The seasonal cycle in surface biological, optical and physical properties across the river dominated Mississippi (MS) Shelf changed during years 2015 to 2017 at different locations across the shelf. VIIRS satellite and ocean model products were used to monitor cycles for different properties of both the nowcast and anomalous water properties. MS Shelf water properties vary spatially between offshore waters and coastal MS Sound waters, as well as temporally throughout the year. Ten selected regions spanning east to west from the MS Sound to the shelf break characterized the cross shelf seasonal fluctuations in satellite-derived chlorophyll-a, backscattering, euphotic depth, sea surface temperature, and modeled salinity currents. The seasonal relationships between physical and bio-optical properties were determined for different regions across the shelf and the seasonal eastward movement of the MS river plume across the shelf was identified in June. Yearly MS Sound seasonal cycles of coastal bio-physical properties are different from the shelf regions’ offshore seasonal cycles and indicate a time-lag between the bio-optical responses to the physical properties. Bio-optical and physical results on the shelf indicated seasonal movements of the MS River plume locations. Results show the seasonal bio-physical response of the shelf waters which can be used to address and understand the timing of data collection and how ocean events are influenced by the natural seasonal cycle interactions between biological and physical properties. The seasonal cycle study will enable the ability to monitor the shelf water quality and to identify non-typical conditions and the impact of an event on the cycle. Correlations between the monthly seasonal cycle of bio-optical and physical properties such as salinity, ocean color, chlorophyll-a and particle scattering were not consistent over the shelf. Seasonal cycles of salinity and chlorophyll-a show improved correlation if chlorophyll-a is delayed one month from the salinity at offshore locations on the shelf. Results of the seasonal trends support how data collected at a single image location on the shelf during a certain month can be different from other seasons. The seasonal cycle of the dynamic anomaly properties (DAP) of bio-physical properties were determined to show how seasonal abnormal changes and trends at locations across the shelf can provide a method for seasonal adaptive sampling. The yearly differences in monthly cycles from 2015 to 2017 at shelf locations, identified elevated chlorophyll-a in several months of 2016 and yearly temperature differences in multiple areas. The seasonal cycle of Euphotic depth, solar UV light penetration, showed a maximum peak (deeper Euphotic depth) at certain shelf locations during the months of September and October and minimal penetration in Aug of 20152016,2017. This information could be useful to understand months for maximum oil UV degradation in case of an oil spill

Ocean observing satellites and models reveal anomalous surface properties associated with the 2016 Bonnet Carré Spillway opening and a 2016 Flower Garden Banks mortality event. Marine bio-optical and physical processes in northeastern Gulf of Mexico are largely driven by river discharge and wind-driven circulation. Satellite observations and regional ocean model output were used to evaluate these processes and their interactions over large spatial areas. Climatology of Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color imagery and Navy Coastal Ocean Model American Seas (AMSEAS) output for the region were generated to explore temporal variability and detect anomalous events. Here we present the 2016 and 2017 time series of 1.) 8-day 750 m resolution VIIRS observations for chlorophylla, diffuse attenuation coefficient, and euphotic depth; and 2.) 8-day 3 km AMSEAS output for surface temperature, salinity and currents. From these time series, we derive temporal anomalies for each parameter. Results from longer term anomalies show elevated ocean color values across the region following a January 2016 river flooding event, and Bonnet Carré Spillway opening, that persist through summer months. The elevated values are compared with river discharge rates and known events (i.e. July and October 2016 Flower Garden Banks mortality), revealing the impacts of the flooding to the region. Results from shorter term anomalies highlight the stages and migration of specific impacts, such as possible upwelling at Flower Garden Banks (January 2016) observed in currents and chlorophyll-a fields.

The combination of increased spectral resolution for in situ ocean optical instrumentation as well as future ocean remote sensing missions (e.g., PACE) provides an opportunity to examine new methods of analysis and ocean monitoring that were not feasible during the multispectral satellite era. For example, hyperspectral data enables a much more precise determination of the apparent true color for natural waters, one based on the full spectral shape of water-leaving radiance distributions. Herein we provide examples of how specific integrated biogeo-optical and physical processes in the northern Gulf of Mexico have characteristic hyperspectral signatures, and thusly, characteristic true color identifiers. Our emergent hypothesis is that once the characteristic hyperspectral color signature of a specific biophysical process is known, it can be detected and monitored even with multispectral or broad-band response digital imaging systems. To test this hypothesis, we examine archived imagery from MODIS and HICO to identify putative bottom boundary layer ventilation events along divergent shelf-frontal boundaries across the northern Gulf continental margin. Whereas on-demand in situ physical data that provide spatiotemporal correspondence with archived images are not available, we employ the data-assimilative Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) as a physical data surrogate. Preliminary results of this method appear to support the hypothesis, with the caveat that model results must be interpreted with due caution.

