POSTER SESSION 1
15:30–17:30, Monday, September 14
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ABSTRACT 1191 | POSTER 69
WATER REFLECTANCE MATCHUPS BETWEEN MULTI-SENSOR SATELLITE DATA AND FIELD AUTONOMOUS MEASUREMENTS IN FRENCH NEARSHORE WATERS
Water reflectance data appropriate for use in satellite validation studies have been derived from hyperspectral field autonomous systems operated in various nearshore environments across France for several years. These HYPERNETS systems combine high-quality radiometric measurements recorded by HYPSTAR sensors and accurate pointing geometry with real-time data processing and extensive quality controls. Several such systems are in operation in contrasted water types including river mouths, estuaries, and coastal lagoons. The optical properties of these sites are highly variable due to rapid variations in the concentrations and composition of suspended particles and coloured dissolved organic substances. The quality control of these measurements is also complex due to the potential contribution of bottom reflectance in optically-shallow waters. The processing and validation of ocean colour satellite data in such coastal water environments is further complicated by variable aerosol types, adjacency effects, and potential bottom contamination of the water-leaving signal.
To address these challenges, the Hypernets_matchups application has been developed and is used here to generate, quality-check, and analyse the great number of quality matchups identified between satellite and field measurements of the derived water reflectance signal (ρw). Results are presented in terms of differences between satellite-derived and field measured ρw, considering different atmospheric corrections algorithms as well as different corrections applied to remove adjacency effects on water pixels identified as optically-deep. Conclusions are provided concerning the validity of existing ocean colour satellite data processors and their limits in complex nearshore environments.
David Doxaran, Laboratoire Océanographie Villefranche CNRS/SU, [email protected]
Denis Pailler, LOV CNRS/SU, [email protected]
Robert Schlegel, LOV CNRS/SU, [email protected]
Kevin Ruddick, REMSEM / RBINS, [email protected]
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