Brandon Casey

Water and bottom properties of a coastal environment derived from Hyperion data measured from the EO-1 spacecraft platform

Hyperion is a hyperspectral sensor on board NASAs 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, waters 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.

Bathymetry of shallow coastal regions derived from space-borne hyperspectral sensor

Hyperion is a hyperspectral sensor on board NASAs EO-1 satellite. Its spatial resolution is about 30 meters with a swath of /spl sim/7 Km. Though Hyperion was not designed for ocean studies, its unique spectral configuration (430 nm-2400 nm with a /spl sim/10 nm step) makes it especially attractive to study the effectiveness of such kind of sensor for observing complex coastal waters. In this study, Hyperion data over two sites of the Florida coasts were acquired, with one focused on the clear Key West waters, and the other focused on the relatively turbid Tampa Bay waters. From both data sets, water properties and bottom bathymetry were simultaneously derived from atmosphere-corrected Hyperion data using a spectral matching technique. More importantly, in the top-to-bottom processing of Hyperion data, there was no use of any a prior or ground truth information. For the Key West site, derived bathymetry and water properties were validated with NAVOCEANO CHARTS (active bathymetric LIDAR system) and field measurements, respectively. It is found that the retrieved depths (in a range of /spl sim/1-20 m) match LIDAR depths very well (/spl sim/15% average error), indicating significant potential of using hyperspectral satellite sensor for efficient and repetitive observation of shallow coastal regions.

Forecasting Coastal Optical Properties using Ocean Color and Coastal Circulation Models

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

Restoring number of suspended particles in ocean using satellite optical images and forecasting particle fields

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.

Automated Validation of Satellite Derived Coastal Optical Products

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 Marthas 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.