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In shallow waters visible remote sensing systems frequently image the bottom including features, such as grass beds and coral reefs. Resolving the bottom features as viewed through the complex and varying optical properties of the water column is the central problem in coastal remote sensing. This requires hyperspectral imaging. There are three factors to estimate: water depth, bottom reflectance, and water clarity. Results demonstrate one approach to resolve this complexity using the additional information available in hyperspectral data. An example is given using data from the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS). Future directions for processing these data and the implications for the design of future systems for imaging the coastal ocean are discussed.