COASTAL BATHYMETRY FROM HYPERSPECTRAL OBSERVATIONS OF WATER RADIANCE

Water depth, bottom reflectance, inherent optical properties of the wa ter column (scattering, absorption, and fluorescence), and illuminatio n conditions combine to determine the upwelling spectral radiance of c oastal waters. If these complex optical relationships could be quantif ied, it would be possible to extract coastal information from spectral radiance data. We use data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in a neural network system to establish quantit ative, empirical relationships between one of these parameters, depth, and remotely sensed spectral radiance. Data are analyzed for two area s: the western coast of Florida in the Tampa Bay area and the Florida Keys between Upper Matecumbe and Plantation Keys. The neural network a pproach results in retrieval of reasonable depths from spectral radian ce in both cases over a depth range of 0 to 6 m. Retrieved depths for Tampa Bay are accurate to a RMS error of 0.84 m relative to depths in the National Ocean Survey (NOS) Hydrographic Database, and the Keys re trievals have an RMS error of 0.39 m relative to a bathymetric survey conducted to support this study. A neural network trained on a combina tion of the two data sets results in a combined RMS error of 0.48 m, n early the same performance as neural networks trained individually. Th e ability of the neural network to generalize, producing algorithms wi th some degree of universality among diverse coastal environments is, thereby, demonstrated. The result of the generalization analysis is of practical importance because it indicates that the neural network may not require an extensive training set of water depth data in order to the ''tuned'' for each location where depth retrievals are desired. W hile empirical the neural network is in some sense a model of the inve rsion of the radiative transfer problem within the marine environment. The neural network approach, therefore, operates on a higher level th an more traditional statistical curve fitting solutions for retrieval of remotely sensed information. Published by Elsevier Science Inc.