This PDF file contains the front matter associated with SPIE Proceedings Volume 9111, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological Satellite (COMS), which was launched in June of 2010 and has eight spectral bands from the blue to the near-infrared (NIR) wavelengths in 412–865 nm, can monitor and measure ocean phenomenon over a local area of the western Pacific region centered at 36°N and 130°E and covering ~2500 × 2500 km². Hourly measurements during daytime (i.e., eight images per day from local 9:00 to 16:00) are a unique capability of GOCI to be used for the short- and long-term regional ocean environmental monitoring.

A recent study from a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korean Institute of Ocean Science and Technology (KIOST) showed that the GOCI ocean color products such as normalized water-leaving radiance spectra, nLw(λ), for GOCI coverage region derived using an iterative NIR-corrected atmospheric correction algorithm (Wang et al., Opt. Express, vol. 20, 741–753, 2012) were significantly improved compared with the original GOCI data products and have a comparable data quality as from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua in this region (Wang et al., Opt. Express, vol. 21, 3835–3849, 2013). It is also shown that the GOCI-derived ocean color data can be used to effectively monitor ocean phenomenon in the region such as tide-induced re-suspension of sediments, diurnal variations of ocean optical and biogeochemical properties, and horizontal advection of river discharge.

In this paper, we show some more recent results of GOCI-measured ocean diurnal variations in various coastal regions of the Bohai Sea, Yellow Sea, and East China Sea. With possibly eight-time measurements daily, GOCI provides a unique capability to monitor the ocean environments in near real-time, and GOCI data can be used to address the diurnal variability in the ecosystem of the GOCI coverage region. In addition, more in situ data measured around the Korean coastal regions are used to validate the GOCI ocean color data quality, including evaluation of ocean diurnal variations in the region. The GOCI results demonstrate that GOCI can effectively provide real-time monitoring of water optical, biological, and biogeochemical variability of the ocean ecosystem in the region.

Launched in late 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (NPP) spacecraft is being evaluated by NASA to determine whether this sensor can continue the ocean color data record established through the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate resolution Imaging Spectroradiometer (MODIS). To this end, Goddard Space Flight Center generated evaluation ocean color data products using calibration techniques and algorithms established by NASA during the SeaWiFS and MODIS missions. The calibration trending was subjected to some initial sensitivity and uncertainty analyses. Here we present an introductory assessment of how the NASA-produced time series of ocean color is influenced by uncertainty in trending instrument response over time. The results help quantify the uncertainty in measuring regional and global biospheric trends in the ocean using satellite remote sensing, which better define the roles of such records in climate research.

The sensitivity of ocean color products to variations in vicarious calibration gains at Top of Atmosphere (TOA) shows varying impacts in different water types for Suomi- NPP VIIRS. Blue water vicarious gains from MOBY in situ data, which is used for global open waters, and green water gains derived from complex coastal WaveCIS AERONET waters, have a different impact on spectral normalized water leaving radiances and the derived ocean color products (inherent optical properties, chlorophyll). We evaluated the influence of gains from open and coastal waters by establishing a set of ensemble-processed products. The TOA gains show a non-linear impact on derived ocean color products, since gains affect multiple ocean color processing algorithms such as atmospheric correction, NIR iterations, etc. We show how the variations within the ensemble TOA gain members spatially impact derived products from different water types (high CDOM, high backscattering, etc). The difference in color products derived from the Blue and Green water gain show a spatial distribution to characterize the product uncertainty in coastal and open ocean water types. The results of the ensemble gain members are evaluated with in situ matchups. Results suggest the sensitivity of the ocean color processing for open ocean verses coastal waters.