The success of current ocean color satellite missions relies on the spectral quality, consistency, accuracy and precision of products (water leaving radiances, aerosols and clouds) derived from the satellite sensors. We propose leveraging available in situ data from various autonomous ocean color data collection sites to provide a near real time (NRT) spectral calibration for the Ocean Land Colour Imager (OLCI) by tuning the top of atmosphere (TOA) spectral radiances. Using the Naval Research Laboratory – Stennis Space Center (NRL-SSC) Automated Processing System (APS) software, NRT calibration of OLCI is demonstrated using in situ data from the MOBY and AERONET-OC collection sites. This calibration procedure has been used with other multi-spectral satellites to rapidly improve the data quality of emergent sensors so that they can be used to support marine spectrometric applications, track the satellite sensor stability, and enable continuity and consistency of ocean color products between several satellites.

The choice of aerosol model in the atmospheric correction is critical in the process of the derivation of the water leaving radiances from the Ocean Color (OC) imagery for ocean monitoring. For the current sensors like MODIS, VIIRS and now OLCI atmospheric correction procedures include assumptions about the characteristics of atmospheric aerosols based on relative humidity and particle size distributions. At the sea level, SeaPRISM radiometric instruments which are part of the Aerosol Robotic Network (AERONET) make direct measurements of the water leaving radiances from the ocean, as well as observations of sky radiances from which aerosol parameters such as aerosol optical depth (AOD), fraction of fine and coarse aerosols and others are determined. The discrepancies between satellite and AERONET data are usually significant in coastal areas which are primarily due to the more complex atmospheres near the coast than in the open ocean. Using NASA SeaDAS software, characteristics of aerosols in atmospheric correction models for VIIRS and MODIS sensors are retrieved and compared with the ones from AERONET-OC data in terms of AOD, and remote sensing reflectance (Rrs) at the several AERONET-OC sites. The impact of the solar angles, scattering angles determined by the Sun-sensor geometry and wind speed on the differences in aerosols parameters are evaluated and correlated with the accuracies in the retrieval of the remote sensing reflectance spectra from ocean waters. Significant dependence of AOD on the wind speed is demonstrated which is most likely related to the modeling of the state of the ocean surface.

Results of measurements by a novel snapshot hyperspectral polarimetric imager are presented using several data sets acquired from ocean platforms. Based on the unique availability of the pixel-to-pixel total, sky and water leaving radiances at multiple wavelengths, variations of these parameters for wind-roughened surface are assessed and possible errors in measurements of these parameters are estimated. Measurements made by the imager are compared with coincident ones from the green-band SALSA Stokes vector imaging camera, a push-broom hyperspectral polarimetric imager operated by Naval Research Laboratory (NRL), and with simulations using a vector radiative transfer code, all demonstrating excellent agreement.

The Indian River Lagoon (IRL) is a large barrier island estuary on Florida’s East coast. Overall, the IRL system spans 260 km or approximately 40% of Florida’s east coast. The 5,700-km2 watershed includes parts of seven counties, and its original extent has been expanded considerably by canals that drain inland areas, including a major canal linking Lake Okeechobee to the system. The IRL has been declared impaired due to excess nutrient inputs and is within an ecological zone particularly susceptible to climate change effects. Along with being large and complex, the IRL system is one of the nation’s most biologically diverse, and is a major spawning and nursery ground for numerous species of fish and shellfish, and home to populations of dolphins and endangered Florida manatees. The IRL ecosystem has large tourism, commercial and recreational fishing, boating, and aquaculture interests with an annual economic value estimated at nearly $8B. Unfortunately, recurrent large scale harmful algal bloom (HAB) events have seriously threatened both the ecological and economic stability/value of the IRL. The biological-chemical-physical complexity of the system presents a significant challenge to understanding its ecology and dynamics. This presentation will review IRL HABs, their complexities and repercussions to the ecosystem and human health, as well as developing scientific monitoring strategies for an improved understanding of their dynamics.

The St. Lucie River Estuary (SLE) in southeast Florida has a very large watershed comprised of several natural rivers and a network of artificial canals used for water supply and flood control. One of the largest and most critical of these canals is the C-44, which connects Lake Okeechobee to the South Fork of the SLE and is one of the primary means by which excess water is drained from the lake. Major discharges from the lake at the start of the 2016 summer wet season resulted in one of the most severe harmful algal blooms in the SLE in recent history, causing millions of dollars of economic losses in the area. Despite similar discharges from Lake Okeechobee in 2017 following Hurricane Irma, no such bloom occurred. However, algae blooms are not the only hazard associated with lake discharges. Large influxes of freshwater harm organisms adapted to life in a brackish estuary. Observation networks, augmented with ad hoc sampling, can speak volumes about the status of the ecosystem and the impact of water management decisions. Examination of historical data can begin to reveal the causes of negative ecological events in the SLE and the conditions correlated to the termination of the event. Stakeholders can use this data to inform choices about potential discharges, including timing and volume, and verify expected outcomes via real-time network data, thereby mitigating ecological and economic harm to the SLE.

