This PDF file contains the front matter associated with SPIE Proceedings Volume 11420, including the Title Page, Copyright information, and Table of Contents.

In response to users’ needs, NOAA SST team is developing a multi-sensor gridded “super-collated” (L3S) SST product that consolidates “uncollated” L3U data from multiple polar sensors into reduced-volume high spatial resolution product, without adding modeled data. The algorithm uses VIIRS data from multiple NPP and N20 overpasses, to create a satellite-based reference, and use it to bias-correct individual overpasses, suppress the noise, and mitigate residual cloud leakages present in individual L3U data. We present the current status of VIIRS L3S product and results of its monitoring in the NOAA global and regional systems, and validation against in-situ data.

The goal of the NOAA AVHRR GAC Reanalysis (RAN) project is to create long-term time series of uniform sea surface temperature (SST) retrievals (Level 2 and 3 products) from AVHRR data using the Advanced Clear-Sky Processor for Oceans (ACSPO) system. During Phase 1 (‘RAN1’), data of several AVHRR/3s from 2002-2015 were reprocessed. Ongoing Phase 2 (‘RAN2’) aims to cover the full period of AVHRR GAC data from 1981-on. At the time of this writing, we reprocessed five AVHRR/2s onboard NOAA-07, -09, -11, -12 and -14 and two AVHRR/3s onboard NOAA- 15 and -16, and created an initial “beta” RAN2 data set (‘RAN2 B01’) spanning ~22 years from 1981-2003. The ACSPO algorithms for cloud masking and training SST regression coefficients, initially developed for operational SST processing, required modifications to mitigate the issues, specific to the RAN2 period: multiple sensor issues, and insufficient number of in situ SST data and their degraded quality. Another derived complexity, also related to insufficient and poor quality of satellite and in situ data, is the limited availability and suboptimal quality of first guess SSTs, which is used in ACSPO for cloud masking and quality control, and employed in the right part of the Non-Linear SST equations. The paper describes modifications to the ACSPO algorithms made for the RAN2 B01, and demonstrates the resulting improvements in the retrieved SST.

Under the NOAA AVHRR GAC Reanalysis project (RAN), a global dataset of consistent sea surface temperature (SST) retrievals from 1981-on will be created from multiple NOAA AVHRRs using the ACSPO system. Following release of RAN1 dataset in 2016, the initial RAN2 Beta 01 (“RAN2 B01”) dataset was produced from NOAA-07, 09, 11, 12, 14, 15 and 16 from 1981-2003. This paper evaluates the initial RAN2 B01 dataset and compares it with two other SST datasets, the NOAA-NASA Pathfinder v5.3 (“PF”) and ESA CCI v2.1 (“CCI”). The time series of monthly global biases and standard deviations with respect to uniformly quality controlled in situ SSTs, and clearsky fractions (percent of SST pixels to the total ice-free ocean) are compared. ‘Skin’ and ‘depth’ SSTs, only available in RAN and CCI data sets, and sensitivity of ’skin’ SST to true SST, are also compared. The RAN B01 outperforms PF. Compared to CCI, it generally delivers more clear-sky observations, often with a better accuracy and precision for both ‘skin’ and ‘depth’ SSTs. The sensitivity to true SST is lower and more variable in RAN2 B01, than in CCI. The RAN2 B01 performance following large volcanic eruptions needs improvements.

Blue bands on the satellite sensors are widely used in algorithms for the estimation of chlorophyll-a concentration and CDOM absorption. Recent studies showed that high uncertainties in these bands in coastal waters are not only due to inaccurate aerosol retrievals during atmospheric correction but also due to variable sky and sun glint that is reflected from the wind-roughened ocean surface, and which is especially pronounced in waters with low blue-band reflectance. These uncertainties are estimated for the 2019 VIIRS calibration/validation cruise by the comparison of data from several satellite sensors and in situ data. Results are compared with satellite – in situ matchups at the Long Island Sound Coastal Observatory (LISCO) and other AERONET-OC sites, showing significant vulnerability of blue bands reflectance to surface effects. It is shown that uncertainties in satellite R_rs retrievals can be explained predominantly with surface reflectance and in-water variability. Propagation of these increased uncertainty levels can be mitigated by avoiding blue bands in the retrieval of chlorophyll-a concentrations in coastal waters.

