Optical properties derived from ocean color imagery represent vertically-integrated values from roughly the first
attenuation length in the water column, thereby providing no information on the vertical structure. Robotic, in situ
gliders, on the other hand, are not as synoptic, but provide the vertical structure. By linking measurements from these
two platforms we can obtain a more complete environmental picture. We merged optical measurements derived from
gliders with ocean color satellite imagery to reconstruct vertical structure of particle size spectra (PSD) in Antarctic shelf
waters during January 2007. Satellite-derived PSD was estimated from reflectance ratios using the spectral slope of
particulate backscattering (γbbp). Average surface values (0-20 m depth) of γbbp were spatially coherent (1 to 50 km
resolution) between space and in-water remote sensing estimates. This agreement was confirmed with shipboard vertical
profiles of spectral backscattering (HydroScat-6). It is suggested the complimentary use of glider-satellite optical
relationships, ancillary data (e.g., wind speed) and ecological interpretation of spatial changes on particle dynamics (e.g.,
phytoplankton growth) to model underwater light fields based on cloud-free ocean color imagery.
A few years ago, Lee and Carder demonstrated that for the quantitative derivation of major properties in an
aqua-environment (information of phytoplankton biomass, colored dissolved organic matter, and bottom
status, for instance) from remote sensing of its color, a sensor with roughly ~17 spectral bands in the 400 −
800 nm range can provide acceptable results compared to a sensor with 81 consecutive bands (in a 5-nm
step). In that study, however, it did not show where the 17 bands should be placed. Here, from nearly 300
hyperspectral measurements of water reflectance taken in both coastal and oceanic waters that covering both
optically deep and optically shallow waters, first and second derivatives were calculated after interpolating
the measurements into 1-nm resolution. From these hyperspectral derivatives, the occurrence of zero value at
each wavelength was accounted for, and a spectrum of the total oc-urrences was obtained, and further the
wavelengths that captured most number of zeros were identified. Because these spectral locations indicate
extremum (a local maximum or minimum) of the reflectance spectrum or inflections of the spectral
curvature, placing the bands of a sensor at these wavelengths maximize the possibility of capturing (and then
accurately restoring) the detailed curve of a reflectance spectrum, and thus maximize the potential of
detecting the changes of water and/or bottom properties of various aqua environments with a multi-band
sensor.
In this study, measurements and models are used to test the closure between remote-sensing reflectance and IOPs. Measurements include those by AC9 (Wetlabs, Inc.) and HS6 (HOBI labs, Inc.), while models include both empirical models (e.g., Voss' beam attenuation coefficient model) and radiative transfer model (e.g., Hydrolight). It is found that, generally, AC9 works better than HS6 in providing scattering and backscattering coefficients. HS6 need more accurate calibration; absorption coefficients by AC9 are consistent with those by Spectrix or Spectrometer. Good linear relationship is found between AC9 measured beam attenuation coefficients (c) and the Voss model; while those measurements by HS6 needs some adjustments before feeding to HYDROLIGHT.
The Autonomous Marine Optical System (AMOS) measures remote sensing reflectance (Rrs) above the water surface and subsurface optical properties (irradiance at depth, beam attenuation, chlorophyll fluorescence, and light backscattering) at predetermined times throughout the day. Data are transmitted back by radio to a networked archival and processing station. AMOS was created to routinely monitor the optical properties of near-surface waters, and make those measurements available to researchers over an Ethernet connection with minimal delay. The Rrs measurements can be used not only to validate satellite and airborne remote sensing imagery, but also to be combined with the in situ measurements so that other water column properties can be estimated. The performance of visible and machine-aided hull inspection is strongly affected by the optical properties of the water. AMOS estimates of these optical properties can be used by optical models to predict both subsurface visibility and the amount of ambient light beneath ships at port inspection sites. An example of the application of an inverse hyperspectral Rrs model to AMOS data from the Port of St. Petersburg (FL) is shown to accurately estimate light absorption due to phytoplankton and colored dissolved organic matter (CDOM), and backscattering due to particles.
