Spatiotemporal Statistical Downscaling for the Fusion of In-lake and Remote Sensing Data

Wilkie, C., Miller, C. , Scott, M. , Simis, S., Groom, S., Hunter, P., Spyrakos, E. and Tyler, A. (2018) Spatiotemporal Statistical Downscaling for the Fusion of In-lake and Remote Sensing Data. In: 33rd International Workshop on Statistical Modelling (IWSM 2018), Bristol, UK, 16-20 Jul 2018, pp. 207-212.

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Abstract

This paper addresses the problem of fusing data from in-lake monitoring programmes with remote sensing data, through statistical downscaling. A Bayesian hierarchical model is developed, in order to fuse the in-lake and remote sensing data using spatially-varying coefficients. The model is applied to an example dataset of log(chlorophyll-a) data for Lake Erie, one of the Great Lakes of North America.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Miller, Dr Claire and Wilkie, Mr Craig and Scott, Professor E Marian
Authors: Wilkie, C., Miller, C., Scott, M., Simis, S., Groom, S., Hunter, P., Spyrakos, E., and Tyler, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in 33rd International Workshop on Statistical Modelling (IWSM 2018): 207-212
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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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