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Ocean colour remote sensing can provide routine, synoptic and highly cost-effective
observations of biological and biogeochemical response to physical drivers across oceanic
ecosystems, over decadal time scales and at high frequency. In many cases, remotely sensed
data are the only systematic observations available for chronically under-sampled marine
systems (e.g. the polar oceans), and there is thus a need to maximise the value of these
observations by developing ecosystem-appropriate, well characterised products.
Phytoplankton cell size and elemental stoichiometry are physiological traits that have the
potential to impose fundamental constraints on growth rates, food web structure and
biogeochemical cycling of carbon (Finkel et al., 2010). An improved understanding of how
phytoplankton community size structure will respond to climate change is required in order to
improve our understanding of the biological pump and the ability of the ocean to act as a
long-term sink for atmospheric carbon-dioxide (Kohfeld et al., 2005). The significance of
unveiling relationships between optical properties and physiology is that it provides a new
tool for investigating routine, broad-scale changes in algal physiology that will allow insights
into the causative environmental forcings of the observed variability.
A primary focus of this study is on gathering the necessary bio-optical and physiological data
to develop and validate appropriate regional ocean colour algorithms. Derivation of new
algorithms relies upon the acquisition of a large variety of in situ data. This includes biooptical data in the form of IOP (scattering, beam attenuation, absorption) from CTD sensors,
and underway sensors as well as Apparent Optical Properties (AOP) (radiance, irradiance,
reflectance, diffuse attenuation coefficient) from a profiling radiometer (e.g. the new
Biosphericals Compact-Optical Profiling System - C-OPS). Large quantities of in situ
biogeochemical data that accompany the bio-optical data are necessary to characterise the
relationship between IOP, carbon content, size structure and dominant functional types of the
phytoplankton community. In addition, physiological data are required from
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production measurements, and photo-physiological (e.g. FV/Fm) responses to light and / or
Fe-limitation.
The above listed bio-optical, biogeochemical and photo-physiological data will be used to
parametrise the particle field (dominated by the phytoplankton community) through empirical
relationships between IOP and size, pigment and carbon content. This information in
conjunction with radiative transfer models and reflectance inversion algorithms will allow us
to use satellite derived ocean colour data to investigate biological responses (through changes
in biomass, community structure and physiology) to event, seasonal and inter-annual
variability in ecosystem physical drivers at the required spatial and temporal scales. Given the
important relationship between community size and carbon export (Finkel et al., 2010) these