The VIIRS (Visible-Infrared Imaging Radiometer Suite) instrument onboard the Suomi NPP (National Polar-orbiting Partnership) spacecraft started acquiring Earth observations in November 2011. Since then, radiometric calibration applied to the VIIRS RSB (Reflective Solar Band) measurements for the SDR (Sensor Data Record) production has been improved several times. In this paper, timeline of the main upgrades to the calibration software and parameters is compared with the changes of the radiometric coefficients applied in the operational production of the VIIRS SDR. Initially, radiometric calibration coefficients were updated once per week to correct for the responsivity degradation that occurs for some of the sensor’s spectral bands due to contamination of the VIIRS telescope’s mirrors. Despite the frequent updates, discontinuities in the radiometric calibration could still affect ocean color time series. In August 2012, magnitude of the radiometric coefficient changes was greatly reduced by implementing a procedure that predicts (about a week ahead) values of the calibration coefficients for each Earth scan until a subsequent update. The updates have been continued with the weekly frequency, and the coefficient prediction errors were monitored by comparisons with the initial invariant coefficients from the following week. The predicted coefficients were also compared with the coefficients derived once per orbit from the onboard solar diffuser measurements by an automated procedure implemented in the VIIRS data operational processing software. The paper evaluates the changes in the VIIRS RSB coefficient updates for bands M1 to M7 and potential impacts of these changes on ocean color applications.

Navy operational ocean color products of inherent optical properties and radiances are evaluated for the Suomi–NPP VIIRS and MODIS-Aqua sensors. Statistical comparisons with shipboard measurements were determined in a wide variety of coastal, shelf and offshore locations in the Northern Gulf of Mexico during two cruises in 2013. Product consistency between MODIS-Aqua, nearing its end-of-life expectancy, and Suomi-NPP VIIRS is being evaluated for the Navy to retrieve accurate ocean color properties operationally from VIIRS in a variety of water types. Currently, the existence, accuracy and consistency of multiple ocean color sensors (VIIRS, MODIS-Aqua) provides multiple looks per day for monitoring the temporal and spatial variability of coastal waters. Consistent processing methods and algorithms are used in the Navy’s Automated Processing System (APS) for both sensors for this evaluation. The inherent optical properties from both sensors are derived using a coupled ocean-atmosphere NIR correction extending well into the bays and estuaries where high sediment and CDOM absorption dominate the optical signature. Coastal optical properties are more complex and vary from chlorophyll-dominated waters offshore. The in-water optical properties were derived using vicariously calibrated remote sensing reflectances and the Quasi Analytical Algorithm (QAA) to derive the Inherent Optical Properties (IOP’s). The Naval Research Laboratory (NRL) and the JPSS program have been actively engaged in calibration/validation activities for Visible Infrared Imager Radiometer Suite (VIIRS) ocean color products.

In situ data are essential for calibration, validation, and bio-optical algorithm development of ocean color remote sensing, as well as for studying and understanding of ocean optical, biological, and biogeochemical properties. Especially, calibration and validation of ocean color satellite data relies on high quality in situ data. In addition, objective evaluation of satellite ocean color products need well quality controlled in situ data from various bio-optical environments covering diverse aquatic waters. The goal of ocean color satellite sensors is to remotely derive accurate normalized water-leaving radiance spectra (nLw(λ)), therefore other water biological and biogeochemical property data can be obtained using satellite-measured nLw(λ) spectra. In this paper, we show results from analyzing in situ data processing procedure from the Marine Optical Buoy (MOBY) and NASA SeaWiFS Bio-optical Archive and Storage System (SeaBASS) used for satellite ocean color calibration and validation purposes. Various issues in determining final product of in situ radiometric data processing such as convolving nLw(λ) with respect to satellite sensor spectral response functions, sensor effective band center wavelengths, and effects of Bi-directional Reflectance Distribution Function (BRDF) are analyzed and discussed. Performance of satellite-derived nLw(λ) taking into consideration of various issues in the in situ data processing is also assessed.

