A 3D ocean optical forecast system called TODS (Tactical Ocean Data System) has been developed to determine the
performance of underwater LIDAR detection/identification systems. TODS fuses optical measurements from gliders,
surface satellite optical properties, and 3D ocean forecast circulation models to extend the 2-dimensional surface satellite
optics into a 3-dimensional optical volume including subsurface optical layers of beam attenuation coefficient (c) and
diver visibility. Optical 3D nowcast and forecasts are combined with electro-optical identification (EOID) models to
determine the underwater LIDAR imaging performance field used to identify subsurface mine threats in rapidly
changing coastal regions. TODS was validated during a recent mine warfare exercise with Helicopter Mine
Countermeasures Squadron (HM-14). Results include the uncertainties in the optical forecast and lidar performance and
sensor tow height predictions that are based on visual detection and identification metrics using actual mine target
images from the EOID system. TODS is a new capability of coupling the 3D optical environment and EOID system
performance and is proving important for the MIW community as both a tactical decision aid and for use in operational
planning, improving timeliness and efficiency in clearance operations.
Current United States Navy Mine-Counter-Measure (MCM) operations primarily use electro-optical identification
(EOID) sensors to identify underwater targets after detection via acoustic sensors. These EOID sensors which are based
on laser underwater imaging by design work best in "clear" waters and are limited in coastal waters especially with
strong optical layers. Optical properties and in particular scattering and absorption play an important role on systems
performance. Surface optical properties alone from satellite are not adequate to determine how well a system will
perform at depth due to the existence of optical layers. The spatial and temporal characteristics of the 3d optical
variability of the coastal waters along with strength and location of subsurface optical layers maximize chances of
identifying underwater targets by exploiting optimum sensor deployment. Advanced methods have been developed to
fuse the optical measurements from gliders, optical properties from "surface" satellite snapshot and 3-D ocean
circulation models to extend the two-dimensional (2-D) surface satellite optical image into a three-dimensional (3-D)
optical volume with subsurface optical layers. Modifications were made to an EOID performance model to integrate a
3-D optical volume covering an entire region of interest as input and derive system performance field. These
enhancements extend present capability based on glider optics and EOID sensor models to estimate the system's "image
quality". This only yields system performance information for a single glider profile location in a very large operational
region. Finally, we define the uncertainty of the system performance by coupling the EOID performance model with the
3-D optical volume uncertainties. Knowing the ensemble spread of EOID performance field provides a new and unique
capability for tactical decision makers and Navy Operations.
Hyperion is a hyperspectral sensor on board NASA's EO-1 satellite with a spatial resolution of approximately 30 m and a swath width of about 7 km. It was originally designed for land applications, but its unique spectral configuration (430 nm - 2400 nm with a ~10 nm spectral resolution) and high spatial resolution make it attractive for studying complex coastal ecosystems, which require such a sensor for accurate retrieval of environmental properties. In this paper, Hyperion data over an area of the Florida Keys is used to develop and test algorithms for atmospheric correction and for retrieval of subsurface properties. Remote-sensing reflectance derived from Hyperion data is compared with those from in situ measurements. Furthermore, water's absorption coefficients and bathymetry derived from Hyperion imagery are compared with sample measurements and LIDAR survey, respectively. For a depth range of ~ 1 - 25 m, the Hyperion bathymetry match LIDAR data very well (~11% average error); while the absorption coefficients differ by ~16.5% (in a range of 0.04 - 0.7 m-1 for wavelengths of 410, 440, 490, 510, and 530 nm) on average. More importantly, in this top-to-bottom processing of Hyperion imagery, there is no use of any a priori or ground truth information. The results demonstrate the usefulness of such space-borne hyperspectral data and the techniques developed for effective and repetitive observation of complex coastal regions.
