A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance ͑R rs ͒ spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured R rs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Remote sensing is a valuable tool for rapid identification of benthic features in coastal environments. Past applications have been limited, however, by multispectral models that are typically difficult to apply when bottom types are heterogeneous and complex. We attempt to overcome these limitations by using a spectral library of remote sensing reflectance (R rs ), generated through radiative transfer computations, to classify image pixels according to bottom type and water depth. R rs spectra were calculated for water depths ranging from 0.5 to 20 m at 0.5-to 1.0-m depth intervals using measured reflectance spectra from sediment, seagrass, and pavement bottom types and inherent optical properties of the water. To verify the library, computed upwelling radiance and downwelling irradiance spectra were compared to field measurements obtained with a hyperspectral tethered spectral radiometer buoy (TSRB). Comparisons between simulated spectra and TSRB data showed close matches in signal shape and magnitude. The library classification method was tested on hyperspectral data collected using a portable hyperspectral imager for low light spectroscopy (PHILLS) airborne sensor near Lee Stocking Island, Bahamas. Two hyperspectral images were classified using a minimum-distance method. Comparisons with ground truth data indicate that library classification can be successful at identifying bottom type and water depth information from hyperspectral imagery. With the addition of diverse sediments types and different species of corals, seagrass, and algae, spectral libraries will have the potential to serve as valuable tools for identifying characteristic wavelengths that can be incorporated into bottom classification and bathymetry algorithms.Remote sensing has long been used to analyze terrestrial features, such as soil mineral content, foliage density and type, and surface elevation (Curran et al. 1992; PalaciosOrueta and Ustin 1998;Rollin and Milton 1998;Kokaly and Clark 1999). Satellite and airborne sensors are well suited to terrestrial observations in the visible and infrared range. These sensors are more limited, however, when used over oceans or lakes because of the low reflectance values of deep water (giving relatively poor signal-to-noise ratios) and the complexity of combined water and bottom signals in shallow water (Jerlov 1976). Most applications of marine remote sensing to date have been estimations of phytoplankton biomass and sea surface temperatures (SSTs). In these applications, it is generally assumed that all light from the ocean is either spectrally reflected from the upper several meters of the water column or thermally emitted from the first few millimeters at the surface. For biomass and SST applications in shallow water, visible radiation reflected from the bottom Acknowledgments

Microbial communities often produce copious films of extracellular polymeric secretions (EPS) that may interact with sediments to influence spectral reflectance signatures of shallow marine sediments. We examined EPS associated with microbial mats to determine their potential effects on sediment reflectance properties. Distinct changes in spectral reflectance signatures of carbonate sediments from the Bahamas were observed among several sediment sites, which were specifically chosen for their presence of microbial mats and adjacent nonmat sediments. The presence of mats greatly reduced sediment reflectance signatures by ϳ10%-20%, compared with adjacent nonmat areas having similar sediment characteristics. Decreases in reflectance near 444 and 678 nm could be attributed primarily to absorbance by photopigments. However, additional nonspecific decreases in reflectance occurred across a wide spectral range (400-750 nm). Experimental manipulations determined that nonspecific reflectance decreases were due to EPS that are produced by biofilm-associated microorganisms of the mats. Microbial EPS, isolated from natural mat sediments exhibited small but nonspecific absorbances across a broad spectral range. When EPS was in relatively high concentrations, as in microbial mats, there was a ''biofilm gel effect'' on sediment reflectance properties. The effect was twofold. First, it increased the relative spacing of sediment grains, a process that permitted light to penetrate deeper into sediments. Second, it resulted in a more efficient capture of photons because of the change in refractive index of EPS gel itself relative to seawater. The relatively translucent EPS of biofilms, therefore, influenced the magnitude of reflectance across a broad spectral range in marine sediments. Downwelling light, on interaction with carbonate sediments, produces variable upwelling reflectance and scatter- AcknowledgmentsWe thank the staff and scientists of the Caribbean Marine Research Center at Lee Stocking Island, Bahamas, for use of their facilities and for support in carrying out field work. We thank Lisa Drake (Old Dominion University) for help in collecting samples. Finally, we acknowledge the very helpful comments of anonymous reviewers who greatly improved the quality of the manuscript.

This study uses derivative spectroscopy to assess qualitative and quantitative information regarding seafloor types that can be extracted from hyperspectral remote sensing reflectance signals. Carbonate sediments with variable concentrations of microbial pigments were used as a model system. Reflectance signals measured directly over sediment bottoms were compared with remotely sensed data from the same sites collected using an airborne sensor. Absorption features associated with accessory pigments in the sediments were lost to the water column. However major sediment pigments, chlorophyll a and fucoxanthin, were identified in the remote sensing spectra and showed quantitative correlation with sediment pigment concentrations. Derivative spectra were also used to create a simple bathymetric algorithm. Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 2002 Optical Society of America