Ensemble machine learning algorithms for satellite remote sensing of water quality in coastal waters

Marvin Li, Alabama, USA 13-15

Sound management of coastal zone resources requires comprehensive and frequent monitoring of water quality and ecosystem productivity in coastal waters. Satellite remote sensing provides a synoptic view of the ocean surface at daily intervals. Central to ocean-color remote sensing is the development of algorithms that can accurately infer inherent water optical properties from satellite reflectance measurements. However, algorithms developed for the open ocean perform poorly in optically complex coastal waters and estuaries. In this project, I developed new machine learning algorithms that successfully retrieved chlorophyll and suspended sediment concentration, advancing satellite remote sensing of coastal waters.

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