Automated validation methods and a suite of tools have been developed in a Quality Control Center to analyze the
stability and uncertainty of satellite ocean products. The automatic procedures analyze match-ups of near real time
coastal bio-optical observations from Martha's Vineyard Coastal Observatory (MVCO) with satellite-derived ocean color
products from MODIS Aqua and Terra, SeaWIFS, Ocean Color Monitor, and MERIS. These tools will be used to
compare MVCO in situ data sets (absorption, backscattering, and attenuation coefficients), co-located SeaPRISM-derived
water leaving radiances, and the Aerosol Robotic Network (AeroNet) derived aerosol properties with daily
satellite bio-optical products and atmospheric correction parameters (aerosol model types, epsilon, angstrom coefficient),
to track the long term stability of the bio-optical products and aerosol patterns. The automated procedures will be used
to compare the in situ and satellite-derived values, assess seasonal trends, estimate uncertainty of coastal products, and
determine the influence and uncertainty of the atmospheric correction procedures. Additionally we will examine the
increased resolution of 250m, 500m, and 1 km satellite data from multiple satellite borne sensors to examine the spatial
variability and how this variability affects assessing the product uncertainty of coastal match-ups of both bio-optical
algorithms and atmospheric correction methods. This report describes the status of the QCC tool development and
potential applications of the QCC tool suite.
Coupling the 3-d ocean optical imagery with 3-d circulation models provides a new capability to understand coastal
processes. Particle distribution derived from ocean color optical properties were coupled with numerical circulation
models to determine a 24 hour forecast of particle concentrations.
A 3-d particle concentration field for the coastal ocean was created by extending the surface satellite bio-optical
properties vertically by parameterzing an expediential Gaussian depth profile. The shape of the vertical particle profile
was constrained by 1) the depth of the 1% light level 2) the mixed layer depth 3) the intensity of the layer stratification
4) and subsurface current field and the surface bio-optical properties. These properties were obtained from MODIS
ocean optical products (phytoplankton absorption and backscattering) and the Intra-America Sea Nowcast Forecast
System - Naval Coastal Ocean Model.
The 3-d particle distribution was imbedded into a 3-d circulation model and the particles advected hourly using forecast
model 3-d current. The particles were diffused, dispersed and differentially settled during the advection processes.
Following the 24 hour advection, the resultant particle distribution were accumulated into 1 km spatial grid and
vertically to a 1 attenuation length (satellite penetration depth) and the forecast ocean color backscattering image
determined. The forecast image was compared with the next day ocean color backscattering image to define the error
budget.
The ocean color particle tracking, defines fine spatial scales processes such as local upwelling and downwelling, which
are essential in understanding the coupling of physical and bio-optical processes. The methods provide new capability
for characterizing how subsurface particles layers change in response to cross and along shelf exchange processes.
Results show methods to forecast satellite optical properties in coastal areas and examine how sequential MODIS
imagery of the particle scattering is related to particle transport and physical processes
Satellite ocean color remote sensing is plagued by loss of coverage due to cloud obscuring, glint contamination, atmospheric
correction failures, and other issues. We have developed a simple and efficient technique for estimating
missing remote sensing data by taking advantage of the inter-pixel spatial and temporal coherency of individual
ocean color products. The technique first employs a limited iterative triangular interpolation procedure. This
procedure attempts to select three neighboring pixels forming the tightest triangle enclosing the data point we
are attempting to recover; and then interpolating. On failure to find three suitable neighbors, a second procedure
is employed which attempts to recover missing data points by using a time dependent "latest pixel" replacement.
This procedure replaces the missing data point with the most recent data point collected at that grid point within
the last seven days. This technique has been applied to MODIS (MODerate resolution Imaging Spectrometer)
ocean color products of phytoplankton absorption, back-scattering coefficient, and chlorophyll concentration to
produce cloud free bio-optical products on a daily basis and provide a new capability for monitoring coastal
processes. We demonstrate a new method on MODIS products and show how bio-optical properties change over
a daily and monthly time scale.
A method to retrieve concentrations of suspended large and small particles in seawater from satellite images is proposed.
The method uses as input images of scattering and backscattering coefficients in several satellite channels as well as an
image of concentration of chlorophyll. All these three properties are derived using an atmospheric correction algorithm
and algorithms to derive inherent optical properties from remote sensing reflectance. The proposed method is based on
several approaches developed previously by Twardowski et al, van de Huist, and Evans and Fournier and is based on
Mie theory. The proposed method was applied to restore a number of suspended particles and their dynamics in ocean
using SeaWIFs satellite optical images.
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