Current ocean color algorithms based on remote-sensing reflectance spectra, Rrs(λ), overestimate chlorophyll a concentrations, Chl, and particulate backscattering coefficients, bbp(λ), in optically shallow oceanic waters due to increased bottom reflectance. Since such regions often contain important ecological resources and are heavily influenced by human populations, accurate estimates of Chl and bbp(λ) are essential for monitoring algal blooms (e.g. red tides), detecting sediment resuspension events and quantifying primary productivity. In this study, a large synthetic data set of 500 Rrs(λ) spectra is developed to examine limitations of ocean color algorithms for optically shallow waters and to develop alternative algorithms that can be applied to satellite (e.g. SeaWiFS and MODIS) and aircraft ocean color sensor data. Rrs(λ) spectra are simulated using a semi-analytic model for optically shallow waters. The model is parameterized with sand bottom albedo spectra, ρ(λ), using a wide range of chlorophyll a concentrations (0.03-30 mg m-3), bottom depths (2-50m) and bottom albedos (ρ(550)=0.01-0.30) to provide a robust data set that accurately represents and complements shipboard Rrs(λ) data from the Gulf of Mexico and Bahamian waters. The accuracy of a remotely-based technique developed recently from shipboard Rrs(λ) data is tested on the synthetic data for identifying waters with bottom reflectance contributions at Rrs(555) greater than 25%. Limitations and improvements regarding this method are discussed.
KEYWORDS: Ocean optics, Inspection, Coastal modeling, 3D modeling, Sensors, Optical inspection, Data modeling, Monte Carlo methods, Environmental sensing, Laser range finders
There are 361 ports of interest to the US Coast Guard regarding homeland security issues. Speed and accuracy of inspections there for “foreign objects” is critical to maintaining the flow of commerce through these ports. A fusion of acoustic and optical imaging technologies has been implemented to rapidly locate anomalies acoustically and inspect them optically. Results of field tests are presented. Effective deployment of AUV- or ROV-mounted optical sensors to inspect ship hulls and port facilities will depend on accurate, real-time prediction of the sub-surface optical environment and upon accurate sensor models parameterized for the time and place of inspection. For bi-static laser-line scanner sensors such as the Real-time Ocean Bottom Optical Topographer (ROBOT), ambient light decreases the range to the inspection object (e.g. hull) for which laser-line contrast is adequate for ranging and imaging in 3-D. Reduced range implies narrower swaths and longer inspection times. A 2-D and 3-D hybrid marine optical model (HyMOM) of the environment beneath ships or adjacent to sea walls and pilings has been developed, applied and validated in eutrophic and mesotrophic settings, and a Monte Carlo sensor model of ROBOT has been developed. Both are discussed and combined to evaluate sensor performance in different environments. To provide the inherent optical properties needed to run such models, data from the Autonomous Marine Optical System (AMOS) were collected and transmitted back to the laboratory. Examples of AMOS results and model outputs are presented.
A spectra-matching optimization algorithm, designed for hyperspectral sensors, has been implemented to process SeaWiFS-derived multi-spectral water-leaving radiance data. The algorithm has been tested over Southwest Florida coastal waters. The total spectral absorption and backscattering coefficients can be well partitioned with the inversion algorithm, resulting in RMS errors generally less than 5% in the modeled spectra. For extremely turbid waters that come from either river runoff or sediment resuspension, the RMS error is in the range of 5-15%. The bio-optical parameters derived in this optically complex environment agree well with those obtained in situ. Further, the ability to separate backscattering (a proxy for turbidity) from the satellite signal makes it possible to trace water movement patterns, as indicated by the total absorption imagery. The derived patterns agree with those from concurrent surface drifters. For waters where CDOM overwhelmingly dominates the optical signal, however, the procedure tends to regard CDOM as the sole source of absorption, implying the need for better atmospheric correction and for adjustment of some model coefficients for this particular region.
