Test, Evaluate, and Characterize a Remote-Sensing Algorithm for Optically-Shallow Waters

摘要

The long-term goal of this project is to provide reliable inversions of water column and bottom properties when the hyperspectral inversion algorithm, HOPE, is applied to any coastal area in which the Navy has an interest. The specific objective of the project is to test, and understand, the performance of the Hyperspectral Optimization Process Exemplar (HOPE), designed for derivation of water-column and bottom properties of optically shallow waters from spectral remote sensing. Two efforts were carried out during the course of the research. One was to evaluate the performance of HOPE with independently collected hyperspectral images; another was to search and identify robust, easy-access, optimization software for use by broader communities. For the algorithm test and evaluation, two images (over Lee Stock Island, USA and Moreton Bay, Australia) were selected after discussion with other colleagues and international collaborators. Lee Stock Island (LSI) was imaged with Ocean Phills, while Moreton Bay (MB) was imaged with the CASI system. Before image-derived remote sensing reflectance (Rrs) were fed to HOPE, a pre-process was done to enhance Rrs quality. This pre-process has two aspects: (1) spatial smoothing for wavelengths longer than 600 nm; and (2) white correction for each pixel. After these fine adjustments of the image Rrs, properties of the water column and the bottom (absorption coefficient, particle backscattering coefficient, bottom reflectance, and bathymetry) were derived with the HOPE algorithm from Rrs. The inverted results were further compared with in situ measurements to evaluate the algorithm performance, and the results were reported in a workshop held in February 2009. To overcome barriers with HOPE's Solver optimization code, two additional optimization routines. Levenberg-Marquardt and a routine (B2NLS) based on the Bounded Constrained Least Squares/Nonlinear Equations were tested and evaluated.