, ocean optical fields clearly fails in this area. The first measurements of the bi-directional reflectance distribution function (BRDF) for benthic substrates were made during CoBOP (Voss et al., 2000). These observations revealed complex interactions between solar illumination geometry and the resulting reflected upwelling radiance field. Transpectral processes, particularly fluorescence, take on a much more important role in these regions, with the added possibility of species discrimination based on fluorescence imaging systems (e.g., Mazel, 1995; Jaffe et al. 2001). An example of a fluorescence image, which was obtained with one of these systems (Jaffe et al.,

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2001) and processed for bottom classification, is shown in Figure 22 (courtesy of Charles Mazel). Regional algorithms have been developed to retrieve chlorophyll a, CDOM, and other bio-optical properties from remotely sensed data for various areas of the coastal ocean including those described above (e.g., Gould and Arnone, 1998). However, these algorithms have not yet been rigorously tested for the HyCODE sites because of the limited set of concurrent remotely sensed and in-water data. A goal of future coastal experiments is to collect a much larger set of coincident airborne hyperspectral imaging spectrometer, satellite color imager, and in situ data for this purpose. Some hyperspectral (few nm resolution) color satellites with spatial resolutions on order of 10’s of meters are being planned for coastal observations (Davis et al., 1999a). The sampling methods utilized during the HyCODE field experiments provide a template for future interdisciplinary nearshore coastal ocean programs, which can exploit the rich informational content of new high spectral and spatial resolution optical observations. Summary The last two decades have been marked by the emergence of a host of new optical instruments and systems and observing platforms. Looking toward the future, it is anticipated that in situ optical instrumentation will be 1) more capable in spectral resolution and numbers of measured variables, 2) smaller and require less power, 3) suitable for deployment from a variety of autonomous platforms, 4) designed for ease in telemetering of data for real-time applications, and 5) less costly as demand increases. In addition, there are growing numbers of ocean color sensing satellites and aircraft imagers with improved capabilities for sampling the optical spectrum with improved spectral and spatial resolution for regional and global data collection. In situ observations will continue to be essential for calibration and validation of remotely sensed data as well as for algorithm development and for providing complementary subsurface and high temporal resolution data sets. Important challenges remain to develop a framework for synthesizing optical data obtained from state-of-the-art and emerging optical sensors using a variety of oceanographic platforms and models and to develop predictive modeling capabilities (e.g., via data assimilation models). These challenges can be met through continued scientific studies and cooperative international and intergovernmental programs, several of which are already planning for optical and bio-optical components. Acknowledgements The present work has benefited greatly from many years of interactions with numerous international collaborators and colleagues who have shared their knowledge through joint research activities, publications, and workshops. Support of our research has been provided by the U.S. Office of Naval Research, National Science Foundation, National Aeronautics and Space Agency, National Oceanic and Atmospheric Administration, the Natural Sciences and Engineering Research Council (Canada), the Centre National de la Researche Scientifique, Minerals Management Service, the National Ocean Partnership Program, Satlantic Incorporated, and the University of California, Santa Barbara.

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Figure Captions Figure 1. Schematic diagrams illustrating several different optical and bio-optical systems,

which have been deployed from moorings. Figure 2. Photograph of a moored optical system used to measure chlorophyll fluorescence, the

volume scattering function, upwelled radiance (3 wavelengths), and downwelled irradiance (3 wavelengths). Insert photograph shows a special copper shutter device for minimizing biofouling of the sensors. Photographs provided by Derek Manov.

Figure 3. Photograph of a moored optical system used to measure PAR (scalar and vector),

upwelled radiance (7 wavelengths), downwelled irradiance (7 wavelengths), pressure, temperature, tilt, and dissolved oxygen. Photograph provided by Derek Manov.

Figure 4. Time-horizontal space diagram illustrating physical and biological processes overlain

with sampling domains of various platforms (moorings, satellites, AUVs, etc.). Figure 5. Conceptual diagram showing a variety of sampling platforms. Most of these have

been deployed in the vicinity of the island of Bermuda. Figure 6. Photograph of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for ocean color

imaging at seven wavelengths. Photograph provided by NASA Goddard Space Flight Center.

Figure 7. Photograph of the Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy

(PHILLS), a hyperspectral imaging spectrometer operating over the wavelength range 400 to 1000 nm with 1.7 nm spectral resolution (HyCODE Photo Gallery).

Figure 8. Site locator map and schematic diagram of the Bermuda Testbed Mooring (BTM). Figure 9. Photograph of an optical drifting buoy that measures seven channels of upwelling

radiance corresponding the same wavebands as the SeaWiFS instrument, and a single channel of downwelling irradiance for normalization. The basic buoy structure and dynamics correspond to the standard WOCE drifter.