Sea surface salinity is determined using the visible channels from the Visual Infrared Imaging Radiometer Suite (VIIRS) to derive regional algorithms for the Gulf of Mexico by normalizing to seasonal river discharge. The dilution of river discharge with open ocean waters and the surface salinity is estimated by tracking the surface spectral signature. The water leaving radiances derived from atmospherically-corrected and calibrated 750-m resolution visible M-bands (410, 443, 486, 551, 671 nm) are applied to bio-optical algorithms and subsequent multivariate statistical methods to derive regional empirical relationships between satellite radiances and surface salinity measurements. Although radiance to salinity is linked to CDOM dilution, we explored alternative statistical relationships to account for starting conditions. In situ measurements are obtained from several moorings spread across the Mississippi Sound and Mobile Bay, with a salinity range of 0.1 - 33. Data were collected over all seasons in the year 2013 in order to assess inter-annual variability. The seasonal spectral signatures at the river mouth were used to track the fresh water end members and used to develop a seasonal slope and bias between salinity and radiance. Results show an increased spatial resolution for remote detection of coastal sea surface salinity from space, compared to the Aquarius Microwave salinity. Characterizing the coastal surface salinity has a significant impact on the physical circulation which affects the coastal ecosystems. Results identify locations and dissipation of the river plumes and can provide direct data for assimilation into physical circulation models.

The East China Sea (ECS) is often obscured from space in the visible and near-visible bands by cloud cover, which prevents remote sensing retrieval of optical properties. However, clouds are transparent to microwaves, and satellites with L-band radiometers have recently been put into orbit to monitor sea surface salinity (SSS). Previous studies have used the mixing of fluvial colored dissolved organic matter (CDOM) near coasts, where the mixing is approximately conservative over short time scales, to estimate SSS. In this study, the usual relationship between CDOM and salinity in the ECS has been used in reverse to estimate CDOM from remotely sensed SSS in the ECS and compare that CDOM with MODIS data. The SSS data used are 7 day composites from NASA’s Aquarius/SAC-D satellite which has an L-band radiometer. The challenges in using this approach are that 1) Aquarius SSS has coarse spatial resolution (150 km), and 2) the ECS has numerous anthropogenic sources of radiofrequency interference which adds noise to the L-band signal for the SSS retrievals. Despite the limits in the method, CDOM distribution in the ECS can be estimated under cloudy conditions. In addition to all-weather retrievals, an additional advantage of the approach is that the algorithm provides an estimate of CDOM absorption that is unaffected by the spectrally similar detritus absorption that can confound optical remote sensing estimates of CDOM.

Exploitation of satellite and aircraft imagery for ocean color applications is limited by the extent to which an accurate atmospheric correction can be accomplished. Characterizing specular reflection off the sea surface is one component of this correction. The WorldView-2 configuration with two multi-spectral focal planes separated by the panchromatic focal plane and a 0.2 second offset in data collection between the two multi-spectral focal planes creates a challenging specular reflection correction scenario. On June 11, 2010 DigitalGlobe, Inc. imaged the Moreton Bay, Australia region seven times between 00:26:07 and 00:27:55 GMT with the WorldView-2 sensor. The atmosphere was exceptionally clear as confirmed by AERONET data collected at the University of Queensland in Brisbane. Specular reflection varied widely among the seven images. With the rapid imaging of the sequence of images other atmospheric and oceanic variable elements can be assumed to be effectively constant making this dataset ideal for testing glint reduction techniques. Glint reduction techniques are compared to identify which technique results in the least variable image sequence of remote sensing reflectances and greatest reduction of spatial glint-induced variability within a glint contaminated image.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-NPP (National Polar-orbiting Partnership) satellite is the first of a new series of imaging radiometers on polar-orbiting earth-observation satellites. In addition to providing real-time data for weather and ocean forecasting, VIIRS is intended to continue the measurements of MODIS (MODerate-resolution Imaging Spectroradiometer), including contributing to the Climate Data Record of Sea-Surface Temperature (SST). To achieve this objective requires a thorough assessment of the errors and uncertainties in the VIIRS SST retrievals. The results of comparisons with a range of independent measurements of SST indicate that the VIIRS SSTs have equal or better accuracies than those of the MODIS’s on Terra and Aqua, and that the VIIRS SSTs are suitable to extend the satellite-derived Climate Data Records of SST into the future.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (VCM) Intermediate Product (IP) has been developed for use with Suomi National Polar-orbiting Partnership (NPP) VIIRS Environmental Data Record (EDR) products. In particular, the VIIRS Sea Surface Temperature (SST) EDR relies on VCM to identify cloud contaminated observations. Unfortunately, VCM does not appear to perform as well as cloud detection algorithms for SST. This may be due to similar but different goals of the two algorithms. VCM is concerned with detecting clouds while SST is interested in identifying clear observations. The result is that in undetermined cases VCM defaults to “clear,” while the SST cloud detection defaults to “cloud.” This problem is further compounded because classic SST cloud detection often flags as “cloud” all types of corrupted data, thus making a comparison with VCM difficult. The Naval Oceanographic Office (NAVOCEANO), which operationally produces a VIIRS SST product, relies on cloud detection from the NAVOCEANO Cloud Mask (NCM), adapted from cloud detection schemes designed for SST processing. To analyze VCM, the NAVOCEANO SST process was modified to attach the VCM flags to all SST retrievals. Global statistics are computed for both day and night data. The cases where NCM and/or VCM tag data as cloud-contaminated or clear can then be investigated. By analyzing the VCM individual test flags in conjunction with the status of NCM, areas where VCM can complement NCM are identified.

