Remote sensing of phytoplankton community composition in the northern Benguela upwelling system - Moloto, Tebatso M.; Thomalla, Sandy J.; Smith, Marie E.; Martin, Bettina; Louw, Deon C.; Koppelmann, Rolf (Frontiers in Marine Science, 2023)
Marine phytoplankton in the northern Benguela upwelling system (nBUS) serve as a food and energy source fuelling marine food webs at higher trophic levels and thereby support a lucrative fisheries industry that sustain local economies in Namibia. Microscopic and chemotaxonomic analyses are among the most commonly used techniques for routine phytoplankton community analysis and monitoring. However, traditional in situ sampling methods have a limited spatiotemporal coverage. Satellite observations far surpass traditional discrete ocean sampling methods in their ability to provide data at broad spatial scales over a range of temporal resolution over decadal time periods. Recognition of phytoplankton ecological and functional differences has compelled advancements in satellite observations over the past decades to go beyond chlorophyll-a (Chl-a) as a proxy for phytoplankton biomass to distinguish phytoplankton taxa from space. In this study, a multispectral remote sensing approach is presented for detection of dominant phytoplankton groups frequently observed in the nBUS. Here, we use a large microscopic dataset of phytoplankton community structure and the Moderate Resolution Imaging Spectroradiometer of aqua satellite match-ups to relate spectral characteristics of in water constituents to dominance of specific phytoplankton groups. The normalised fluorescence line height, red-near infrared as well as the green/green spectral band-ratios were assigned to the dominant phytoplankton groups using statistical thresholds. The ocean colour remote sensing algorithm presented here is the first to identify phytoplankton functional types in the nBUS with far-reaching potential for mapping the phenology of phytoplankton groups on unprecedented spatial and temporal scales towards advanced ecosystem understanding and environmental monitoring.