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Remote Sens. 2011, 3(2), 257-269;

Nearshore Water Quality Estimation Using Atmospherically Corrected AVIRIS Data

Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Received: 11 January 2011 / Revised: 8 February 2011 / Accepted: 9 February 2011 / Published: 11 February 2011
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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The objective of the research is to characterize the surface spectral reflectance of the nearshore waters using atmospheric correction code—Tafkaa for retrieval of the marine water constituent concentrations from hyperspectral data. The study area is the nearshore waters of New York/New Jersey considered as a valued ecological, economic and recreational resource within the New York metropolitan area. Comparison of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) measured radiance and in situ reflectance measurement shows the effect of the solar source and atmosphere in the total upwelling spectral radiance measured by AVIRIS. Radiative transfer code, Tafkaa was applied to remove the effects of the atmosphere and to generate accurate reflectance (R(0)) from the AVIRIS radiance for retrieving water quality parameters (i.e., total chlorophyll). Chlorophyll estimation as index of phytoplankton abundance was optimized using AVIRIS band ratio at 675 nm and 702 nm resulting in a coefficient of determination of R2 = 0.98. Use of the radiative transfer code in conjunction with bio optical model is the main tool for using ocean color remote sensing as an operational tool for monitoring of the key nearshore ecological communities of phytoplankton important in global change studies. View Full-Text
Keywords: spectral reflectance; water quality parameters; imaging spectrometer; eutrophication; radiative transfer code spectral reflectance; water quality parameters; imaging spectrometer; eutrophication; radiative transfer code

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Bagheri, S. Nearshore Water Quality Estimation Using Atmospherically Corrected AVIRIS Data. Remote Sens. 2011, 3, 257-269.

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