Apply a newly developed ocean-color inversion algorithm to measurements made in the west Florida Shelf and off the California coast, we derived the particle backscattering coefficient and the total absorption coefficient. From the derived total absorption coefficient, we calculated the absorption coefficients of the phytoplankton pigments and that of the gelbstoff (Colored Dissolved Organic Matter). From the derived pigment absorption coefficients, we further calculated the chlorophyll-a concentrations. These derived values were compared with those measured from water samples. It is found that the derived values are very consistent with those measured ones, suggesting that the inversio nmethods work very well for these coastal waters, and can be applied to satellite data for wide observations.
Near-shore coastal waters are important for our quality of life, but near-shore environments are under continuous stress due to human activities and natural events. Also, data on some navigational charts are often decades old. Many do not reveal changes due to recent strong storms or coastal evolution. As a result, the doubtful bathymetric data can make coastal navigation dangerous. Methods and techniques are also needed to monitor the properties of near-shore waters as well as the condition of benthic ecosystems such a seagrass beds. Traditional ship-borne surveys are slow and expensive, and there is no need to remeasure all coastal areas as environmental change is in general a slow process. It is more important to find and flag the places where significant changes may have occurred and then focus field programs in those regions. One practical method may be to use satellite sensors, which have been proven very useful for quickly providing important environmental information over large areas. Satellite sensors for ocean studies use the relationship between the spectral signals received at the sensor and the contents below the sea surface to detect oceanic properties such as chlorophyll concentration. The use of passive spectral data for bathymetry was first demonstrated in the late 1960s (Polcyn and Sattinger 1969). Using several different approaches, reasonable results were obtained with limited spectral channels (Polcyn et al. 1970, Lyzenga 1985, Clark et al. 1987). All of these approaches require some important assumptions, however. These assumptions are not always valid. There is a strong need for a method to analytically and simultaneously derive bottom depth and albedo and the optical properties of the water column relying simply on remotely sensed data.
Global maps of MODIS pigment products were produced using SeaWiFS and AVHRR data from 2 and 3 July 1998. The global mode value for the Case 2 chlorophyll product is 0.075 mg m-3, while global modes for two empirical chlorophyll algorithms are 0.02 to 0.03 mg m-3 higher. The Case 2 chlorophyll product shows a pronounced bimodal distribution with a secondary mode at around 0.30 mg m-3, which is consistent with high-latitude in situ pigment concentration data from the Southern Ocean. Analysis of spectral ratios of Rrs suggests that the 412 nm channel of SeaWiFS may be 1.5-2.0% too low. Blue-absorbing aerosols are not correctly removed from the imagery of the Mediterranean Sea, causing erroneously high retrievals of gelbstoff (or CDOM) absorption coefficient. A numerical filter to diagnose the presence of Saharan dust is suggested.
KEYWORDS: Imaging systems, Spectroscopy, Signal to noise ratio, Remote sensing, Space operations, Reflectivity, Short wave infrared radiation, Calibration, Spatial resolution, Water
A wide variety of applications of imaging spectrometry have been demonstrated using data from aircraft systems. Based on this experience we have developed requirements for a satellite imaging spectrometer system to best characterize the littoral environment, for scientific and environmental studies and to meet Naval needs. This paper describes the process for determining those requirements and the resulting hyperspectral remote sensing technology (HRST) program. The HRST spacecraft has a coastal ocean imaging spectrometer with adequate spectral and spatial resolution and high signal to noise to provide long term monitoring and real- time characterization of the coastal environment. It includes on-board processing for rapid data analysis and data compression, a large volume recorder, and high speed downlink to handle the required large volumes of data. This is a joint program with an industrial partner,and their commercial remote sensing requirements are included in the system design.
