Bayesian Models for Deriving Biogeochemical Information from Satellite Ocean Color

This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.


Download Preprint


Susanne Elizabeth Craig, Erdem Karaköylü


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.



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

Older Versions

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.