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Improving the retrieval of carbon-based phytoplankton biomass from satellite ocean colour observations

TitoloImproving the retrieval of carbon-based phytoplankton biomass from satellite ocean colour observations
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2020
AutoriBellacicco, M., Pitarch J., Organelli E., Martinez-Vicente V., Volpe G., and Marullo S.
RivistaRemote Sensing
Parole chiaveBackscattering, Backscattering coefficients, Biomass, carbon, Chlorophyll-a concentration, Climate change, Ecosystems, Phytoplankton, Phytoplankton biomass, Phytoplankton distributions, Satellite ocean colour observation, Space-based observations, Spatial and temporal variation, Spatio-temporal variation

Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine ecosystem. To observe phytoplankton from space, chlorophyll-a concentration (Chl) has been widely used as a proxy of algal biomass, although it can be impacted by physiology. Therefore, there has been an increasing focus towards estimating phytoplankton biomass in units of carbon (Cphyto ). Here, we developed an algorithm to quantify Cphyto from space-based observations that accounts for the spatio-temporal variations of the backscattering coefficient associated with the fraction of detrital particles that do not covary with Chl. The main findings are: (i) a spatial and temporal variation of the detritus component must be accounted for in the Cphyto algorithm; (ii) the refined Cphyto algorithm performs better (relative bias of 23.7%) than any previously existing model; and (iii) our algorithm shows the lowest error in Cphyto across areas where picophytoplankton dominates (relative bias of 14%). In other areas, it is currently not possible to accurately assess the performance of the refined algorithm due to the paucity of in situ carbon data associated with nano-and micro-phytoplankton size classes. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.


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Citation KeyBellacicco20201