There is growing interest in the development and utilization of optical instrumentation to measure water properties of coastal waters for ground-truthing satellite data. Current methods for determining above-water remote-sensing reflectance assume vertical homogeneity in the water column. In cases where in-water vertical structure and bottom reflectance confound standard algorithms, new methods must be developed to incorporate inhomogeneities. This paper addresses the available avenues for characterizing optical properties, color-correcting underwater imagery and determining bottom albedo values. The method begins by deriving backscatter from remote-sensing reflectance data collected near the red end of the visible spectrum near the surface where bottom reflectance is negligible and path radiance is maximal. Measured upwelling radiance is divided by measured downwelling irradiance yielding underwater remote sensing reflectance values. The backscattering coefficient is then modeled for each wavelength and the path radiance calculated and removed using measured attenuation coefficients. The above values are used to reduce the algorithm to an equation for bottom albedo by removing the bias associated with path radiance and the filter effects associated with the water path to and from the bottom. The calculated bottom reflectance is needed to interpret and correct above-water remote-sensing reflectance and satellite imagery. The results are illustrated using comparisons of color-corrected and non-color-corrected in-situ imagery of specific corals and their immediate surroundings. Imagery of a coral scene at various altitudes is also presented to illustrate spectral changes due to changes in thickness of the water column between the camera and the bottom.
Remote-sensing reflectance and inherent optical properties of oceanic properties of oceanic waters are important parameters for ocean optics. Due to surface reflectance, Rrs or water-leaving radiance is difficult to measure from above the surface. It usually is derived by correcting for the reflected skylight in the measured above-water upwelling radiance using a theoretical Fresnel reflectance value. As it is difficult to determine the reflected skylight, there are errors in the Q and E derived Rrs, and the errors may get bigger for high chl_a coastal waters. For better correction of the reflected skylight,w e propose the following derivation procedure: partition the skylight into Rayleigh and aerosol contributions, remove the Rayleigh contribution using the Fresnel reflectance, and correct the aerosol contribution using an optimization algorithm. During the process, Rrs and in-water inherent optical properties are derived at the same time. For measurements of 45 sites made in the Gulf of Mexico and Arabian Sea with chl_a concentrations ranging from 0.07 to 49 mg/m3, the derived Rrs and inherent optical property values were compared with those from in-water measurements. These results indicate that for the waters studied, the proposed algorithm performs quite well in deriving Rrs and in- water inherent optical properties from above-surface measurements for clear and turbid waters.
The absorption of light by phytoplankton at a single wavelength, aph((lambda) ), is reduced with the increased packaging of the light absorption material. A common method of estimating the package effect is to divide aph((lambda) ) by the light absorption of the intracellular material after it has been extracted in an organic solvent. The absorption of the extract is often assumed to be representative of the true absorption of the cellular material in a dissolved state, asol((lambda) ). However, asol((lambda) ) is affected by the process of removing the light absorptive material from the organic matrix of the cell, the destruction of the pigment-protein complexes, and the solvent interference with the excited states of the chromophore. What is actually being measured by these extraction methods to determine asol((lambda) ), is aom((lambda) ), i.e., the absorption of light by the pigment material in the organic medium of the experiment. A solvation factor, S, that is the ratio of the true asol((lambda) ) to the measured aom((lambda) ) is needed before the package effect can be determined. We have developed an internally consistent measure of aph((lambda) ), aom(lambda), chlorophyll a concentration, and pheopigment concentration to determine the ratio asol((lambda) ):aom((lambda) ) and the package effect, Qa equals aph/at 675 nm. These parameters are used to determine a functional relationship between chlorophyll a concentrations and light absorption for high- light adapted, natural phytoplankton populations in optically clear waters. The packaging effect in these waters is negligible at the red peak of the spectrum. Exclusion of the weight specific absorption of pheopigments and the assumption of a zero aph(675) at a zero pigment concentration produces a misleading chlorophyll a-specific absorption and a false determination of pigment packaging. An algorithm is developed and validated for predicting chlorophyll a concentration from aph(675) in high-light, optically clear waters.
