A Constrained Optimization Technique For Estimating Environmental Parameters From Czmil Hyperspectral And Lidar Data

We have developed a combined atmospheric-oceanographic spectral optimization solution decomposing measured airborne radiance data from the passive spectrometer into environmental parameters of interest. In this model, we hold depth measurements from the lidar as fixed constraints, thereby gaining a degree of freedom in the solution, and extending the solution into deeper waters than achieved with passive data alone. In this paper, we illustrate results of the data processing procedure and assess the accuracy of estimated IOPs (Inherent Optical Properties) parameters through comparison to in-situ measurements.