Data Fusion of Remote-sensing and In-lake chlorophylla Data Using Statistical Downscaling

Wilkie, C. J., Scott, E. M. , Miller, C. , Tyler, A. N., Hunter, P. D. and Spyrakos, E. (2015) Data Fusion of Remote-sensing and In-lake chlorophylla Data Using Statistical Downscaling. In: Spatial Statistics 2015: Emerging Patterns, Avignon, France, 9-12 Jun 2015, pp. 123-126. (doi: 10.1016/j.proenv.2015.05.014)

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Chlorophylla is a green pigment, used as an indirect measure of lake water quality. Its strong absorption of blue and red light allows for quantification through satellite images, providing better spatial coverage than traditional in-lake samples. However, grid-cell scale imagery must be calibrated spatially using in-lake point samples, presenting a change-of-support problem. This paper presents a method of statistical downscaling, namely a Bayesian spatially-varying coefficient regression, which assimilates remotely-sensed and in-lake data, resulting in a fully calibrated spatial map of chlorophylla with associated uncertainty measures. The model is applied to a case study dataset from Lake Balaton, Hungary.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Scott, Professor Marian and Miller, Professor Claire and Wilkie, Dr Craig
Authors: Wilkie, C. J., Scott, E. M., Miller, C., Tyler, A. N., Hunter, P. D., and Spyrakos, E.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2015 The Authors
First Published:First published in Procedia Environmental Sciences 26:123-126
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
595861Global Observatory of Lake Responses to Environmental Change (GloboLakes).Claire MillerNatural Environment Research Council (NERC)NE/J022810/1M&S - STATISTICS