Remote-sensing reflectance (Rrs ratio of the water- leaving radiance to the downwelling irradiance above the surface) with and without a vertical polarizer in front of the sensor were derived for measurements made at 90 degrees to the solar plane and in a direction 30 degrees to nadir. These measurements were carried out to see if a vertical polarizer mounted in front of a sensor would improve the Rrs results. For 28 pairs of measurements with chlorophyll- a concentrations ranging from 0.07 to 38 mg/m3, solar zenith angles from 18 degrees to 66 degrees, clear to cloudy skies, and for optically shallow and deep waters, there was no significant variation between the polarized and unpolarized Rrs values. Statistical comparisons of polarized to unpolarized results provided R2 values of 0.987, 0.987, 0.994, and 0.999 with slopes 1.007, 1.005, 0.983 and 0.998 for wavelengths at 410, 440, 550 and 630 nm, respectively. These results suggest that although the underwater light field is partially polarized, a vertical polarizer in front of a sensor will provide close results to unpolarized sensor, if the measurements were made in a direction 90 degrees to the solar plane and 30 degrees to the nadir.
Scattering properties of large particles are mostly unknown, either in theory or measurement, primarily due to the significant variations of large particle characteristics in the natural environment and the inability to sample non- invasively. The Marine-Aggregated, Profiling and Enumerating Rover, measures scattering angles in the vicinity of 50, 90 and 130 degrees caused by very large particles ranging from 280 micrometers and up. Measurements were made during SIGMA cruise, April 13-21, 1994 in East Sound, Washington, using structured light sheet formed by 4 diode lasers of 660nm wavelength. The results fit the analytic phase function used by Beardsley and Zaneveld, and indicate a significant elevation of back-scattering efficiency throughout the water column for all downcasts during our multi-day experiments. Some of this increase in efficiency can be explained by multiple scattering, using Monte Carlo simulation, assuming independent scattering. Measured in-situ particle size distributions, in conjunction with Mie theory, demonstrate that large particles are significant scatterers in the ocean and contribute up to 20 percent of total scattering. These measurements support previous theories that large, marine- snow types of particles enhance back scattering efficiency and, when present, contribute significantly to remote sensing signals.
Interpretation of remotely sensed data is difficult in coastal regions compared to the open ocean, where optical signals are highly coupled to phytoplankton/chlorophyll. In estuarine and coastal areas, terrigenous colored dissolved organic matter (CDOM) does not covary with chlorophyll, and if the water column is optically shallow, bottom reflectance confounds interpretation of remote-sensing reflectance (RTS) signals. In order to more accurately model RTS in nearshore environements, bottom reflectance must be adequately characterized. During research cruises to the Florida Keys region in July, 1994 and March, 1995, reflectance spectra were obtained of various bottom types. RTS measurements, obtained during these cruises, and RTS measurements made with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were compared against RTS derived from a hyperspectral model that decoupled water column and bottom contributions to the RTS signal.
In many coastal oceans of the world, the flora and fauna are under stress. In some areas, seagrasses, coral reefs, fish stocks, and marine mammals are disappearing at a rate great enough to capture the attention of, and in some cases, provoke action by local, national, and international governing bodies. The governmental concern and consequent action is most generally rooted in the economic consequences of the collapse of coastal ecosystems. In the United States, for example, some experts believe that the rapid decline of coral reef communities within coastal waters is irreversible. If correct, the economic impact on the local fisheries and tourism industries would be significant. Most scientists and government policy makers agree that remedial action is in order. The ability to make effective management decisions is hampered, however, by the convolution of the potential causes of the decline and by the lack of historical or even contemporary data quantifying the standing stock of the natural resource of concern. Without resource assessment, neither policy decisions intended to respond to ecological crises nor those intended to provide long-term management of coastal resources can be prudently made. This contribution presents a methodology designed to assess the standing stock of immobile coastal resources (eg. seagrasses and corals) at high spatial resolution utilizing a suite of optical instrumentation operating from unmanned underwater vehicles (UUVs) which exploits the multi-spectral albedo and fluorescence signatures of the flora and fauna.
