Identification and classification of vertical chlorophyll patterns in the Benguela upwelling system and Angola-Benguela front using an artificial neural network

Information on the vertical chlorophyll structure in the ocean is important for estimating integrated chlorophyll a and primary production from satellite. For this study, vertical chlorophyll profiles from the Benguela upwelling system and the Angola-Benguela front were collected in winter to identify characteristic profiles. A shifted Gaussian model was fitted to each profile to estimate four parameters that defined the shape of the curve: the background chlorophyll concentration (B0), the height parameter of the peak (h), the width of the peak (&#963) and the depth of the chlorophyll peak (zm). A type of artificial neural network called a self-organizing map (SOM) was then used on these four parameters to identify characteristic profiles. The analysis identified a continuum of chlorophyll patterns, from those with large surface peaks (>10 mg m-3) to those with smaller near-surface peaks