The timing of phytoplankton growth (phenology) in tropical oceans is a crucial factor influencing the survival rates of higher trophic levels, food web structure and the functioning of coral reef ecosystems. Phytoplankton phenology is thus categorised as an ‘ecosystem indicator’, which can be utilised to assess ecosystem health in response to environmental and climatic perturbations. Ocean-colour remote sensing is currently the only technique providing global, long-term, synoptic estimates of phenology. However, due to limited available in situ datasets, studies dedicated to the validation of satellite-derived phenology metrics are sparse. The recent development of autonomous oceanographic observation platforms provides an opportunity to bridge this gap. Here, we use satellite-derived surface chlorophyll-a (Chl-a) observations, in conjunction with a Biogeochemical-Argo dataset, to assess the capability of remote sensing to estimate phytoplankton phenology metrics in the northern Red Sea – a typical tropical marine ecosystem. We find that phenology metrics derived from both contemporary platforms match with a high degree of precision (within the same 5-day period). The remotely-sensed surface signatures reflect the overall water column dynamics and successfully capture Chl-a variability related to convective mixing. Our findings offer important insights into the capability of remote sensing for monitoring food availability in tropical marine ecosystems, and support the use of satellite-derived phenology as an ecosystem indicator for marine management strategies in regions with limited data availability.
|Original language||English (US)|
|State||Published - Dec 1 2019|
Bibliographical noteFunding Information:
The authors are grateful to the Ocean Colour CCI team (European Space Agency) for providing and processing the Chl-a dataset. We acknowledge Mustapha Ouhssain, Antoine Poteau, Catherine Schmechtig and the Coastal & Marine Resources Core Lab (CMOR) of King Abdullah University of Science and Technology (KAUST) for technical support, the installation of sensors and the deployment of the BGC-Argo float. The authors would also like to thank George Krokos for 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), and the Remotely Sensed Biogeochemical Cycles in the Ocean (remOcean) project, funded by the European Research Council (GA 246777). The BGC-Argo data used in this manuscript were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System.
© 2019, The Author(s).
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