Validation of Spectral IOP and Particle Characteristics
NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS
Pagination or Media Count:
Providing particle characteristics such as particle size distribution PSD and particle composition np from ocean color data greatly facilitates classifying water types and predicting optical properties of natural waters. This information impacts many Navy warfare areas which depend on active and passive imagery or that require knowledge of particle types and their dispersion. Potential methods to predict particle characteristics using the inherent optical properties IOPs has been proposed Boss et al. 2001 Twardowski et al. 2001. Haltrin and Arnone 2003 put forward an algorithm to separate particle type into large and small particles and terrigenic versus organic source and concentration of particles using remote sensing data using spectral particulate coefficient information. This work however relied heavily on assumptions between the backscattering to scattering ratio and the ability to iterate using scattering efficiencies to derive total particle number given a Junge slope via Twardowski et al. The ultimate goal of the work reported here is to examine these algorithms and their assumptions in order to provide more confidence in the methods used to retrieve in-situ particle and optical properties from either passive or active remote sensing and from either underwater, airborne, or satellite platforms. Knowing the optical and particle properties of the water can improve the predicted performance of active and passive imaging systems and our ability to differentiate possible biological threats in an area. It is the goal of this research to strive to produce such data that will aid in the characterization of the battlespace environment for electro-optical systems deployed by the Navy for Mine Counter Measures, Naval Special Warfare, Intelligence Surveillance and Reconnaissance, and Anti-Submarine Warfare.
- Physical and Dynamic Oceanography
- Hydrology, Limnology and Potamology