Figure 10. Line drawing of the K-SOLO profiling float that is used to measure vertical profiles

of the spectral diffuse attenuation coefficient. Schematic by Greg Mitchell. Figure 11. (Top) Odyssey autonomous underwater vehicle (AUV) used to measure optical

variability in Massachusetts Bay and Monterey Bay. Photograph provided by Derek Manov.

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29

(Bottom) AUV glider, known as Seaglider, used to measure optical and physical properties in Puget Sound and Monterey Bay. Photograph provided by Charles Eriksen.

Figure 12. Draft global map of planned or existing time series observatories. Based on figure in

Send et al. (2001). Figure 13. Global map depicting planned global distribution of Argo profiling floats. Based on

figure in ARGO Science Team (2001). Figure 14. Schematic illustrating the input of multi-disciplinary data into an interdisciplinary

data assimilation model along with feedbacks for model improvement and adaptive sampling. Lower portion of diagram is based on Figure 20.1 of Robinson et al., 2000.

Figure 15. Concurrent images of sea surface temperature and ocean color (using CZCS color

imager for chlorophyll a) in the vicinity of Bermuda on April 20, 1984 (Top) and April 28, 1984 (Bottom). Images provided by Dennis McGillicuddy.

Figure 16. Time series (15-min. averages) of spectral downwelling irradiance for five

wavelengths using moored spectroradiometers deployed from the BTM in the spring-summer of 1997 (Dickey et al., 2001a). The three solid curves represent the minimum, maximum, and average values obtained from the radiometers during 20s sampling periods. X’s indicate concurrent data collected from ship-based (BBOP) profile observations provided by David Siegel.

Figure 17. Spectral water-leaving radiance derived from BTM radiometers and from the

SeaWiFS ocean color satellite for eight days of the sampling period from April 1 to July 21, 1999. Inserts show surface PAR time series to illustrate periods of cloudy conditions. Daily average wind speeds are also indicated.

Figure 18. Schematic diagram illustrating the various platforms used for the HyCODE

experiments at the LEO-15 study site off the New Jersey coast in the summers of 2000 and 2001. Image provided by Scott Glenn.

Figure 19. Contour plots of transects of (a) temperature, (b) salinity, and (c) chlorophyll

fluorescence with depth illustrating the presence of a water mass/turbidity front located ~12 km offshore. Partitioned spectral absorption at (d) the mid-shelf mooring (~25 km offshore) and (e) the nearshore node (~5 km offshore); at-w, ap, and ag denote total minus water, particulate, and gelbstoff absorption, respectively.

Figure 20. The inshore/offshore variation in the diffuse attenuation coefficient for upwelling radiance at 555 nm, as determined from optical sensors deployed on an undulating autonomous vehicle (REMUS, WHOI) at the LEO-15 HyCODE site in summer of 2000. The dotted lines represent the individual data points for radiance measurements, and the color scale represents the diffuse attenuation coefficient (m-1).

Figure 21. Time series contour plots of temperature at the (a) mid-shelf mooring and (b)

nearshore node, showing the deep, lower temperature, lower biomass coastal jet; Time series

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30

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 the displacement of higher biomass waters from nearshore to offshore.

Figure 22. Processed image obtained from a fluorescence imaging laser line scan instrument

deployed during the CoBOP experiment 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, vase sponges – purple, sand – green, shadowed pixels and non-fluorescing targets – black, and ‘other’ – pink. Such images enable computation of percent cover by type and size and spatial distributions. Image provided by Charles Mazel.

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Figure 1. Schematic diagrams illustrating several different optical and bio-optical systems that have been deployed from moorings.

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Figure 2. Photograph of a moored optical system used to measure chlorophyll fluorescence, the volume scattering function, upwelled radiance (3 wavelengths), and downwelled irradiance (3 wavelengths). Insert photograph shows a special copper shutter device for

minimizing biofouling of the sensors. Photographs provided by Derek Manov.

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Figure 3. Photograph of a moored optical system used to measure PAR (scalar and vector), upwelled radiance (7 wavelengths), downwelled irradiance (7 wavelengths), pressure,

temperature, tilt, and dissolved oxygen. Photograph provided by Derek Manov.

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Figure 4. Time-horizontal space diagram illustrating physical and biological processes overlain with sampling domains of various platforms (moorings, satellites, AUVs, etc.).

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Figure 5. Conceptual diagram showing a variety of sampling platforms. These have been deployed in the vicinity of the island of Bermuda.

