Computational Approaches for Sub-Meter Ocean Color Remote Sensing

Ryan O’Shea, Ph.D., 2021
Samuel Laney, Advisor
The satellite ocean color remote sensing paradigm developed by government space agencies enabled the assessment of ocean color products on global scales at kilometer resolutions. A similar paradigm has not yet been developed for regional scales at sub-meter resolutions, but it is required for specific ocean color applications. This dissertation adapts the satellite ocean color remote-sensing paradigm to the sub-meter scale by addressing changes in the optical character of the ocean and opto-electronics of the sensing instruments. First, the optimal surface reflected light removal algorithm and view-angle combination are determined for centimeter scale fluctuations induced by capillary waves. Second, a simulation framework is developed to assess the impact of higher optical and electronics noise on ocean color product retrieval from unique ocean color scenarios. Third, a spectral super-resolution technique is developed to provide increased spectral resolution from spatially over-sampled regions, despite environmental and optical noise characteristic of the ocean’s color. Overall, the proposed sub-meter ocean color remote sensing paradigm enables researchers to collect high fidelity sub-meter data from imaging spectrometers in unique ocean color scenarios, despite changes in the optical character of the ocean and opto-electronics of the sensing instruments.