The Indian River Lagoon Observatory (IRLO) is investigating ecological relationships in the Indian River Lagoon (IRL) and how they are impacted by natural and human-induced stressors. An important IRLO component is a network of advanced observing stations: the Indian River Lagoon Observatory Network of Environmental Sensors (IRLON). IRLON has 10 sites in the IRL and St. Lucie Estuary (SLE) to provide real-time, high-accuracy, and high-resolution water quality and weather data through an interactive website. This network enables researchers to follow environmental changes in the IRL, assist resource and planning managers to make informed decisions, model and correlate environmental data to biological, chemical and physical phenomena, and contribute to education and public outreach on the lagoon. Here we contrast two years of water quality conditions in the IRL and SLE. 2016 was a very “wet” year, including nine months of freshwater releases from Lake Okeechobee, which resulted in cyanobacteria blooms, and the nearby passage of Hurricane Matthew, which caused much shorter-term impacts in water quality. 2017 was a “dry” year, a significant drought early in the year, and major water quality parameters were substantially different than in 2016, until the nearby passage of Hurricane Irma. These high-frequency, continuous observatory data enable better quantification and modeling of relationships between environmental factors and biological processes in estuaries with tremendous climate-related interannual variability. This technology enables scientists, managers, educators, students, and the public to directly observe both long-term ecosystem changes and those driven by events, such as freshwater discharges, droughts, storms, and algal blooms.

As the Gulf of Mexico has experienced major environmental hazards in the recent past, e.g. the BP oil spill in 2010, landfall of major hurricanes, and the frequent outbreak of red tide (Karenia brevis), it is required to evaluate possible alterations in the water quality and biogeochemical balance of this partially enclosed waterbody. Chlorophyll concentrations (Chl-a) and sea surface temperature (SST) Level-2 data from five satellites during the period from 2009 to 2013 were analyzed for their spatial, temporal, and inter-annual variability. Based on the evaluation of data from 24 transects from the West Florida Shelf (WFS), highest Chl-a was observed for the sector from St. Petersburg to Sanibel Island. Additionally, high Chl-a was observed for the Big Bend region, particularly during the spring and early summer. SST distribution also closely followed Chl-a distribution, even though occasional uptick in SST values were noticed from the inner continental shelf even during high primary productivity. Furthermore, between 2010 and 2011, monthly Chl-a and SST varied across the shelf significantly and can be due to the incursion of the Loop Current towards the WFS, as suggested by Weisberg et al. (2014).

Suspended particles are important components of coastal marine ecosystems that are often the target of environmental sensing efforts (e.g. harmful algae blooms, suspended sediments). Automated measurements of individual particles provide advantages over traditional manual methods of particle analysis and sensors that measure bulk water properties commonly used for coastal ecosystem monitoring. However, the large, multidimensional data sets provided by automated particle measurement techniques can be difficult to analyze and interpret without the use of automated algorithms to classify large numbers of particles. In this paper we demonstrate efficient methods for classifying particles using an unsupervised, watershed transform based, clustering algorithm. The methods were applied to samples collected from the Indian River Lagoon, Banana River Lagoon, and St. Lucy Estuary located along the eastern coast of Florida. Samples were analyzed by flow cytometry and by imaging in flow (FlowCam). Results of analyses reveal patterns of distribution for distinct particle populations over space and time, and in relation to environmental characteristics. These methods represent a highly efficient strategy for monitoring coastal waters that can improve our understanding of ecosystem structure and function.

Karenia brevis Harmful Algal blooms (KB HABS) plague the coasts of the West Florida Shelf (WFS) and effective monitoring is needed to provide information to local authorities for health warnings etc. We compare results of satellite retrievals of KB HABs, using previously existing algorithms, for both MODIS and VIIRS, as well as with our more recently developed neural network (NN) algorithm. Retrievals are compared against in-situ HABs measurements. To obtain sufficient numbers of in-situ measurements nearly concurrent with satellite overpasses, comparisons were extended over 2012-17. Algorithms compared included nFLH, RBD OCI/OC3, GIOP and QAA. Retrieval statistics showed that the NN technique achieved the best accuracies, possibly due to the fact that it uses the 486, 551 and 671 nm channels for the retrievals, which are less impacted by atmospheric correction inadequacies in coastal waters, than the deeper blue channels used with other retrieval algorithms. Results highlight impacts of temporal variabilities on retrieval accuracies. Thus a shorter overlap time window between in-situ measurement and satellite observation of 15 minutes, showed significantly better accuracies than a 100 minutes, reflecting short-term changes in the KB HABs scene being observed. These relatively rapid temporal changes are further confirmed by retrievals from consecutive satellite overpasses: VIIRS-MODIS-A –VIIRS (second overpass) within a 100 minute period, and by in-situ measurements in the WFS. Temporal changes are seen to clearly affect the timeliness and relevance of satellite retrievals of HABs and Ocean Color parameters, particularly in coastal zones with dynamically changing conditions, and need to be taken into account, including possible development of alternate observation means in real time, such as UAVs.

