Spatio-temporal Modelling of Remote-sensing Lake Surface Water Temperature Data

Gong, M., Miller, C. and Scott, M. (2018) Spatio-temporal Modelling of Remote-sensing Lake Surface Water Temperature Data. In: 33rd International Workshop on Statistical Modelling (IWSM 2018), Bristol, UK, 16-20 Jul 2018, pp. 106-111.

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Remote-sensing technology is widely used in environmental monitoring. The coverage and resolution of satellite based data provide scientists with great opportunities to study and understand environmental change. However, the large volume and the missing observations in the remote-sensing data present challenges to statistical analysis. This paper investigates two approaches to the spatio-temporal modelling of remote-sensing lake surface water temperature data. Both methods use the state space framework, but with different parameterizations to reflect different aspects of the problem. The appropriateness of the methods for identifying spatial/temporal patterns in the data is discussed.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Scott, Professor Marian and Gong, Miss Mengyi and Miller, Professor Claire
Authors: Gong, M., Miller, C., and Scott, M.
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): 106-111
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