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/m3at 5 m bottom depth, 0.2 mg/m3at 10 m, and 0.07 mg/m3at 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.
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Engineering (miscellaneous)
- Electrical and Electronic Engineering