| Summary: | The accuracy of estimates of air-sea exchange of carbon dioxide in the polar oceans is currently limited due to critical gaps in our understanding of the relationship and interactions between the air, sea, and ice. Advances in methods to make direct measurements of air-sea carbon dioxide fluxes using tower and ship mounted eddy covariance systems means that it is increasingly possible to collect high quality air-sea carbon dioxide flux observations within regions of variable sea ice coverage. This thesis focusses on examining the current and future use of remote sensing data for characterising sea ice conditions within air-ice-sea flux studies. Chapter 2 critically reviews the results of previously published polar eddy covariance studies in sea ice environments to determine the current state of the art in terms of measurements and our understanding. This identifies where methodological differences may be influencing these findings, and possible future directions for this area of research; this includes the need for the development of ‘best-practice’ methodologies. Improving the use of spatial data and its associated uncertainties, particularly in mixed ice-water environments, is identified as a research priority. In Chapter 3, an analysis framework using published field data and ice data uncertainties identifies that these uncertainties can significantly impact the relationship between sea ice coverage and gas transfer velocity found in the published literature. This work shows that future effort should focus on improved methods of monitoring sea ice heterogeneity in the flux footprint which include fully characterised ice data uncertainties. In response to this, Chapter 4 presents a drone-based method and solution for collecting fine-scale ocean and sea-ice surface observations which includes characterised uncertainties. This is achieved via an easy to use, open-source automated workflow for georectifying individual aerial images taken over water surfaces without the use of fixed ground control; a key ...
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