Abstract
Algorithms for estimation of the concentrations of suspended particulate matter (SPM) are being developed in order to fulfill USACE design requirements for the Coastal Zone Mapping and Imaging Lidar (CZMIL). The lidar sensor will be used to characterize bottom depth as well as water column reflectance properties that can be related to SPM concentrations. Because lidar uses an artificial light source (i.e., the laser), it is considered an active system. Additionally, CZMIL will make observations of the water column with a hyperspectral imaging sensor, which acquires images of the magnitude of water leaving radiance at multiple wavelengths. Since the hyperspectral sensor relies solely on ambient light, it is considered a passive system.
Current research includes the testing and validation of published algorithms for estimating SPM from passive spectral data produced by ship-borne, airborne and satellite-based sensors, as well as the development of a new active-passive data fusion algorithm. The new algorithm will combine observations from CZMIL's lidar and hyperspectral sensors. Data which are being collected in the northern Gulf of Mexico as part of an NSF-funded project will be applied to this research. These data come from ship-borne hyperspectral radiometers, as well as in situ SPM and optical observations. These observations will be used to validate the applicability of published SPM algorithms to the project site. This paper explores the relationship between SPM and two optical parameters used in algorithm development, remote sensing reflectance and the backscattering coefficient.