Ensemble neural network methods for satellite-derived estimation of chlorophyll α

In this paper, neural network-based methods incorporating ensemble learning techniques are presented that estimate chlorophyll α (chl α) concentration in the coastal waters of the Gulf of Maine (GOM). A dataset was constructed consisting of in situ chl measurements from the GOM matched with satellite data from the sea-viewing wide-field-of-view sensor (SeaWiFS). These data were used to develop models using diverse neural network ensembles for estimation of chl α concentration from satellite-retrieved ocean reflectances. Results indicate that the models are able to generalize across geographical and temporal variation, and are resilient to uncertainty such as that introduced by poor atmospheric correction, or radiance contributions from non-chl α components in case 2 waters.