Computational approaches for sub-meter ocean color remote sensing
type: CreativeWork
description: The satellite ocean color remote sensing paradigm developed by government space agencies enables 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 essential for specific ocean color applications (e.g., mapping algal biomass in the marginal ice zone). While many aspects of the satellite ocean color remote sensing paradigm are applicable to sub-meter scales, steps within the paradigm must be adapted to the optical character of the ocean at these scales and the opto-electronics of the available sensing instruments. This dissertation adapts the three steps of the satellite ocean color remote sensing paradigm that benefit the most from reassessment at sub-meter scales, namely the correction for surface-reflected light, the design and selection of the opto-electronics and the post-processing of over-sampled regions. First, I identify which surface-reflected light removal algorithm and view angle combination are optimal at sub-meter scales, using data collected during a field deployment to the Martha’s Vineyard Coastal Observatory. I find that of the three most widely used glint correction algorithms, a spectral optimization based approach applied to measurements with a 40∘ view angle best recovers the remotesensing reflectance and chlorophyll concentration despite centimeter scale variability in the surface-reflected light. Second, I develop a simulation framework to assess the impact of higher optical and electronics noise on ocean color product retrieval from unique ocean color scenarios. I demonstrate the framework’s power as a design tool by identifying hardware limitations, and developing potential solutions, for estimating algal biomass from high dynamic range sensing in the marginal ice zone. Third, I investigate a spectral super-resolution technique for application to spatially over-sampled oceanic regions. I determine that this tec…
abstract: The satellite ocean color remote sensing paradigm developed by government space agencies enables 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 essential for specific ocean color applications (e.g., mapping algal biomass in the marginal ice zone). While many aspects of the satellite ocean color remote sensing paradigm are applicable to sub-meter scales, steps within the paradigm must be adapted to the optical character of the ocean at these scales and the opto-electronics of the available sensing instruments. This dissertation adapts the three steps of the satellite ocean color remote sensing paradigm that benefit the most from reassessment at sub-meter scales, namely the correction for surface-reflected light, the design and selection of the opto-electronics and the post-processing of over-sampled regions. First, I identify which surface-reflected light removal algorithm and view angle combination are optimal at sub-meter scales, using data collected during a field deployment to the Martha’s Vineyard Coastal Observatory. I find that of the three most widely used glint correction algorithms, a spectral optimization based approach applied to measurements with a 40∘ view angle best recovers the remotesensing reflectance and chlorophyll concentration despite centimeter scale variability in the surface-reflected light. Second, I develop a simulation framework to assess the impact of higher optical and electronics noise on ocean color product retrieval from unique ocean color scenarios. I demonstrate the framework’s power as a design tool by identifying hardware limitations, and developing potential solutions, for estimating algal biomass from high dynamic range sensing in the marginal ice zone. Third, I investigate a spectral super-resolution technique for application to spatially over-sampled oceanic regions. I determine that this tec…
author: O’Shea, Ryan E.
language: en_US
keywords: Ocean color, Hyperspectral, Remote sensing
date published: 2021-02
date modified: 2026-07-27T22:05:19Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/26475

PropertyValue
property ID: DOI
value: 10.1575/1912/26475