In response to a request from the NOAA Coral Reef Watch Program, NOAA SST Team initiated reprocessing of 4 km resolution GAC data from AVHRRs flown onboard NOAA and MetOp satellites. The objective is to create a longterm Level 2 Advanced Clear-Sky Processor for Oceans (ACSPO) SST product, consistent with NOAA operations. ACSPO-Reanalysis (RAN) is used as input in the NOAA geo-polar blended Level 4 SST and potentially other Level 4 SST products. In the first stage of reprocessing (reanalysis 1, or RAN1), data from NOAA-15, -16, -17, -18, -19, and Metop-A and -B, from 2002-present have been processed with ACSPO v2.20, and matched up with quality controlled in situ data from in situ Quality Monitor (iQuam) version 1. The ~12 years time series of matchups were used to develop and explore the SST retrieval algorithms, with emphasis on minimizing spatial biases in retrieved SSTs, close reproduction of the magnitudes of true SST variations, and maximizing temporal, spatial and inter-platform stability of retrieval metrics. Two types of SST algorithms were considered: conventional SST regressions, and recently developed incremental regressions. The conventional equations were adopted in the EUMETSAT OSI-SAF formulation, which, according to our previous analyses, provide relatively small regional biases and well-balanced combination of precision and sensitivity, in its class. Incremental regression equations were specifically elaborated to automatically correct for model minus observation biases, always present when RTM simulations are employed. Improved temporal stability was achieved by recalculation of SST coefficients from matchups on a daily basis, with a ±45 day window around the current date. This presentation describes the candidate SST algorithms considered for the next round of ACSPO reanalysis, RAN2.

Discriminating clear-ocean from cloud in the thermal IR imagery is challenging, especially at night. Thresholds in automated cloud detection algorithms are often set conservatively leading to underestimation of the Sea Surface Temperature (SST) domain. Yet an expert user can visually distinguish the cloud patterns from SST. In this study, available pattern recognition methodologies are discussed and an automated algorithm formulated. Analyses are performed with the SSTs retrieved from the VIIRS sensor onboard S-NPP using the NOAA ACSPO system. Based on the analyses of global data, we have identified low-level spectral and spatial features potentially useful for discriminating cloud from clear-ocean. The algorithm attempts to mimic the visual perception by a human operator such as gradient information, spatial connectivity, and high/low frequency discrimination. It first identifies contiguous areas with similar features, and then makes decision based on the statistics of the whole region, rather than on a per pixel basis. Our initial objective was to automatically identify clear sky regions misclassified by ACSPO as cloud, and improve coverage of dynamic areas of the ocean and coastal zones.

