Remotely sensing phytoplankton size structure in the Red Sea

Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells <2 μm in size (picophytoplankton) and large cells >2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.

Gittings, J. A., Brewin, R. J. W., Raitsos, D. E., Kheireddine, M., Ouhssain, M., Jones, B. H., & Hoteit, I. (2019). Remotely sensing phytoplankton size structure in the Red Sea. Remote Sensing of Environment, 234, 111387. doi:10.1016/j.rse.2019.111387

The authors are grateful to the Ocean Colour CCI team (European Space Agency) for providing and processing the Chl-a dataset. The authors also thank Nick Selmes for processing and providing the Sentinel-3 OLCI Chl-a dataset for the Red Sea. The authors express their gratitude to the scientists, officers and crews of the R/V Thuwal, the KAUST Coastal and Marine Resources Core Lab who provided logistical support and assistance during fieldwork, and the Analytical Core Lab for providing facilities for the analyses of the samples collected during the fieldwork. The authors are grateful to Benjamin Kurten, Susana Carvalho, Sarma Yellepeddi, and Joanne Ellis for facilitating the sampling of optical and pigment samples for the research cruises listed. The authors also thank George Krokos for his useful discussions. This work was funded by the KAUST Office of Sponsored Research (OSR) under the Collaborative Research Grant (CRG) program (Grant # URF/1/2979-01-01) and the Virtual Red Sea Initiative (Grant # REP/1/3268-01-01). Fieldwork was supported by KAUST baseline funding (BAS/1/1032-01-01) attributed to Burton Jones.

Elsevier BV

Remote Sensing of Environment


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