Infrared polarimetry is an emerging sensing modality that offers the potential for significantly enhanced contrast in situations where conventional thermal imaging falls short. Polarimetric imagery leverages the different polarization signatures that result from material differences, surface roughness quality, and geometry that are frequently different from those features that lead to thermal signatures. Imaging of the polarization in a scene can lead to enhanced understanding, particularly when materials in a scene are at thermal equilibrium. Polaris Sensor Technologies has measured the polarization signatures of oil on water in a number of different scenarios and has shown significant improvement in detection through the contrast improvement offered by polarimetry. The sensing improvement offers the promise of automated detection of oil spills and leaks for routine monitoring and accidents with the added benefit of being able to continue monitoring at night. In this paper, we describe the instrumentation, and the results of several measurement exercises in both controlled and uncontrolled conditions.

Visual inspection of crude oil on water can determine the depth of thin layers of oil. However, catastrophic spills with millimeter (mm) thick oil will just be black, with no visual variation as the oil gets thicker. A day/night heat transfer model was developed to determine crude oil slick thickness. The model uses LWIR thermographic imagery, weather station outputs of air and water temperature, relative humidity, solar radiation, wind speed, other weather input such as cloud cover percent and altitudes, and measured thermal conductivity of Alaskan North Slope (ANS) crude oil. Outdoor field-testing was performed with fresh, weathered, and emulsified ANS crude oils that were placed on water at depths of 2-10 mm. A FLIR T640SC camera viewed the scene from a three-story roof-top to simulate small unmanned aerial vehicle (sUAV) altitudes. A low-cost, portable weather station was set up next to the pool and temperature calibrated LWIR imagery was collected every 15-minutes for 24-hours. The average oil surface temperature was measured for each target. The day/night model predicts oil slick thickness within one or two standard deviations. The fidelity of the thickness measurements is dependent on the accurate measurement of the atmospheric and weather parameters, sea state, heat transfer constants, and calibration and stability of the thermal camera.

MARINE SCOUT is a compact, lightweight, Puma and other small UAS (SUAS) compatible, multi-spectral airborne sensor payload designed to sense and discriminate oil on water (e.g., maritime oil spills), and enable via post-processing the measurement of oil thickness in marine environments. The payload includes near-infrared (NIR), short-wavelength infrared (SWIR), and long-wavelength infrared (LWIR) sensor channels that enable the detection of oil and its byproducts, the rejection of vegetative clutter, and the discrimination of thick crude oils. The stabilized airborne payload hosting the sensors compensates aircraft roll, yaw, and forward motion – the latter using a novel, enabling forward motion compensation (FMC) technology. The airborne payload’s capabilities, combined with a ground station exploitation and human interface computer, support ocean mapping and scene interrogation, producing high-fidelity, mosaiced, geo-rectified, multi-spectral image stacks along with full motion video for use by oil spill responders.

Suspended particulate matter (SPM) significantly impacts water clarity, degrading underwater electro-optical detection systems. It is also comprised of the living, detrital, and minerogenic particles that contribute to oceanic biogeochemical cycling. Models designed to derive SPM from optical properties such as particulate backscattering and attenuation have been largely empirical in nature, i.e., simple linear relationships, and therefore fluctuate with varying particle composition. Consequently, such models perform well regionally and/or temporally, but their applicability is constrained. An analytical inversion model has been developed to quantitatively interpret scattering measurements in terms of SPM. The algorithm requires measurements of backscattering and spectral attenuation. These measurements can be made with commercial-off-the-shelf technology suitable for deployment on compact autonomous platforms, thus having the potential to dramatically increase spatial and temporal resolving capabilities for SPM. Recent work evaluates the role of particle size ranges in greater detail and assesses performance for multiple data sets including the GlobCOAST data set, a large, diverse data set of high quality SPM and optical property measurements.

It is well known that elastic or compliant boundaries can have a stabilizing effect on boundary layer flow leading to a reduction in turbulence and frictional drag. This phenomenon has wide-ranging interdisciplinary applications from the study of energy-efficient propulsion to the study of blood flow through the cardiovascular system. While a substantial body of work exists on the theory of turbulent boundary layers and the transition of laminar to turbulent flow, it is equally important to measure in detail the flow near rigid and compliant boundaries to better understand the dynamics underlying the stabilizing effect and the reduction of turbulence. Recent advances in technology and computational resources have allowed the measurement and numerical simulation of boundary layer instabilities in unprecedented detail. We employ particle image velocimetry as well as high-frequency fiber-optics sensors to visualize and measure velocity and temperature fluctuations under various flow conditions: a laminar flow tank to study the development of Tollmien-Schlichting waves and the laboratory tank of the Simulated Turbulence and Turbidity Environment (SiTTE) to identify boundary layers streaks. The laboratory environments are complemented by computational fluid dynamics representations of the respective setups, implemented as high-resolution large-eddy simulation. The simulations provide spatial and temporal scales of boundary layer instabilities, allow the calculation of turbulence characteristics and add prediction capabilities. The combined approach allows the detailed characterization of boundary layer instabilities for a range of flow conditions, which is critical to improve our understanding of the impact of elastic boundaries, both active and passive, on boundary layer drag.