Conventional underwater laser imaging systems are configured such that the laser illuminator and the optical receiver co-exist on the same platform. The main challenge for optical imaging in turbid water is the collection of light that is scattered multiple times on its path to and from the scene of interest. Sophisticated techniques using pulsed or modulated lasers and gated, high speed photo receivers have been developed to reject scattered light and enhance image quality. Although advancements in laser and receiver hardware have made it possible to reduce the size, weight and power of these laser imaging systems, it is still a significant challenge to make these systems compatible with small, autonomous platforms that are desirable for undersea surveillance. Researchers at NAVAIR have developed a multistatic laser imaging approach where the transmitter(s) and receiver(s) are located on separate, smaller platforms. Each laser illuminator is temporally encoded with information that is used by the receiver(s) to construct an image. Recently, this multistatic configuration was modified so that both 2D (amplitude) and 3D (amplitude and range) underwater imagery was collected. This new system design enables the system to identify low contrast objects that contain distinct range relief features. Results from recent laboratory water tank experiments will be presented to evaluate the performance of this new 3D, multistatic laser imaging system in different water environments.

The compressive line sensing (CLS) imaging system adopts the paradigm of independently sensing each line and jointly reconstructing a group of lines. This system achieves “resource compression” and is compatible with the conventional push-broom line-by-line sensing mode. This paper discusses the development of a prototype system to enable the experimental study the CLS imaging system. The results from an initial turbidity cycle experiments are presented.

The analysis of images of several underwater targets that exhibits different polarization properties measured using an underwater camera in various water conditions is presented. The measurements are compared with an imaging model which combines vector radiative transfer simulations by the RayXP program for the propagation of light in the atmosphere-interface-ocean system and the Monte Carlo simulations for the near horizontal imaging in the water. Modeling includes analysis of the vector point spread function (PSF) from the target and the contribution of the veiling light between the target and the camera.

The paper provides an overview of a Hybrid Underwater Camera (HUC) system combining sonar with a range-gated laser camera system. The sonar is the BlueView P900-45, operating at 900kHz with a field of view of 45 degrees and ranging capability of 60m. The range-gated laser camera system is based on the third generation LUCIE (Laser Underwater Camera Image Enhancer) sensor originally developed by the Defence Research and Development Canada. LUCIE uses an eye-safe laser generating 1ns pulses at a wavelength of 532nm and at the rate of 25kHz. An intensified CCD camera operates with a gating mechanism synchronized with the laser pulse. The gate opens to let the camera capture photons from a given range of interest and can be set from a minimum delay of 5ns with increments of 200ps. The output of the sensor is a 30Hz video signal. Automatic ranging is achieved using a sonar altimeter. The BlueView sonar and LUCIE sensors are integrated with an underwater computer that controls the sensors parameters and displays the real-time data for the sonar and the laser camera. As an initial step for data integration, graphics overlays representing the laser camera field-of-view along with the gate position and width are overlaid on the sonar display. The HUC system can be manually handled by a diver and can also be controlled from a surface vessel through an umbilical cord. Recent test data obtained from the HUC system operated in a controlled underwater environment will be presented along with measured performance characteristics.

Techniques have been developed to mitigate many of the issues associated with underwater imaging in turbid environments. However, as targets get smaller and better camouflaged, new techniques are needed to enhance system sensitivity. Researchers at NAVAIR have been developing several techniques that use RF modulation to suppress background clutter and enhance target detection. One approach in particular uses modulation to encode a pulse in a synchronous line scan configuration. Previous results have shown this technique to be effective at both forward and backscatter suppression. Nearly a perfect analog to modulated pulse radar, this technique can leverage additional signal processing and pulse encoding schemes to further suppress background clutter, pull signals out of noise, and improve image resolution. Additionally, using a software controlled transmitter, we can exploit this flexibility without the need to change out expensive hardware. Various types of encoding schemes were tested and compared. We report on their comparative effectiveness relative to a more conventional non-coded pulse scheme to suppress background clutter and improved target detection.

