Observing optically complex oceans in situ and from space : a radiative transfer approach to determining improved algorithms and uncertainties.

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Satellite-borne ocean colour sensors can provide data on the materials suspended in natural waters on spatial scales that are unmatched by any other sensor platform. Higher temporal resolution data can be obtained from optical sensors on moored observatories. The combination of both modes of data collection is important for providing data for models of ecosystems and climate change. However, there are currently serious limitations in the performance of interpretation algorithms for retrieving water quality parameters such as concentrations of chlorophyll and diffuse attenuation coefficients. Standard algorithms have generally been optimised for clear oceanic waters and perform poorly in optically complex waters, where non-algal materials including inorganic particles (e.g. sediments and dusts), coloured dissolved organic materials (CDOM) and even bubbles can have a negative impact on performance. Another major problem is the fact that end-users of remote sensing data do not have easy access to information about the accuracy of remote sensing products. This project will resolve some of the uncertainties affecting interpretation of remote sensing optical signals through a combination of in situ optical measurements and radiative transfer simulations. Particular attention will be paid to developing improved quality in situ absorption measurements that are currently subject to incompletely resolved scattering errors in turbid waters. Methods will be developed for partitioning absorption and scattering coefficients (from in situ and remote sensing data) into components for different material constituents (algal and non-algal). New algorithms will be developed to retrieve CDOM and particulate organic carbon (POC) from in situ and remote sensing optical data. The performance of remote sensing algorithms will be assessed for different water types using in situ data sets and a simulated data set over extended ranges of constituents obtained by radiative transfer modelling. The errors for remote sensing products will be determined and presented as colour-coded maps that will be made available to end-users. This work will result in improved algorithms for analysing remote sensing signals for coastal waters and will contribute to an improved understanding of relationships between biogeophysical processes and climate change factors in the marine environment. Improved interpretation algorithms will result in better quality data products for monitoring agencies to assure compliance with legal obligations and policy makers to make informed decisions with.