A Bayesian approach for estimating vertical chlorophyll profiles from satellite remote sensing: proof-of-concept

A proof-of-concept demonstration is presented using a novel method for estimating vertical distributions of chlorophyll a (Chl a) from archives of data from ships, combined with remotely sensed data of sea surface temperature, surface Chl a, and wind (U and V vectors) from satellites. Our study area has contrasting hydrographic regimes that include the dynamic southern Benguela upwelling system and the stratified waters of the Agulhas Bank. Cluster analysis is used to identify “typical ” Chl a profiles from an archive of profiles recorded in 2002–2008. Bayesian networks were then used to relate characteristic profiles to remotely sensed surface features, sub-regions, seasons, and depths. The proposed method could be used to predict daily Chl a profiles for each pixel of a satellite image to estimate biomass and subsurface light fields, and these combined with a light algorithm to model primary production for the Benguela large marine ecosystem