A maritime environment presents a complex problem for propagating a laser through its turbulent nature. The environment includes standard aerosols, sea surface spray, and atmospheric turbulence when propagating along horizontal, vertical, or slant angled paths. Atmospheric measurements of the turbulence structure (Cn 2) and Fried’s coherence length (r0) were taken in a maritime environment using an IR Laser beacon at 1.06 microns mounted on an unmanned aerial system (UAS) at varying distances and slant angles using a Differential Image Motion Monitor (DIMM) and Wide-Angle Teleradiometric Transmissometer (WATT). The DIMM measured the coherence length, r0 and atmospheric turbulence strength, Cn 2 from the IR Laser beacon source as it propagated through the atmosphere. The WATT measured the transmissivity of the IR Laser beacon through the atmosphere.

Marine animals are known to have developed adaptations to minimize drag and energy expenditure. Among these are passive material properties, such as the streamwise-aligned riblets found on shark skin, as well as the active modulation of the viscoelastic skin layer, which is thought to give dolphins their hydrodynamic edge. These adaptations serve to delay the transition from laminar to turbulent flow in the boundary layer around the body, minimize boundary layer turbulence, and reduce frictional drag. Transition to turbulence in the boundary layer happens via the development of two-dimensional instabilities, so-called Tollmien-Schlichting (TS) waves, which break down into fully developed turbulence. One mechanism to delay transition, is to counteract TS waves, reduce their amplitude, and delay their breakdown. This can be achieved by actively modulating a deformable membrane as part of the boundary near which the instabilities develop. We investigate boundary layer flow and transition to turbulence, and the effect of actuated boundaries, in a laminar to turbulent flow tank. To test the impact of a deformable boundary on the flow, a hydrofoil is outfitted with fluid chambers overlaid by an optical quality PDMS membrane, which can be actuated in response to the flow. Flow over the hydrofoil is visualized with dye experiments and quantified with Particle Image Velocimetry. The impact of boundary actuation, as well as different boundary materials, on the flow is characterized. Achieving a reduction of boundary layer turbulence on operational scales would have profound implications for platform energy efficiency, as well as signature and acoustic noise reduction.

High resolution three dimensional (3D) optical imaging in the turbid underwater scenarios over extended length remains an outstanding challenge, primarily impeded by the absorption and scattering in turbid water, which result in substantial signal attenuation over short propagation distances. Overcoming water absorption by using optimum illumination wavelengths (480- 600 nm) of the visible spectrum, however, still requires one to address the strong scattering effects. To address the above challenge, we introduce a novel 3D imaging modality based on quantum parametric mode sorting (QPMS). It is a nascent quantum measurement technique that utilizes mode-selective quantum frequency conversion (QFC) in a χ² nonlinear waveguide to up convert signal photons in a single spatiotemporal mode efficiently. Undesirable photons in other modes, even if they spectrally and temporally overlap with the signal, are converted with much less efficiency. This unique feature, combining with picosecond time gated detection as defined by the pump pulse width, can isolate signal photon backscattered by the target from multiscattered photons by the obstacle. It thus enables imaging through a strongly scattering medium, where the background photons are orders of magnitude stronger. With QPMS, we demonstrate 3D imaging of a target occluded by strongly scattering turbid media with optical depth < 9 (<18 round trip), while needing only 10􀀀5 detected photons/pulse to achieve sub-millimeters resolution. This makes our single photon sensitive 3D imager suitable for imaging and remote sensing applications in photon-starved natural water environments where it's high sensitivity and excellent temporal resolution can be exploited to its full extent.

Axicon spatial coherence filtering is presented as a method to improve underwater optical ranging. In underwater environments, light detection and ranging (lidar) is often limited by scattering from particulate matter. Previous work suggests that scattered light and object-reflected light have different spatial phase distributions. This work exploits this difference in spatial phase, using an axicon to optically separate light with different degrees of spatial coherence. The performance of the lidar system with and without the axicon filter is compared. Axicon spatial coherence filtering demonstrates the ability to suppress multipath backscatter and forward scatter, leading to improvements in range accuracy

Ever-increasing data rate demands on the electromagnetic spectrum have become a staple of the 21st century. Additionally, the demand for underwater communication has seen dramatic growth as both the military and industry work to reap the benefits of employing unmanned underwater vehicles. Exploration of the viability of relatively untapped portions of the electromagnetic spectrum to transmit data is crucial to potentially alleviate congested spectrums as well as provide high data rate, low detect probability, and low probability of intercept modalities of data transmission. This research explores the use of visible light communication methods to transmit data in an underwater medium. Specifically, this research proposes and analyzes the performance of an underwater free-space optical transmission scheme based on twodimensional, multi-colored grids. We explore the effects the underwater medium has on the transmitted image and evaluate link performance using metrics that include data rate and bit error rate. Additionally, this work evaluates the potential performance improvements that can be gained through the employment of adaptive equalization, which is designed to minimize bit error rate at the receiver.