The underwater light field is affected by the geometry of the incident radiance, the sea-surface state, the inherent optical properties of the water-column constituents, the distribution of these constituents, and, in shallow areas, the bottom albedo. New instrumentation and platforms designed to assist in the quantification of the above are described. The new instrumentation includes the Marine Aggregated Particle Profiling and Enumerating Rover (MAPPER), the next-generation MAPPER II system, and the Bottom Classification and Albedo Package (BCAP). the new platforms include a custom manufactured remotely operated vehicle (ROV) designed to deploy the MAPPER II module, the BCAP module, and a vertical-profiling instrument suite, and an autonomous underwater vehicle (AUV) designed for optical measurements in coastal waters. These include the deployment of the BCAP module on long- range, bottom-mapping missions.
Particle volume spectra are often inferred from optically measured particle areal size distributions after the areal size distributions have been transformed into equivalent spherical diameter (ESD) distributions. Resolution and sensitivity differences between imaging systems result in different shapes for the measured particle size distribution. Additionally, the sensitivity threshold of the imaging system is not only fundamental to the determination of the particle edge but also determines the optical density level below which material will not be imaged. All this affects the measured size of a particle. This contribution utilizes laboratory data and unique, synchronous, ocean field data collected by three coincident imaging systems to evaluate these effects in the study of large marine particles. An algorithm rooted in the theory of moment invariants is presented which avoids the distortions to the particle size distributions when the size of non-spherical and/or porous particles are presented as ESD.
Optical case-2 waters near an ocean outfall were examined, using a combination of AVIRIS imagery and ship-based surface and profile bio-optical measurements. Bio-optical mooring data were useful in determining the hydrodynamics of the area. After correcting the image to units of water-leaving radiance (Lw), excellent agreement was achieved between remotely-sensed and in- situ measurements. Spectra from visibly different areas were extracted and compared to the in-water measurements and to each other. Near-shore spectra were dominated by the presence of suspended sediment from beach erosion. Spectra from the central part of the image had a characteristic signature from particulates from either the outfall or resuspension of bottom material or both. At the offshore edge of the image elevated levels of chlorophyll had the greatest influence on spectral shape. Backscatter at 660 nm was calculated from the AVIRIS data and a backscatter image was produced which clearly showed the distribution of the two types of sediments.
According to Kirk's as well as Morel and Gentili's Monte Carlo simulations, the popular simple expression, R approximately equals 0.33 bb/a, relating subsurface irradiance reflectance (R) to the ratio of the backscattering coefficient (bb) to absorption coefficient (a), is not valid for bb/a > 0.25. This means that it may no longer be valid for values of remote-sensing reflectance (above-surface ratio of water-leaving radiance to downwelling irradiance) where Rrs4/ > 0.01. Since there has been no simple Rrs expression developed for very turbid waters, we developed one based in part on Monte Carlo simulations and empirical adjustments to an Rrs model and applied it to rather turbid coastal waters near Tampa Bay to evaluate its utility for unmixing the optical components affecting the water- leaving radiance. With the high spectral (10 nm) and spatial (20 m2) resolution of Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS) data, the water depth and bottom type were deduced using the model for shallow waters. This research demonstrates the necessity of further research to improve interpretations of scenes with highly variable turbid waters, and it emphasizes the utility of high spectral-resolution data as from AVIRIS for better understanding complicated coastal environments such as the west Florida shelf.
Knowledge of the marine particle volume concentration is critical to many oceanographic research efforts. A description of particle volume concentration is most often estimated by assuming particle volumes based on a particle size distribution. This requires the enumeration (ideally, in situ) of a statistically significant number of particles across the size spectrum. This becomes increasingly difficult as one approaches the large-particle end of the size distribution because of the low concentrations of large particles found in most waters. However, even though number concentrations are generally low, the volume flux of large particles and their effect on the underwater light field may be significant. The Marine Aggregated Particle Profiling and Enumerating Rover (MAPPER) is an instrument under development to help address the difficulties associated with the enumeration and analysis of large marine particles. MAPPER will utilize visible diode lasers (670 nm) to produce a structured-light sheet (SLS) coincident with the image planes of video imaging systems of various resolutions. This contribution focuses on the development of the diode laser SLS, on the sheet/system characterization required to make possible the retrieval of quantitative, individual-particle information, and on the unique information offered by imaging in an SLS as opposed to other structured-light volumes. The deviation of the implemented sheet from the `ideal' sheet is presented as are the first field data acquired by deploying the SLS module on a remotely operated vehicle testbed.
