Mapping marine phytoplankton assemblages from a hyperspectral and Artificial Intelligence perspective

Abstract

The aim of this contribution is to demonstrate the feasibility of different processing techniques to identify phytoplankton assemblages when applied to oceanographic hyperspectral data sets (i.e. above surface measurements and vertical profiles). In order to address this issue and validate the proposed techniques, a simulated framework has been used based on the oceanic radiative transfer model Hydrolight-Ecolight 5.0. The potential offered by an unsupervised hierarchical cluster analysis technique and two Artificial Intelligence algorithms (i.e. Particle Swarm Optimization and Case-Based Reasoning) have been explored. Our results confirm their suitability to map phytoplankton's distribution from hyperspectral information given a variety of hypothetical oceanic environments.