Understanding and quantifying the natural processes that occur along coasts are critical components of managing environmental resources and planning and executing coastal operations, from humanitarian relief to military actions. However, the coastal ocean is complicated, with dissolved and suspended matter that hinders water transparency, phytoplankton blooms that can be toxic, and bathymetry and bottom types that vary over spatial scales of tens of meters, all of which affect processes in an area that spans millions of square kilometers. A hyperspectral imager collects the spectrum of the light received from each pixel in an image. For environmental characterization the wavelength range typically spans the visible and shortwave infrared wavelengths, and the spectrum is collected in contiguous spectral intervals 1–10 nanometers wide. This spectral information is exploited to provide significantly more information about vegetation, minerals, and other components in the scene than can be retrieved from panchromatic or even multispectral imagery, which rely primarily on the shape of the object for detection [Goetz et al., 1985]. Such technology can also work over shallow seas. Over the past 2 decades, experiments with hyperspectral imagers on airborne platforms have demonstrated the ability to characterize the coastal environment [Davis et al., 2002, Davis et al. 2006] and produce maps of coastal bathymetry, in-water constituents, and bottom type.