SS3.03 Recent Advances in Coastal and River Plume Remote Sensing
Date: Thursday, June 13, 2002
Time: 3:00:00 PM
Location: View Royal
 
HuC, University of South Florida, St. Petersburg, USA, hu@seas.marine.usf.edu
Odriozola, A, L, University of South Florida, St. Petersburg, USA, luchi@marine.usf.edu
Akl, J, P, University of South Florida, St. Petersburg, USA, jakl@marine.usf.edu
Muller-Karger, F, E, University of South Florida, St. Petersburg, USA, carib@marine.usf.edu
Varela, R, , Fundacion La Salle de Ciencias Naturales, Estacion de Investigaciones Marinas de Margarita, Apartado, Isla de Margarita, Venezuela, 
Astor, Y, , Fundacion La Salle de Ciencias Naturales, Estacion de Investigaciones Marinas de Margarita, Apartado, Isla de Margarita, Venezuela, 
Swarzenski, P, , USGS, St. Petersburg, USA, pswarzen@usgs.gov
Froidefond, J, M, Université Bordeaux-1, TALENCE cedex, France, jm.froidefond@geocean.u-bordeaux.fr
 
Remote sensing algorithms for river plumes: A comparison
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Passive optical remote sensing of river plumes is increasingly recognized as an important tool to study coastal and oceanic processes, yet it still remains a challenge due to the complex nature of the optical properties of the various river constituents, namely dissolved matter, phytoplankton pigments, and suspended particles. Using the reflectance data and water sample data collected from several major river plumes (the Amazon, Orinoco, Mississippi, and Atchafalaya) we test several bio-optical algorithms, from the simple band-ratio empirical, multi-band semi-analytical, to the sophisticated optimization algorithm. Our study covers a wide range of water types from these rivers: up to 55 mg/m3 for chlorophyll concentration, 4 m-1 for Gelbstoff absorption coefficient (400 nm), and 160 mg/L for total suspended sediment (TSS) concentration. Each of these constituents has it's own optical signature, represented by the distinguishable shape in the reflectance spectra. We present and compare the performance of each bio-optical algorithm on these river plume data.