The backscattering coefficient bb is one of the inherent optical properties of natural waters which means that it is independent of the ambient light field in the water. As such, it plays a central role in many problems of optical oceanography and is used in the characterization of natural waters. Essentially, any measurement that involves sending a beam of light into water must account for all inherent backscattering. Some of the applications that rely on the precise knowledge of the backscattering coefficient include studies of suspended particle distributions, optical bathymetry, and remote sensing. Many sources contribute to the backscattering, among them any suspended particles, air bubbles, and the water molecules themselves. Due to the importance of precise measurements and the ease with which water samples can be contaminated, an instrument to determine directly and quickly the backscattering coefficient in situ is highly desirable. We present such an instrument in both theory and experiment. We explain the theory behind our instrument and based on measurements made in the laboratory we demonstrate that our prototype shows the predicted behavior. We present data for increased extinction in the water, and show how measuring the extinction and taking it into account improves the quality of our measurements. We present calibration data obtained from three different particle sizes representing differently shaped volume scattering functions. Based on these measurements we demonstrate that our prototype has the necessary resolution to measure the backscattering coefficient bb over the whole range found in natural waters. We discuss potential improvements that should be made for a commercial version of the instrument.

In an effort to investigate the role of turbulence in near-forward scattering, laboratory measurements of scattering on turbulent flow were carried out in a Rayleigh-Bénard convective tank. Particle Image Velocimetry and profiling thermistor temperature measurements are used to characterize the turbulent flow through determination of the large scale flow features, turbulent kinetic energy dissipation rates, and thermal dissipation rates. Polarized diffractometer measurements allow for determination of the turbulence-induced depolarization rate, which is comparable to that observed with polarimetric lidar. Measurements were made over a range of turbulent strengths, with Rayleigh number between 10^8 and 3*10^9, and with turbulent parameters corresponding to those characteristic of the oceanic mixed layer. Results show that the turbulence-induced depolarization rate is indirectly proportional to the strength of the turbulent flow, suggesting that light beam depolarization from turbulent flow may contain useful information regarding the smallest length scales of turbulent flow.

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Phytoplankton serve as the bottom of the marine food web and therefore play an essential role in marine ecosystems. On the other hand, coastal phytoplankton communities can adversely affect the marine ecosystem and humans. A variety of techniques have been developed to measure and study phytoplankton, including in situ methods (e.g., flow cytometry) and laboratory methods (e.g., microscopic taxonomy). These provide accurate measurements of phytoplankton taxa and concentrations, yet they are limited in space and time, and synoptic information is difficult to obtain with these techniques. Optical remote sensing may provide complementary information for its synoptic nature, as demonstrated by satellite estimates of major phytoplankton taxa in major ocean basins. It has remained a challenge, however, for coastal and estuarine waters due to their optical complexity. One pioneering work relied on hyperspectral absorption spectra of phytoplankton pigments (Millie et al., 1995), from which Gymnodinium breve (i.e., Karenia brevis) blooms on the West Florida shelf could be detected and quantified in situ. However, whether a similar approach can be developed for estuarine waters where toxic blooms are often found is still unknown. Thus, the objective of this study is to test and develop an approach to classify major phytoplankton taxa found in two estuaries in Florida, U.S.A., based on optical analysis of the phytoplankton absorption spectra. In this study, over 250 surface water samples were collected on numerous cruise surveys from two Florida estuaries (Tampa Bay, ∼1000 km2 on the west coast; and the Indian River Lagoon, ∼900 km2 on the east coast). The samples were filtered and then processed using standard NASA protocols to determine 1) their spectral absorption coefficients due to phytoplankton pigments, aph (λ) (m-1), and 2) their chlorophyll a concentrations (mg m-3). aph (λ) was further normalized by Chl a, resulting in chlorophyll-specific absorption coefficient, a aph∗ (λ) (m2 mg-1). For each sample, phytoplankton cell counts were enumerated by the Florida Wildlife Conservation Commission (FWC) Fish and Wildlife Research Institute (FWRI) through microscopic taxonomy. The a aph∗ (λ) data were then categorized based on the dominant phytoplankton taxa, and were separated as either bloom or non-bloom using a 100,000 cell∕L threshold of the dominant taxa. Three techniques were tested for classifying phytoplankton taxa using absorption spectra; a first derivative summation, a relative height analysis, and an integration analysis. The integration technique proved to be the most successful of the three. This technique performed an integration of a aph∗ (572-600nm) against a linear baseline, and yielded an 81% success rate (13 of 16 samples) and 9% false positive rate (13 of 144 samples) in separating blooms of the dinoflagellate Pyrodinium bahamense from other bloom and non-bloom taxa found in the Tampa Bay estuary. The same integration technique, but with the wavelength range shifted to 471 nm - 490 nm, was also applied to the samples collected in the Indian River Lagoon estuary from summer 2011 to study the green flagellate of the class Pedinophyceae.. The results showed an 80% success rate (8 of 10 samples) and a 0.5% false positive rate (1 of 156 samples) in separating the Pedinophyceae bloom taxa from other bloom and non-bloom taxa found in both the Indian River Lagoon and Tampa Bay. The number of bloom samples was relatively low (16 from Tampa Bay and 10 from IRL). Thus, the results from this study are preliminary and will require more sampling in order to further develop this technique to a practical method for field use. However, the results obtained from this study are comparable to those from other techniques for classification of phytoplankton taxa, for example, BreveBuster, SIPPER, FlowCAM, and satellite ocean color remote sensing of the open ocean. Yet this technique extends to optically complex estuarine waters, and therefore may represent a step towards the ultimate goal of applying satellite remote sensing in characterizing phytoplankton taxa in estuaries. Once confirmed with more samples from the same two estuaries as well as from other estuaries, an immediate next step may be the implementation of in situoptical instruments on either buoys (e.g., MARVIN in Tampa Bay) or flow-through systems to provide continuous characterization of major phytoplankton taxa in the two estuaries.

