This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.
This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.
In this study, we present Bayesian machine learning approaches to predict the spectral phytoplankton absorption coefficient - a proxy of phytoplankton biomass - from top of atmosphere measurements of ocean color. This presents a significant advance in ocean color research as it permits the bypassing of conventional atmospheric correction, which is notoriously challenging in optically complex waters such as costal seas and inland waters - regions particularly vulnerable to anthropogenic forcing and climate change. Model prediction accuracy is significantly greater than conventional algorithms, and most, importantly, offers a method to derive biogeochemically relevant information from ocean color in scenarios where it may otherwise not be possible.
https://doi.org/10.31223/osf.io/shp6y
Life Sciences, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
machine learning, Atmospheric Correction, Neural Networks, Bayesian, ocean color remote sensing, phytoplankton ecology
Published: 2019-12-04 08:14
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