Our current research adds another layer of enhancement over Pulsed Laser Line Scanning (PLLS) by partially rejecting the source-to-target forward scattering effects. The technique uses the same source as PLLS system, but the receiver consists of a linear array of n time-resolved bucket collectors in the cross-axis direction. The spacing between receive elements is large enough to cause measurable time-of-flight differences in the near-field. By using a near-field beamforming algorithm, which sums together the time-resolved receiver waveforms with the appropriate delays based on the geometry, the contrast ratio is improved and some of the multiple forward scattering induced blur/glow/ghosting effects are rejected as well as improving the time-of-flight based 3D image reconstruction. This paper utilizes modeling to investigate the feasibility of this method.

In recent years, the Compressive Line Sensing (CLS) active imaging scheme has been proposed for underwater imaging applications. This concept has been demonstrated to be effective in the scattering mediums, and in the turbulence environment through simulations and test tank experiments. Nevertheless, in many atmospheric and underwater surveillance applications, the degradation of the visual environment may come from both particle scattering (turbidity) and turbulence. In this work, we studied the CLS imaging system in a hybrid environment consists of particle and turbulence induced scattering. A CLS prototype was used to conduct a series of experiments at the Naval Research Lab Simulated Turbulence and Turbidity Environment (STTE). The imaging path was subjected to various turbulence intensities and water turbidities. The adaptation of the CLS sensing model to the hybrid scattering environment was discussed. The experimental results with different turbidities and turbulence intensities were presented.

The scattering of light observed through the turbid underwater channel is often regarded as the leading challenge when designing underwater electro-optical imaging systems. There have been many approaches to address the effects of scattering such as using pulsed laser sources to reject scattered light temporally, or using intensity modulated waveforms and matched filters to remove the scattered light spectrally. In this paper, a new method is proposed which primarily uses the backscattering asymmetry property for object detection and geometric profiling. In our approach, two parallel and identical continuous wave (CW) laser beams with narrow beam widths (~2mm) are used as active illumination sources. The two beams also have controllable spacing and aiming angle, as well as initial phase difference for convenience of scanning and profiling a target. Through theory and experimental results, it will be shown that when an object leans or tilts towards one of the beam’s central trajectory, the asymmetry in the backscattered signals can be used to indicate the location or slope of the target’s surface, respectively. By varying the spacing or aiming angle of the two beams, a number of surface samples can be collected to reconstruct the object’s shape geometrically. The resolution and range limit of our approach are also measured and reported in this work. In application, our proposed method provides an economic solution to perform imaging through turbid underwater environments. Additionally, the idea can be combined with the pulsed or modulated laser signals for enhanced imaging results.

This work presents a processing technique for enhancing images collected by an underwater modulated pulse laser imaging system. Laser-based sensors offer high-resolution and high-accuracy ranging in the underwater environment. However, these capabilities can be degraded in turbid waters due to scattering. This work presents experimental results demonstrating an image processing technique that reduces the effects of both backscatter and forward scatter. Without the use of gating, filtering, or a priori information, the processing technique can generate useful imagery to 6.9 attenuation lengths in a controlled laboratory environment.

Digital holography provides a unique perspective towards studying aquatic particles/organisms. The ability to sample particles in undisturbed conditions, coupled with the ability to generate 3-D spatial distributions is currently unmatched by any other technique. To leverage these advantages, field experiments with the goal of characterizing aquatic particle properties in situ, were conducted using a submersible holographic imaging system. Diverse aquatic environments were sampled over 3 separate deployments between 2014 and 2017. The areas included: (a) The Gulf of Mexico (GoM), in the vicinity of the Mississippi river plume; (b) Lake Erie; and (c) East Sound in the US Pacific Northwest. A database of more than two million different types of particles in the 10-10000 m size range, was created after processing > 100,000 holograms. Particle size distributions (PSDs) exhibited a Junge-type distribution when characterized by size grouping into logarithmically spaced bins. Particles/plankton were also classified into different groups (e.g. diatoms, copepods). Results presented will be broadly grouped into two parts: (a) PSDs at different depths within the water column during the occurrence of a Microcystis bloom at Lake Erie and individual cell counts within these colonies; and (b) Vertical structure of plankton in East Sound, specifically the presence of diatom thin layers. Finally, the rich diversity in species composition in the GoM and successful data collection towards creating a training set to implement automated classification routines will be briefly discussed.