The spectral fluorescence efficiency function ((eta) ((lambda) x,(lambda) m) equals quanta fluoresced per nm interval of (lambda) m per quanta absorbed at (lambda) x, (lambda) x equals excitation wavelength, (lambda) m equals emission wavelength) has been determined for several different fulvic and humic acid samples, and the 3-dimensional surfaces have been described mathematically. These data are used along with a published two-flow irradiance model to calculate the effect of solar-stimulated fluorescence due to colored dissolved organic matter (CDOM; also gelbstoff) on irradiance reflectance just below the sea surface along a transect taken on the West Florida Shelf. In addition, a strategy is suggested for using (eta) ((lambda) x,(lambda) m) and mass-specific absorption coefficient measurements of CDOM to determine CDOM concentrations from remotely sensed fluorescence measurements.
Remote sensing reflectance is easier to interpret for the open ocean than for coastal regions since bottom reflectance and fluorescence from colored dissolved organic matter (CDOM) need not be considered. For estuarine or coastal waters, the reflectance is less easy to interpret because of the variable terrigenous CDOM, suspended sediments, and bottom reflectance, since these factors do not covary with the pigment concentration. To estimate the pigment concentration, the water-leaving radiance signal must be corrected for the effects of these non- covarying factors. A two-parameter model is presented to model remote sensing reflectance of the water-column, to which contributions due to CDOM fluorescence, water Raman scattering, and bottom reflectance have been added. The purpose of this research is to try to understand the separate contributions of the water-column, CDOM fluorescence, water Raman, and bottom reflectance for stations on the West Florida Shelf and Lake Tahoe. This model requires data with spectral resolution of 10 nm or better, consistent with that provided by AVIRIS and expected from HIRIS.
The in situ imaging of marine particles shares many of the signal-to-noise difficulties of underwater imaging in general. Natural and traditional artificial illumination, for example, allow light scattered from particles outside the imaging volume, reducing the image contrast. Sizing and classification of small-particle images (magnification approaching 1 or more) have additional difficulties associated with a limited depth-of-field and the resulting noise from illuminated but unfocused targets in the field of view. Moreover, target sizing and classification are uncertain without individual target range information. The new marine particle imaging instrument to be discussed employs diode laser illumination (675 nm) with line-generator optics to produce a thin light sheet at the system focal plane. This light sheet and narrow-band, optical filters are utilized to minimize noise associated with diffuse ambient illumination since significant red ambient illumination is lost below 5 m depth. It also removes the uncertainty involved in the determination of the three-dimensional position and size of a target in a two-dimensional image. An additional problem inherent in marine particle research is that the size of the particles of interest ranges over several orders of magnitude (micrometers to centimeters diameter). The instrument addresses this problem of scale with coincident video imaging systems of high and low spatial resolution. Shape-generated feature vectors and particle optical attributes are extracted from digitized particle images and utilized in an automatic particle classification scheme. The strategy is multidimensional and incorporates a pattern recognition algorithm rooted in the theory of moment invariants.
The modeling of oceanic remote sensing reflectance typically
employs absorption and scattering parameters for the various
constituents present in marine waters. Trans-spectral light sources
such as fluorescence and Raman scattering are not generally
parameterized in these models. Bioluminescence is not considered to be
a significant contributor to water-leaving radiance measurements
obtained mid-day, and has not been included in the models either. In
this paper we present evidence of effects due to these three phenomena
by comparing model results to remote sensing reflectances measured at
several stations during the 1988 California Coastal Transition Zone
(CTZ) Experiment. Differences between modeled and measured Rrs(A)
values are discussed from the perspective of in-situ light source
contributions.
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