In this paper simulation and experimental results are presented for two hybrid lidar-radar modulation techniques for underwater laser ranging. Both approaches use a combination of multi-frequency and single frequency modulation with the goal of simultaneously providing good range accuracy, unambiguous range, and backscatter suppression. The first approach uses a combination of dual and single frequency modulation. The performance is explored as a function of increasing average frequency while keeping the difference frequency of the dual tones constant. The second approach uses a combination of a stepped multi-tone modulation called frequency domain reflectometry (FDR) and single frequency modulation. The FDR technique is shown to allow simultaneous detection of the range of both the volumetric center of the backscattered “clutter” signal and the desired object. Experimental and simulated results are in good agreement for both techniques and performance out to ten attenuations lengths is reported.

We are proposing a novel remote sensing technique to measure sound in the upper ocean. The objective is a system that can be flown on an aircraft. Conventional acoustic sensors are ineffective in this application, because almost none (~ 0.1 %) of the sound in the ocean is transmitted through the water/air interface. The technique is based on the acoustic modulation of bubbles near the sea surface. It is clear from the ideal gas law that the volume of a bubble will decrease if the pressure is increased, as long as the number of gas molecules and temperature remain constant. The pressure variations associated with the acoustic field will therefore induce proportional volume fluctuations of the insonified bubbles. The lidar return from a collection of bubbles has been shown to be proportional to the total void fraction, independent of the bubble size distribution. This implies that the lidar return from a collection of insonified bubbles will be modulated at the acoustic frequencies, independent of the bubble size distribution. Moreover, that modulation is linearly related to the sound pressure. The basic principles have been demonstrated in the laboratory, and these results will be presented. Estimates of signal-to-noise ratio suggest that the technique should work in the open ocean. Design considerations and signal-to-noise ratios will also be presented.

This document outlines a ‘proof-of-concept’ for the maritime application of a ship-based LIDAR system for measuring the optical and physical properties in the water column. It is divided up into sections, documenting that there exists today the engineering, modeling and optical expertise to accomplish this task as well as a discussion of the reasons that LIDAR has not become the powerful observational platform that it should have been for horizontally and vertically monitoring optical and physical water column properties. Previous research on this approach has been limited because LIDAR systems have for most cases not been thoroughly calibrated, if at all, nor have LIDARs been focused on above-water, ship-based measurements. Efforts at developing derived product algorithms with uncertainties have been limited. This review concludes that there is a huge potential for the successful application of LIDAR measurements in the marine environment to estimate the vertical distribution of optical and physical properties and that measurement costs can be minimized by deployment of these automated systems on ‘ships-of-opportunity’ and military vessels on a non-interfering basis. Although LIDAR measurements and research have been around since the 1960’s, this approach has not really been investigated by any civilian or military agencies or laboratories even though providing ‘through-sensor performance matrixes’ for existing bathymetry, target detection, underwater communication and imaging should be high on their list.

Validation measurements of satellite ocean color sensors require in situ measurements that are accurate, repeatable and traceable enough to distinguish variability between in situ measurements and variability in the signal being observed on orbit. The utility of using a Satlantic Profiler II equipped with HyperOCR radiometers (Hyperpro) for validating ocean color sensors is tested by assessing the stability of the calibration coefficients and by comparing Hyperpro in situ measurements to other instruments and between different Hyperpros in a variety of water types. Calibration and characterization of the NOAA Satlantic Hyperpro instrument is described and concurrent measurements of water-leaving radiances conducted during cruises are presented between this profiling instrument and other profiling, above-water and moored instruments. The moored optical instruments are the US operated Marine Optical BuoY (MOBY) and the French operated Boussole Buoy. In addition, Satlantic processing versions are described in terms of accuracy and consistency. A new multi-cast approach is compared to the most commonly used single cast method. Analysis comparisons are conducted in turbid and blue water conditions. Examples of validation matchups with VIIRS ocean color data are presented. With careful data collection and analysis, the Satlantic Hyperpro profiling radiometer has proven to be a reliable and consistent tool for satellite ocean color validation.