We are investigating the potential of the “vortex” laser beam to provide additional information of natural scenes from aircraft and space-based lidars. This type of beam has a spatial wavefront with a helical twist that creates an optical singularity on axis, and carries orbital angular momentum. We will report on preliminary results for differences in Rayleigh-Mie scattering, and scattering from rough surfaces, and plans for future studies.

We demonstrate a novel approach for transmissometry that uses an optical vortex for e↵ective discrimination between scattered and ballistic light. Current commercial transmissometers reject unwanted scattered light using a narrow field-of-view (FOV), but this technique fails in multiple scatter environments, or in Mie scattering regimes where large particles create a high probability of scatter near the beam axis. In the optical vortex approach to transmissometry, received light passes through a di↵ractive spiral phase plate. Coherent nonscattered light that passes through the phase plate will create an intensity vortex, while incoherent scattered light will not. The resulting spatial dependence can be exploited to discriminate between the scattered and ballistic light. We present experimental results demonstrating the e↵ectiveness of this approach.

Underwater communications present a challenge in the US Naval Undersea Community. During underwater operations, command and control requires effective communication on both a tactical (short-range) and strategic (long-range) level. One approach to address this issue would be the use of an optical beam to transmit underwater. This paper evaluates the use of an Orbital Angular Momentum (OAM) beam and their effectiveness in underwater transmissions as compared to a Gaussian beam. The normalized variance, or scintillation index is used as the metric for comparison. Additionally, an inlaboratory underwater turbulence emulator is used to generate and control various environmental parameters to include varying levels of salinity as well as temperature gradients. The goal of the emulation is to be able to simulate some of the scaled effects of various environments in order to better predict and gain intuition and understanding of the performance of an optical system in various environments. Results provided evaluate efficiency as a function of environmental conditions and scintillation index. Transmission in the 532 nm band is analyzed.

synthetic bio-optical dataset of inherent optical properties (IOPs) was created based on Chlorophyll concentrations ranging between 0.01 and 30 mg m^-3. Dissolved and particulate fractions of absorption were varied to account for the natural ranges in values. The IOPs will then be used as inputs to a time-resolved Monte-Carlo radiative transfer model to generate accurate lidar backscatter time history wave forms. Test experiments were performed to validate the model, where the primary lidar geometry in the model matched an existing system developed at HBOI under NOAA-OAR funding. The system uses blue and green pulsed laser sources (473 and 532 nm, respectively) and has two telescopes arranged at a 10° offset (on and off axis) from one another. The field of view of the telescopes is set at 1°. Approaches are being investigated to invert simulated and measured lidar results to derive input water column IOP properties. Results are tested through application to lidar measurements collected in an experimental tank with known suspended particle type and concentration.

Quantifying the oceanic whitecaps and subsurface bubble is critical to characterize the long term evolution of the ocean environment as they are the primary mechanism through which atmosphere and ocean exchange heat, momentum, and gas. Bubble bursting is a major production mechanisms for cloud condensation nuclei in the marine boundary layer (Quinn and Bates 2011). Turbulence driven exchange at the air-sea interface is associated with wave breaking and this includes bubble-mediated gas transfer (Woolf et al., 2007). A global coverage of oceanic whitecaps and subsurface bubble properties can provide invaluable information to study the climate system. In the past few years, we have demonstrated several applications of the ocean surface and subsurface signal from the space lidar onboard the CALIPSO satellite. We have shown that this signal included the unambiguous signature of surface and subsurface bubbles but this feature still has to be exploited. In this presentation, we will show results of whitecaps/bubble identification from the dual wavelength polarized space lidar return. We will present preliminary results of bubble properties quantification and comparison with NRL wave models.

The use of Lagrangian platforms and of Autonomous Underwater Vehicles (AUVs) in oceanography has increased rapidly over the last decade along with the development of improved biological and chemical sensors. These vehicles provide new spatial and temporal scales for observational studies of the ocean. They offer a broad range of deployment and recovery capabilities that reduce the need of large research vessels. This is especially true for ice-covered Arctic ocean where surface navigation is only possible during the summer period. Moreover, safe underwater navigation in icy waters requires the capability of detecting sea ice on the surface (ice sheets). AUVs navigating in such conditions risk collisions, RF communication shadowing, and being trapped by ice keels. In this paper, an underwater sea-ice detection apparatus is described. The source is a polarized continuous wave (CW) diode-pumped solid-state laser (DPSS) module operating at 532 nm. The detector is composed of a polarizing beam splitter, which separates light of S and P polarization states and two photodetectors, one for each polarized component. Since sea-ice is a strong depolarizer, the ratio P/S is an indicator of the presence or absence of sea-ice. The system is capable of detecting sea-ice at a distance of 12m. This apparatus is designed to be used by free drifting profiling floats (e.g., Argo floats), buoyancy driven vehicles (e.g., sea gliders) and propeller-driven robots (e.g., Hugin class AUV).