Nearshore bathymetry in the Canadian Arctic is poorly surveyed, but is vital knowledge for coastal communities that rely on marine transportation for resources and development. Nautical charts currently available are often outdated and surveying by traditional methods is both time consuming and expensive. Satellite-derived bathymetry (SDB) offers a significantly cheaper and faster option to provide information on nearshore bathymetry. The two most common approaches to SDB are empirical and physics-based. The empirical approach is simple and typically does well when calibrated with high-quality in-situ data, whereas the physics-based approach is more difficult to implement and requires precise atmospheric correction. This project tests the practical use of five methods within the empirical and physics-based approaches to SDB, using Landsat 8 and Sentinel-2 satellite imagery, at seven sites across Nunavut. Methods tested include: the Ratio-Transform, Multiband, and Random Forest Regression methods (empirical) and radiative transfer modeling (physics-based) using two atmospheric correction models: ACOLITE and Deep Water Correction. All methods typically use geolocated water depth data for validation, as well as calibration for the empirical methods. Spectral reflectance for model inputs were collected in Cambridge Bay, NU. Water depth data were acquired from the Canadian Hydrographic Service. All processing was conducted within the framework of plugins developed for the open-source GIS software, QGIS. Results from the empirical methods were typically poor due to poor calibration data, though Random Forest Regression performed well when good calibration data were available. Due to poor quality validation data, error for the physics-based results cannot be adequately quantified in most places. Additionally, atmospheric correction remains a challenge for the physics-based methods. Overall, results indicate that where large, high-quality calibration datasets are available, Random Forest Regression performs best of all methods tested, with little bias and low mean absolute error in water less than 10 m deep. As such datasets are rare in the Arctic, the physics-based method is often the only option for SDB and is an excellent qualitative tool for informing communities of shallow bathymetry features and assessing navigation risk.

Remote sensing is a powerful tool for studying the marine environment; however, many images are contaminated by sun glint, the specular reflection of light from the water surface. Improved radiative transfer modelling could lead to better methods for estimating and correcting sunglint. This thesis explores the effect of using detailed numerical models of the sea surface when investigating the transfer of light through the atmosphere-ocean system. New numerical realisations that model both the shape and slope of the sea surface have been created; these contrast with existing radiative transfer models, where the air-water interface has slope but not elevation. Surface realisations including features on a scale from 3 mm to 200 m were created by a Fourier synthesis method, using up to date spectra of the wind-blown sea surface. The surfaces had mean square slopes and elevation variances in line with those of observed seas, for wind speeds up to 15 m/s. Ray-tracing using the new surfaces gave estimates of reflected radiance that were similar to those made using slope statistics methods, but significantly different in 41% of cases tested. The mean difference in the reflected radiance at these points was 19%, median 7%. Elevation-based surfaces give increased sideways scattering and reduced forward scattering of light incident on the sea surface. The elevation-based models have been applied to estimate pixel-pixel variation in ocean colour imagery and to simulate scenes viewed by three types of sensor. The simulations correctly estimated the size and position of the glint zone. Simulations of two ocean colour images gave a lower peak reflectance than the original values, but higher reflectance at the edge of the glint zone. The use of the simulation to test glint correction methods has been demonstrated, as have global Monte Carlo techniques for investigating sensitivity and uncertainty in sun glint correction. This work has shown that elevation-based sea surface models can be created and tested using readily-available computer hardware. The new model can be used to simulate glint in a variety of situations, giving a tool for testing glint correction methods. It could also be used for glint correction directly, by predicting the level of sun glint in a given set of conditions.