Development and evaluation of a genetic algorithm-based ocean color inversion model for simultaneously retrieving optical properties and bottom types in coral reef regions.

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 chlorophyll-a (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 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  mg/m3 at 5 m bottom depth, 0.2  mg/m3 at 10 m, and 0.07  mg/m3 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 (Chl-a<1  mg/m3) and shallow (bottom depth<10  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.