Several groups produce Sea Surface Temperature (SST) retrievals derived from data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on-board the S-NPP satellite. Because of varying requirements or history, the groups often use differing SST equations to make their SST retrievals. Here we compare and discuss the equations through an examination of the SST fields. In most cases, the fields are created using the same program but differing equations, while in other cases, such as for the Interface Data Processing Segment (IDPS) Environmental Data Records (EDR), the SST fields are directly produced by other groups. Also discussed is the effect of the equation coefficients because independent groups may use the same equation but with different coefficients The focus of this study is on a region covering the Northern Gulf of Mexico and part of the Western North Atlantic. The comparison to buoys tries to minimize the effect of data contamination such as clouds on the results by matching the best satellite derived SST value in a neighborhood to the value from drifting or moored buoys. Finally we look at the overlap between consecutive passes to evaluate how the various equations perform at higher satellite zenith angles.

A fibre optic extrinsic Fabry Perot Interferometer (EFPI) sensor is developed for monitoring pressure in the underwater and sub-seabed under simulated conditions. The sensor is robust in design and is fabricated entirely from Silica glass. The EFPI is formed at the tip of the fibre, where the single mode is spliced to a 200μm capillary, sealed by a 200μm Multimode, which forms the diaphragm. The diaphragm thickness is reduced by polishing and etching with hydrofluoric (HF) acid to about 2-3μm for a high sensitivity. The thickness of the diaphragm is monitored online during polishing and HF etching. The spectrum of the fibre optic sensor (FOS) is interrogated using a broad band optical light source and an optical spectrometer. The sensitivity of the sensor achieved is 0.6cmH₂O, excellent for small depth-changes. Experimental measurements with saturated salt water and chlorophyll pigmentation of different standards were tested, to simulate the sub-sea conditions where a stability of 0.7cmH₂O was reached with a drift of less than 10% under the simulated conditions.

The Landsat 8 satellite, recently launched (February 2013), carries the next generation of Landsat sensors and extends over 40 years of continuous imaging acquisition. Landsat 8, with its improved spectral coverage and radiometric resolution, has the potential to dramatically improve our ability to simultaneously retrieve the three primary Color Producing Agents (CPAs) (chlorophyll, colored dissolved organic matter, and suspended minerals) from water bodies and considering its 30-meter resolution should be especially useful for studying the nearshore environment. In the Case 2 water problem, accurate atmospheric correction is essential, yet remains a significant source of water-constituent retrieval error particularly since the sensor-reaching signal, due to water, is very small compared to the signal from atmospheric effects. Furthermore, the standard black target assumption commonly used for open ocean studies is not valid in turbid water due to the presence of water-leaving signal in the near infrared (NIR). In this work, a modified version of the traditional empirical line method (ELM) has been developed, which utilizes reectance from both an in-water radiative transfer model (Hydrolight) and a reectance product (Landsat surface reectance product) to atmospherically correct Landsat 8 images. This method employs pseudo-invariant feature (PIF) pixel extraction to mask urban landscape from the reectance product for the bright pixel determination. For the dark pixel, Hydrolight is used to obtain the field spectra that replaces ground-truth measurements normally used in the traditional ELM. The radiance values for the dark and bright pixels are extracted from the corresponding regions in the Landsat 8 image. Initial results of this method are compared to results obtained from a traditional ELM for validation.

Underwater temperature and salinity microstructure can lead to localized changes in the index of refraction and can be a limiting factor in oceanic environments. This optical turbulence can affect electro-optical (EO) signal transmissions that impact various applications, from diver visibility to active and passive remote sensing. To quantify the scope of the impacts from turbulent flows on EO signal transmission, and to examine and mitigate turbulence effects, we perform experiments in a controlled turbulence environment allowing the variation of turbulence intensity. This controlled turbulence setup is implemented at the Naval Research Laboratory Stennis Space Center (NRLSSC). Convective turbulence is generated in a classical Rayleigh-Benard tank and the turbulent flow is quantified using a state-of-the-art suite of sensors that includes high-resolution Acoustic Doppler Velocimeter profilers and fast thermistor probes. The measurements are complemented by very high- resolution non-hydrostatic numerical simulations. These computational fluid dynamics simulations allow for a more complete characterization of the convective flow in the laboratory tank than would be provided by measurements alone. Optical image degradation in the tank is assessed in relation to turbulence intensity. The unique approach of integrating optical techniques, turbulence measurements and numerical simulations helps advance our understanding of how to mitigate the effects of turbulence impacts on underwater optical signal transmission, as well as of the use of optical techniques to probe oceanic processes.