Communication in maritime environments presents unique challenges often requiring the secure transfer of information over long distances in a complex dynamic environment. Here a system for generating orbital angular momentum (OAM) beams, multiplexing, transmitting, and demultiplexing using a convolutional neural network (CNN) is presented. A single input from a 1550 nm seed laser is amplified, split into four separate beams that are directed and modulated by four switches, and the resulting beams directed into phase plates to create beams carrying OAM. These four beams constitute the individual channels. The beams are passed through several optical elements, coherently combined, and transmitted to a receiver at a range of 12 m. The resulting OAM beam spatial patterns are captured using a high speed short-wave infrared detector, concurrently transmitted to a workstation for storage, and processed in real-time using a trained CNN. Results from short range and quiescent environmental state show a pattern detection accuracy of <99%.

Data characterization of an eight-site nitrate-level time-series array using a suite of intra- and inter-site dimensional reduction and analysis algorithms was performed as the preliminary stage of a full Bayesian state-estimation approach for understanding the Illinois section of the Mississippi watershed. Preliminary analysis shows high mean nitrate levels in the northern, western, and southern parts of the Illinois watershed with significant correlations of nitrate levels appearing not only in the southern region, but also across a north-south transect. Intra-site dimensional reduction of the eight-site array, based on empirical orthogonal function analysis and nonnegative matrix factorization, demonstrates that specific time series, lower in number than the eight-site dimension, are responsible for both global and local variability. Inter-site dimensional reduction based on Gaussian mixture modeling applied to sets of dual-site time series in the north and south shows multimodal clusters characterized by mean and covariance information. Competitive-leaky-learning-based intersite data group modeling depicts nonlinearly generated data clusters possessing labels also based on distinct mean and covariance structure. Hidden Markov model parameter estimation applied to dual time-series sets across northern and southern regions, and over two different seasonal time scales, provides emission matrix tables with maximum probability trends consistent with the results from Gaussian mixture modeling. All facets of the machine-learning results offer a means for quantitatively describing the Illinois watershed’s nitrate-level dynamics over a fall-winter seasonal time scale.

In recent years the study of the orbital angular momentum (OAM) of light has gained traction for applications of remote sensing in underwater environments. When a laser beam propagates through turbid water, the dominant form of attenuation is scattering by large particles relative to visible wavelengths. The volume scattering function (VSF) describes the intensity distribution of light versus angle from an infinitesimal volume of scatterers. Recent computational studies have suggested that the distribution of scattered light due to a single scattering particle differs depending on whether the light is encoded with OAM or not. Other computational studies suggest that these differences are minimized when a volume of particles is illuminated. However, none of these computational projects provide experimental evidence to validate their predictions. This paper sets out to determine the experimental behavior of the VSF in the single scattering regime with and without OAM encoding on the transmitted beam. The experimental results are directly compared to Mie theory and a mixed numerical and analytical method.

The paper presents the underwater digital holographic sensor that can solve the tasks of monitoring the ecosystem biodiversity and bioproductivity for fisheries. The sensor is adapted for use in the accompanying measurement mode. The results of in situ testing of the sensor are presented.

Remote sensing of marine vehicles is involved with object detection, tracking, and classification. This task is a real challenge in the maritime environment. In recent years, the deep learning techniques are demonstrated to be effective classifiers for this purpose, however, they need to be trained properly using rich training datasets that are rarely publicly available. To overcome this limitation, there is a need for a large-scale multi-look dataset of marine vehicles while take account of different sensor ranges, azimuth and elevation observation perspectives, operating contexts, ocean and atmospheric conditions, and marine vehicle type and wakes models. In this study, we used IRIS electromagnetic modeling and simulation system for virtualization of such a maritime environment and test vehicles and created different scenarios signifying the requirements. Through this approach, we initially construct and employ the physics-based CAD models of the test marine vehicles and their corresponding wake formation patterns that realistically represent their operating signature in the marine environment. Next, we apply our specific remote sensing techniques (i.e., EO/IR, SAR, and LIDAR) to generate unique multimodality synthetic imagery of the test marine vehicles. To evaluate and verify the effectiveness of this approach, we compared our generated simulated marine vehicle imagery with those images of the corresponding physical remote sensors. In this paper, we discuss the technical aspects of this work and detail our primarily evaluations of the obtained results.