We consider the characteristics of macroalgal (kelp) targets of a LiDAR capable of assessing algal 3D morphology and quantifying algal biomass via fluorescence or differential absorption. Spectral absorption, fluorescence emission, fluorescence efficiency, and temporal fluorescence induction dynamics of Arctic algae can differ by class due to variation in photopigment complement. Surface reflectance characteristics of macroalgae can vary by morphology and structure. In this, work, we present an investigation of fluorescence via excitation-emission spectra of Arctic macroalgal targets. Simulations using these optical characteristics will later guide us in optimizing LiDAR configuration and performance under various operating conditions.

This paper derives system performance for a chaotically modulated laser rangefinder operating in turbid water, both as a function of rangefinder parameters and as a function of water and target characteristics. An ocean impulse response simulator is used to calculate signal-to-noise-ratios and target detection performance at a variety of water turbidities and target ranges. The use of a digital filter chain is demonstrated, and its effect on system performance is considered. The use of an optical backscatter-removal filter is proposed, and its potential effect on system performance is considered.

The physical and biological properties of Arctic ice and coastal benthos remain poorly understood due to the difficulty of accessing these substrates in ice-covered waters. A LiDAR system deployed on an autonomous underwater vehicle (AUV) can interrogate these 3D surfaces for physical and biological properties simultaneously. Using our understanding of the absorption, inelastic scattering (fluorescent), and elastic scattering properties of photosynthetic micro- and macroalgae excited by lasers, we present results of in situ tank tests using a two-wavelength (473 nm, 532 nm) prototype to evaluate both fluorosensor and differential absorption (DIAL) approaches using reflectance standards and selected macroalgae as targets.

Active remote sensing systems, such as Lidar, allow for range-resolved, non-contact probing of water properties and submerged objects. A new Lidar technique has been developed to measure water depth and subaqueous terrain in shallow waters where many Lidar systems suffer. To further advance this new capability, the influence of water turbidity must be addressed. Lidar systems are in use around the world to observe oceanic parameters, but retrievals primarily rely on a single-scattering assumption which breaks down in optically dense media, such as in turbid water. Multiple scattering of laser light by suspended particles can be exploited to identify levels of turbidity and particle size by recognizing the altering effects such scatter has on the polarization properties of light. For example, identifying regions by the amount the transmitted polarized laser light is depolarized enables differential detection of single- and multiplescattered photons from optically dense media. Thus, a non-invasive, Lidar remote-sensing technique for determining the properties of turbid water is being developed. Through the use of a self-developed Monte Carlo code, we investigate a) how the angular spread and altered polarization caused by multiple scattering of returned photons depends on recorded range, b) what new information this can provide about the particles doing the multiple scattering, and c) whether this can be incorporated into a Lidar transmitter/receiver using current technologies. This Monte Carlo code has wide implications for polarization-sensitive Lidar systems with multiple-field-of-view capabilities. Results from the theoretical Monte Carlo simulations indicate that a polarizationsensitive Lidar system is able to retrieve optical depth and particle size of the turbid/cloudy medium, enabling remote characterization of turbid waters.

The Global Positioning System (GPS) was originally developed to provide accurate navigation and timing services. Since then, a plethora of additional applications have been developed. This paper describes the deployment of a dual-frequency GPS receiver developed primarily for ionospheric space weather monitoring activities, but which is also capable of continuously measuring variations of water level and thus deducing tidal height. The approach uses GPS signals reflected from the water surface. Accurate water level observations are fundamental for storm-surge forecasting, informed emergency response, ecosystem management, safe navigation, and efficient coastal and nautical mapping and charting. Installations of water level gauges for making coastal tidal measurements are often restricted to areas with coastal infrastructure, and incorporate in-water stilling wells. Newer systems make primary water level measurements using downward looking acoustic sensors that still incorporate a protective well, but one that is more open to the local dynamics in order to avoid removing shorter-period wave signals. Such systems are implemented operationally by the National Oceanic and Atmospheric Administration as part of the National Water Level Observation Network (NOAA NWLON) and are the preferred method when the most exacting measurements are needed, such as for safe maritime navigation. However, NWLON units are expensive to install and maintain, and can be impractical for regions that lack coastal infrastructure and experience icing, leaving remote shorelines critically under-instrumented for water level observations, for example along the western and northern coastline of Alaska. Areas lacking sufficient water level information could benefit dramatically from increased water level observations despite a slightly reduced accuracy, for example a water level measurement to within ten centimeters instead of tenths of a centimeter. GPS receivers provide a viable alternative for water level observing at this accuracy level, they are much less expensive to install and maintain since there is no contact with the water, and they do not require coastal infrastructure, such as piers, for installation. ASTRA performed a successful pilot study with partners, the Alaska Ocean Observing System (AOOS) and the NOAA National Weather Service (NWS), demonstrating the efficacy of GPS receivers for water level measurements. The study confirmed that receivers originally developed for ionospheric space weather monitoring can also be used to accurately and reliably measure water levels and tides to generate a quality water level measurement adequate for many water level data needs, which for this demonstration was within 5 cm. We present details of the pilot study, and development of automated processing algorithms for GPS reflectometry water level observations in real time.