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Figure 6. Photograph of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for ocean color imaging at seven wavelengths. Photograph provided by NASA Goddard Space Flight Center.

Page 37: Toward a Global Ocean System for Measurements of Optical … · 2002-05-13 · on optical properties with practical applications ranging from diver visibility to coastal bathymetric

Figure 7. Photograph of the Ocean Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS), a hyperspectral imaging spectrometer operating over the

wavelength range 400 to 1000 nm at 1.7 nm spectral resolution (HyCODE Photo Gallery).

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Figure 8. Site locator map and schematic diagram of the Bermuda Testbed Mooring (BTM).

Page 39: Toward a Global Ocean System for Measurements of Optical … · 2002-05-13 · on optical properties with practical applications ranging from diver visibility to coastal bathymetric

Figure 9. Photograph of an optical drifting buoy that measures seven channels of upwelling radiance corresponding to the same wavebands of the SeaWiFS instrument, and a single channel of downwelling irradiance for normalization. The basic

buoy structure and dynamics correspond to the standard WOCE drifter.

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Figure 10. Line drawing of the K-SOLO profiling float that is used to measure vertical profiles of the spectral diffuse attenuation coefficient. Drawing provided by Greg Mitchell.

Page 41: Toward a Global Ocean System for Measurements of Optical … · 2002-05-13 · on optical properties with practical applications ranging from diver visibility to coastal bathymetric

Figure 11. (Top) Photograph of the Odyssey autonomous underwater vehicle (AUV) used to measure optical variability in Massachusetts Bay and Monterey Bay. Photograph provided by Derek Manov. (Bottom) AUV glider, known as Seaglider, used to

measure optical and physical properties in Puget Sound and Monterey Bay. Photograph provided by Charles Eriksen.

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Figure 12: Draft global map of planned or existing time series observatories. Based on figure in Send et al. (2001).

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Figure 13. Global map depicting planned global distribution of Argo profiling floats. Based on figure in ARGO Science Team (2001).

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Figure 14. Schematic diagram illustrating the input of multi-disciplinary data into an interdisciplinary data assimilation model along with feedbacks for model improvement and adaptive sampling. Diagram is based on Figure 20.1 of Robinson et al., 2000.

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Figure 15. Concurrent images of sea surface temperature and ocean color (using CZCS color imager for chlorophyll a) in the vicinity of Bermuda on April 20, 1984 (Top) and April 28, 1984 (Bottom). Images provided by Dennis McGllicuddy.

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Figure 16: Time series (15-min. averages) of spectral downwelling irradiance for five wavelengths using moored spectroradiometersdeployed from the BTM in the spring-summer of 1997. The three solid curves represent the minimum, maximum, and average values obtained from the radiometers during 20 s sampling periods. X’s indicate concurrent data collected from ship-based (BBOP) profile

observations provided by David Siegel.

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Figure 17. Spectral water-leaving radiance derived from BTM radiometers and from the SeaWiFS ocean color satellite for eight days of the sampling period from April 1 to July 21, 1999. Inserts show surface PAR time series to illustrate periods of cloudy conditions.

Daily average wind speeds are also indicated.

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Figure 18. Schematic diagram illustrating the various platforms used for the HyCODE experiments at the LEO-15 study site off the New Jersey coast in the summers of 2000 and 2001. Image provided by Scott Glenn.

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Figure 19. Contour plots of transects of (a) temperature, (b) salinity, and (c) chlorophyll fluorescence with depth illustrating the presence of a water mass/turbidity front located ~12 km offshore; Partitioned spectral absorption at (d)

the mid-shelf mooring (~25 km offshore) and (e) the nearshore node (~5 km offshore); at-w, ap, and ag denote total minus water, particulate and gelbstoff absorption, respectively.

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Figure 20. The inshore/offshore variation in the diffuse attenuation coefficient for upwelling radiance at 555 nm, as determined from optical sensors deployed on an undulating autonomous vehicle (REMUS, WHOI) at the LEO-15 HyCODE site in summer of 2000.

The dotted lines represent the individual data points for radiance measurements, and the color scale represents the diffuse attenuation coefficient (m-1).

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Figure 21. Time series contour plots of temperature at the (a) mid-shelf mooring and (b) nearshore node, showing the deep, 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 the displacement of higher biomass waters from nearshore to offshore.

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Figure 22. 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, vase sponges – purple, sand – green, shadowed pixels and non-fluorescing targets – black, and ‘other’ – pink. Such images enable computation of percent cover

by type and size and spatial distributions. Image provided by Charles Mazel.