Timely response to an oil spill requires continuous areal monitoring of the ocean surface around oil facilities. Polaris Sensor Technologies has tested the Pyxis uncooled microbolometer-based polarimetric camera at the Department of Interior’s Ohmsett test facility for oil spill response over the last several years and has demonstrated excellent performance of detection of multiple types of crude oil, diesel, and kerosene in still water, in waves, during the day and overnight, and even showed strong detection of emulsified oil in waves. Further testing in the Gulf of Mexico and at the Santa Barbara seeps has also been completed. In this paper, we report these test results as well as the Pyxis-based autonomous detection system for continuous monitoring. Finally, we will describe potential operational scenarios, including deployment on fixed, floating, and drone platforms, in which this technology could be exploited for spill recovery operations as well as for automated monitoring.

In this work we present the spatio-temporal characteristics of the surface expressions generated by various species of reef fish in visible and thermal wavebands with the intention of understanding the structures formed during locomotion, station-keeping, and feeding in a large scale aquarium environment. Data collected focused on diurnal events when the majority of the fish were active and overlapped with the feeding cycle of the marine animals. Expressions generated by a sea turtle (1 m) down to smaller fish (0.3 m) were observed and recorded with the resulting surface thermal footprints varying from one meter to several centimeters respectively. Surface thermal wakes and boils were recorded as fish swarmed near the surface, breached the water, and struck at food particles floating on the surface. This collection of surface thermal features serves as a template for expected outcomes in a more complex unconfined environment such as a harbor or blue water.

This paper will discuss development work for a Navy unmanned surface vehicle called NIX for its use in multi-platform collaborative autonomy for S&T development efforts. The NIX platform is an 80” long by 40” wide M-Hull design. The M-Hull concept includes two full-length slats in its design which provides enhanced inherent tracking stability. Currently, the NIX is being outfitted with an optical imaging receiver to support high-resolution bottom imaging when paired with an unmanned underwater vehicle (UUV) that is outfitted with a laser illuminator. Together, the NIX and the UUV will form a complete bistatic laser imaging system. The bistatic imaging architecture is optimized for use in multi-platform collaborative autonomy. Precision tracking within a specific conical volume of the UUV is critical for optimal performance of the imaging system.

Geo-intelligence remote sensing platforms situated over spatially diverse areas are often tasked with geo-intelligence surveillance and adversarial monitoring for military organizations. Limited resources disallow continuous sampling of local areas at the same time, necessitating a need for smart sensing of diverse environments according to a rational evidence-based rule. Such algorithms should not only provide insight into which local region should be focused on, but should also facilitate decisions as to which environmental features should be measured over time once a local site has been selected. Multicomponent optimal learning observational arrays are demonstrated using numerically simulated data of turbulent flow to show not only the feasibility of how individual observational platforms should be chosen in a Bayesian sense, but also how goal state directed sampling of complex systems or turbulent processes over local regions can be accomplished. A Bayesian amalgamation algorithm guides which observational arrays perform knowledge gradient policy based optimal learning to smartly sample observations in local regions. Machine learning and operations research algorithms function as data agnostic, Bayesian processors demonstrating how geo-intelligence information can be efficiently captured to help solve data-driven problems.

Laboratory instruments used to measure velocity within a fluid, such as Acoustic Doppler Velocimetry (ADV) or Particle Image Velocimetry (PIV), often only gather data at one or a few points in the fluid, if using ADV, or values within a plane, when PIV is used. To get a complete picture of the total shear stress inside a container for the study of coupled biophysical interaction and stress impact on phytoplankton cells, it is best to complement measurements with a numerical model. Since the total shear stress is the primary driver in mechanical bioluminescence production, it is important to be able to accurately quantify fluid flow and dynamics at small spatial and temporal scales across the fluid domain. In this work, the fluid domains of different laboratory beakers were studied. They were modeled in Solidworks, and exported into a multi-physics software package (COMSOL) to be solved numerically. A rotating domain setup was used, and solved with a multiphase computational fluid dynamics (CFD) model, using both laminar and turbulent flow, as well as various rotational velocities. We further compare the model data to individual data points from measurements using a fiber flow sensor, to verify the model and constrain the total shear stress within the container.