Atmospheric correction algorithm based on vector radiative transfer modeling for hyperspectral remote sensing of ocean color

Multi-channel remote sensing of ocean color from space has a rich history -- from the past CZCS, to the present SeaWiFS, and to the near-future MODIS. The atmospheric correction algorithms for processing remotely sensed data from these sensors were mainly developed by Howard Gordon at University of Miami. The algorithms were primarily designed for retrieving water leaving radiances in the visible spectral region over clear deep ocean areas. The information about atmospheric aerosols is derived from channels between 0.66 and 0.87 micrometer, where the water leaving radiances are close to zero. The derived aerosol information is extrapolated back to the visible when retrieving water leaving radiances from remotely sensed data. For the turbid coastal environment, the water leaving radiances for channels between 0.66 and 0.87 micrometer may not be close to zero because of back scattering by suspended materials in the water. Under these conditions, the channels are no longer useful for deriving information on atmospheric aerosols. As a result, the algorithms developed for applications to clear ocean waters cannot be easily modified to retrieve water leaving radiances from remote sensing data acquired over the costal environments. We have recently developed a fast and fully functional atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the Coastal Ocean Imaging Spectrometer (COIS). Our algorithm uses lookup tables generated with a vector radiative transfer code developed by Ahmad and Fraser (1982) and a spectral matching technique for the retrieval of water leaving radiances. The information on atmospheric aerosols is estimated using dark channels beyond 0.86 micron. Quite reasonable results were obtained when applying the algorithm to process spectral imaging data acquired over Chesapeake Bay with the NASA JPL Airborne Visible Infrared Imaging Spectrometer (AVIRIS).