Computational approaches for sub-meter ocean color remote sensing

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 remote-sensing 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 technique more accurately represents spectral frequencies beyond the Nyquist and that it can be trained to be invariant to noise sources characteristic of ocean color remote sensing on images with similar statistics as the training dataset. Overall, the developed and critically assessed sub-meter ocean color remote sensing paradigm enables researchers to collect high fidelity sub-meter data from imaging spectrometers in unique ocean color scenarios.