A probabilistic sensitivity analysis of water-leaving radiance to water constituents in coastal shallow waters

Julio Martin Duarte; Miguel Velez-Reyes; Stefano Tarantola; Fernando Gilbes; Roy Armstrong

doi:10.1117/12.507808

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5 November 2003 A probabilistic sensitivity analysis of water-leaving radiance to water constituents in coastal shallow waters

Julio Martin Duarte, Miguel Velez-Reyes, Stefano Tarantola, Fernando Gilbes, Roy Armstrong

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Proceedings Volume 5155, Ocean Remote Sensing and Imaging II; (2003) https://doi.org/10.1117/12.507808
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

An important step to determine whether certain coastal environment parameters can be estimated from remote sensing measurements is to establish their identifiability from the water leaving remote sensing reflectance. This work addresses the sensitivity analysis of water leaving remote sensing reflectance to water constituents. The model used in the sensitivity analysis is Hydrolight, a radiative transfer code for ocean waters. We use the Morris factor screening method to determine which parameters have a substantial influence on the remote sensing reflectance. From Morris results, we realize a more precise sensitivity analysis focusing on the most influential parameters, using variance decomposition (Sobol method). As an important example of application, we perform a sensitivity analysis of coral reefs in coastal shallow waters, where the concentrations of Chlorophyll, yellow substance and suspended sediments were limited to a feasible range of variability. The results of the sensitivity analysis lead us to the formulation of band relationships for the estimation of water depth and seabed reflectance in coral reefs.

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Julio Martin Duarte, Miguel Velez-Reyes, Stefano Tarantola, Fernando Gilbes, and Roy Armstrong "A probabilistic sensitivity analysis of water-leaving radiance to water constituents in coastal shallow waters", Proc. SPIE 5155, Ocean Remote Sensing and Imaging II, (5 November 2003); https://doi.org/10.1117/12.507808

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