Bioluminescence is a striking and ubiquitous source of light in the global ocean, utilized in a variety of ecologically important communication, camouflage, and predator deterrence functions. It can be prevalent in surface waters at night and at most times in mesopelagic waters (≈200-1000m) where ambient light approaches a weak, asymptotic radiance field. The propagation of bioluminescent signals, and therefore the distance at which these signals can be detected, is dependent upon the inherent optical properties (IOPs) of the water column. The effects of IOPs on the propagation of light from isotropic point sources embedded in bioluminescent layers were examined in terms of emitted signal against background radiance throughout the water column, i.e., a metric defining the required ability to detect the emissions.

Nonnegative matrix factorization-based feature selection analysis performed on land based hyperspectral imagery of the Mississippi river identifies ten spectral bands in the visible and near infrared portion of the electromagnetic spectrum that are significant contributors to the resulting structural image clustering of sediment-laden water. Different distance metrics provide clear evidence of the potency of these spectral bands for class separation of turbid, sediment-laden water from clear water, provided that the data contains low noise. In addition, feature ranking of spectral band subsets of the identified characteristic spectral bands allows insight into the relative importance of smaller spectral band subsets for water-sediment characterization. Results support present day multispectral satellite design methods for land-water imagery where payload power resources are relegated to certain spectral bands at the expense of others.

Direct numerically simulated data can serve as a proxy for understanding many issues concerning multidimensional remotely sensed data. As a step towards performing operational Bayesian belief network modeling for rivers, which is of practical utility to naval intelligence, direct numerically simulated sediment-laden oscillatory flow is used to estimate statistical surface layer spatial eddy scales. This is done using spatial realizations of the sediment concentration, vertical velocity, and pressure fields along with feature extraction algorithms which utilize self-organizing mapping, independent component analysis, and two-dimensional omnidirectional Morlet wavelet analysis. Stress versus scale distributions exhibit distinct phase modulation over the three ambient forcing phases of maximum negative velocity, zero velocity, and maximum positive velocity. The stress versus sediment concentration scale distribution, which is of great pertinence to riverine remote sensing, exhibits a significant amount of large eddy scales suggesting coherent large-scale sediment structure formation possibly due to particle interstitial forces. The estimated statistical results can serve as feature parameters for naïve Bayesian belief network prediction of bottom boundary layer stress from surface eddy scale observations.

Medium Resolution Spectral Imager (MERSI) onboard FY-3 was a MODIS-like sensor which was designed for application in land, ocean and atmosphere. However, the prelaunch characterization of MERSI was not sufficient. The polarization property was not measured, which would influence the accuracy of sensor radiometric measurement, especially at scan edges. To evaluate the polarization sensitivity after launch, an ocean target based method was presented to model the Stokes vector entering the sensor at the top of atmosphere, and the on-orbit polarization response coefficients were estimated by regression between the modeled and measured radiance. After testing with Aqua MODIS, the method was applied to FY-3B MERSI. It revealed that there was relatively large polarization sensitivity for FY-3B MERSI visible bands. For the most sensitive band at 412nm, the polarization factor is approximately twice of that for Aqua MODIS. The mean polarization factors are above 3% for blue bands. For 412-nm band, the TOA polarization correction range is 16% for FY-3B MERSI, while 7% for Aqua MODIS on the same day.

Sea surface temperature measurements are reported using a high resolution spectroradiometer covering the longwave and midwave infrared regions viewing upwelling and down welling radiance. Measurements were collected on two different piers, one over the Atlantic Ocean (Duck, North Carolina) and the other over the Chesapeake Bay (Solomons, Maryland). The ocean salinity was 35‰ and the bay salinity was 15‰. Multiple viewing angles were examined. A method for removing reflected sky light at different angles is described. Also, a seawater emissivity model is described and applied in the data analysis. The goal is to find an optimal infrared filter pass band and viewing angle for accurate ocean surface pyrometry.

Laser beams propagating through complex media commonly experience degradation. This experiment investigates the effects of using laser beams with different wavelengths propagating along the same path as a method of mitigating distortion. We recorded intensity measurements of both a red and green laser after passing through a temperature and flow controlled underwater path and explored the effects of wavelength diversity on laser scintillation. Specifically, temperature variations were induced in a 243cm long water tank, containing 500 liters of deionized water using three heating sources. Experiments were performed with a triple pass through the tank for a total propagation length of 980cm. The final experimentation yielded repeatable and significant reductions in the scintillation of the multiple wavelength beam compared to its individual component beams.