Hayley Evers-King

Dr. Hayley Evers-King

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Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 May 2014 in Optics Express
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Journal Article
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HEHayley Evers-King
Hayley Evers-King
SBStewart Bernard
Stewart Bernard
LRLisl Robertson
Lisl Robertson
TPTrevor A. Probyn
Trevor A. Probyn
Synoptic scale knowledge of the size structure of phytoplankton communities can offer insight in to primary ecosystem diversity and biogeochemical variability from operational to the decadal scales. Accordingly, obtaining estimates of size and other phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure is proposed to determine information about the cell size of phytoplankton communities using Standard size distributions of two layered spheres to derive a full suite of algal inherent optical properties for a coupled radiative transfer model. This new capability allows explicit quantification of the remote sensing reflectance signal attributable to changes in phytoplankton cell size. Inversion of this model reveals regions within the parameter space where ambiguity may limit potential of inversion algorithms. Validation of the algorithm within the Benguela upwelling system using independent data shows promise for ecosystem applications and further investigation of the interaction between phytoplankton functional types and optical signals. The results here suggest that the utility of assemblage related signals in spectral reflectance is highly sensitive to algal biomass, the presence of other absorbing and scattering constituents and the resultant constituent-specific inherent optical property budget. As such, optimal methods for determining phytoplankton size from (in situ or satellite) ocean colour data will likely rely on appropriately spectrally dense and optimised sensors, well characterised measurement errors including those from atmospheric correction, and an ability to appropriately limit ambiguity within the context of regional inherent optical properties.