Functional approaches for sparse spatiotemporal satellite data

Claire Miller (University of Glasgow)

Frank Adams 2,

Developments in satellite retrieval algorithms continually extend the extraordinary potential of satellite platforms such as the MEdium ReSolution Imaging Spectrometer (MERIS) and the Advanced Along-Track Scanning Radiometer (AATSR) to retrieve information across the Earth at finer spatial resolution. For example, resolution down to 300m for MERIS now enables water quality products (and associated retrieval accuracy) to be produced for lakes, and Sentinel 2A & 2B (launched in June 2015 and March 2017) will enable quantitative retrieval with resolutions down to around 10-60m. 

Challenges associated with these new environmental data streams are the large volumes of data in space and time, collected as images, and the often large quantities of missing data. Functional data analysis provides an attractive approach to investigating spatiotemporal lake images providing efficient dimensionality reduction and clustering. However, novel developments to functional approaches are required to account for complex uncertainties and sparseness. Specifically, we propose functional Bayesian downscaling for calibrating remotely sensed data using in-situ measurements, adaptive penalties to account for quantified uncertainty, and spatiotemporal mixed model functional PCA to reduce dimensionality and provide imputations for missing data. 

This presentation will provide an overview of these statistical developments resulting from this ambitious and cutting edge project to identify coherence and drivers of lake ecosystem health at a global scale. Applications will use data from the Advanced Along-Track Scanning Radiometer (AATSR) and MERIS instruments on the European Space Agency satellite platform, which have been used to estimate the lake surface water temperature (LSWT) and ecological properties such as chlorophyll (as an indicator of water quality) for lakes in the projects ARC Lake ( and GloboLakes.

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