Hyperspectral Sensing for Assessing Ne arshore Water Quality Conditions of Hudson/Raritan Estuary

Abstract

This study addresses the use of remote sensing hyperspectral data acquired by NASA/Airborne Visible Infra Red Imaging Spectrometer (AVIRIS) to estimate concentrations of optical water quality parameters in the Hudson/Raritan Estuary of New York-New Jersey. Hudson/Raritan estuary is a complex estuarine system where tidal and wind-driven currents are modified by freshwater discharges from the Hudson, Raritan, Hackensack, and Passaic rivers. Hypersectral (high resolution) remote sensing data offers unique advantages for the study of recurrent hydrological phenomena on regional and local scales. The paper describes the bio-optical water quality model constructed linking the water constituent concentrations to (i) the inherent optical properties (IOPs), using the specific inherent optical properties (SIOP), and (ii) to the subsurface light levels. Generation of accurate reflecta nce R(0) from radiance recorded by the AVIRIS is the key parameter for input into inverse modeling for estimation of constituent concent rations. In conjunction with bio-optical model, the linear matrix inversion technique is used for retrieval of estuarine water constituents in terms of chlorophyll, colored dissolved organic matter and inorganic material. The long term goal is to establish a monitoring/management system for retrieval of estuarine water constituents using remotely sensed data. Such efforts are complementary to the development of spectral library for detec tion/identification of harmful algal blooms (HABs) causing eutrophication and pollutions in the world’s coastal and estuarine waters. Results of AVIRIS data analyses in forms of thematic maps of concentrations of water quality parameters can be integrated into the Geographic Information Systems (GIS) of the estuary. GIS is a computer-based geospatial database which can be used as a management tool for monitoring of water quality conditions of the estuary.

Keywords: arctic sea ice, climate change, North Atlantic Os cillation, principal components analysis, teleconnections