Abstract

This work presents a novel approach that integrates a shallow water semi-analytical (SSA) model and a genetic algorithm (GA) to retrieve water column inherent optical properties (IOPs) and identify bottom types simultaneously from measurement of subsurface remote sensing reflectance. This GA–SSA approach is designed based on the assumption that each pixel is homogeneous with regard to the bottom type when viewed at small (centimeter) scales, and it is validated against a synthetic data set ($N=11,250$) that consists of five types of bottom, three levels of bottom depth, 15 concentrations of $\text{chlorophyll-}a$ ($\text{Chl-}a$), and a wide range of modeled IOP variations in clear and optically complex waters representing the coral reef environment. The results indicate that the GA–SSA approach is accurate and robust in the retrieval of IOPs and its success rate in identifying the real bottom type is limited by the level of $\text{Chl-}a$ and bottom depth. When a pixel is homogeneous at a small scale, the maximum allowable concentrations for GA–SSA to perfectly identify all the five bottom types are $0.7\mathrm{mg}/{\mathrm{m}}^3$ at 5 m bottom depth, $0.2\mathrm{mg}/{\mathrm{m}}^3$ at 10 m, and $0.07\mathrm{mg}/{\mathrm{m}}^3$ at 15 m. A promising 80% recognition rate of the benthic community is possible with GA–SSA when an underwater hyperspectral imager is deployed to examine the health status of coral reefs in a clean ($\text{Chl-}a<1\mathrm{mg}/{\mathrm{m}}^3$) and shallow ($\text{bottom depth}<10\mathrm{m}$) environment. Further study that collects field data for direct validation is required to ensure that the GA–SSA approach is also applicable in real coral reef regions.

© 2014 Optical Society of America

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