As part of the Joint Polar Satellite System (JPSS) Ocean Cal/Val Team, Naval Research Lab - Stennis Space Center (NRL-SSC) has been working to facilitate calibration and validation of the Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products. By relaxing the constraints of the NASA Ocean Biology Processing Group (OBPG) methodology for vicarious calibration of ocean color satellites and utilizing the Aerosol Robotic Network Ocean Color (AERONET-OC) system to provide in situ data, we investigated differences between remotely sensed water leaving radiance and the expected in situ response in coastal areas and compare the results to traditional Marine Optical Buoy (MOBY) calibration/validation activities.

An evaluation of the Suomi National Polar-Orbiting Partnership (SNPP)-VIIRS ocean color products was performed in coastal waters using the time series data obtained from the Northern Gulf of Mexico AERONET-OC site, WaveCIS. The coastal site provides different water types with varying complexity of CDOM, sedimentary, and chlorophyll components. Time series data sets were used to develop a vicarious gain adjustment (VGA) at this site, which provides a regional top of the atmospheric (TOA) spectral offset to compare the standard MOBY spectral calibration gain in open ocean waters.

Remote estimation of chlorophyll-a concentration [Chl-a] in the Chesapeake Bay from reflectance spectra is challenging because of the optical complexity and variability of the water composition as well as atmospheric corrections for this area. This work is focused on algorithms for near surface measurements. The performance and tuning of several well established global inversion algorithms that use the NIR and Blue-Green parts of the spectrum are analyzed together with recently proposed algorithm that use the Red-Green part of the spectrum. These algorithms are evaluated and tuned on our field data collected during summer 2013 field campaign in the in the Chesapeake Bay region . These data consist of a full range of water optical properties as well as chlorophyll concentrations and specific absorption spectra from in water samples.

We then compare these algorithms with a multiband retrieval algorithm that was developed using neural networks (NN) and which was trained on simulated data generated through bio-optical modeling typical for a broad range of coastal water parameters, including those known for the Chesapeake Bay. This NN algorithm was then applied to our field measurements and used to retrieve the phytoplankton absorption at 443nm which was then related to [Chl-a]. In this process, special attention was paid to field data consistency in terms of both measured reflectance and [Chl-a] values, to avoid undesirable biases and trends. All algorithm retrievals were finally evaluated by several statistical indicators to arrive at their relative merits and potential for further improvements and application to satellite data.

Autonomous underwater vehicles do not have sufficient communications bandwidth over long ranges to send back real time images even for monitoring purposes. Autonomous imaging from underwater vehicles will therefore, require realtime imaging system performance prediction in order to ensure that the vehicle can position itself at a range that will allow it to take an image of the scene or target of interest at the required resolution and contrast level. Ideally the inherent optical properties of the surrounding waters should be measured onboard. This may not be feasible or only a restricted set may be measurable. In order to improve the prediction of the imaging performance, a physics-based analytic phase function that could effectively exploit any a priori or in-situ measured parameters would be extremely helpful. Such a new physics-based analytic phase function has been derived and tested against exact scattering codes. Among other features it is sufficiently precise to allow an accurate determination of the backscatter ratio based on an estimate of the mean index of refraction. The new formulation shows clearly why the backscatter ratio, which is the dominant factor in determining imaging range, is insensitive to the inverse power of the size distribution and almost entirely controlled by the mean index of refraction. This new formulation also has a direct application to improve inverse radiative transfer equation (RTE) modeling for estimating inherent optical properties (total absorption and total backscattering) from measured apparent optical properties (ocean color).