Phytoplankton community structure determined through remote sensing and in situ optical measurements

Doctoral Thesis


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University of Cape Town

Linking variability in optical signals with phytoplankton community characteristics is important to extend the use of the vast resource that is the satellite ocean colour archive. Detection of species, functional types or size classes has been addressed through a spectrum of empirical to analytical approaches. A key step in developing these techniques is quantifying the sensitivity in reflectance, which can be attributed to phytoplankton characteristics (e.g cell size) under different optical regimes. Ultimately, highly spatially and temporally resolved information on phytoplankton characteristics can help the global scientific community to answer important questions relating to primary ecosystem variability. In the southern Benguela, Harmful Algal Blooms threaten public health and the economic viability of fishery and aquaculture industries in the region. Concurrently, the dominance of phytoplankton biomass amongst optically significant constituents in the southern Benguela makes the region ideal for assessing the extent to which phytoplankton characteristics beyond biomass can influence the ocean colour signal. A forward and inverse approach is presented. Phytoplankton absorption and back scattering are generated from a phytoplankton particle population model coupled to two radiative transfer approaches: a reflectance approximation and the radiative transfer model, EcoLight-S. Non-linear optimisation inversion schemes are then implemented. A simulated dataset is created to investigate how much variability in reflectance can be associated with changes in phytoplankton cell size in different bio-optical water types. This dataset is inverted to investigate the errors inherent in the inversion process as a result of ambiguity. Comparison of the two radiative transfer techniques allows for consideration of the suitability of approximations for bidirection-ality and subsurface propagation. The inversion algorithm is then applied to hyperspectral in situ radiometric data to provide validation and further assessment of errors from all sources. Results indicate that size related sensitivity in reflectance is highly dependent on phytoplank-ton biomass, as determined by the relative phytoplankton contribution to the Inherent Optical Property budget. The algorithm is finally applied to ten years of MERIS data covering the southern Benguela. A time series of biomass and cell size is presented and metrics developed to demonstrate the utility of this approach for identifying previously unobserved interannual variability in Harmful Algal Blooms.

Includes bibliographical references.