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State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality

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Title: State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality
Authors: Mengyi Gong, Claire Miller, Marian Scott, Ruth O’Donnell, Stefan Simis, Steve Groom, Andrew Tyler, Peter Hunter, Evangelos Spyrakos
Contributors: NERC Natural Environment Research Council, Lancaster University, University of Glasgow, Plymouth Marine Laboratory, Biological and Environmental Sciences, orcid:0000-0001-7655-1675, orcid:0000-0003-0604-5827, orcid:0000-0001-7269-795X
Source: Stochastic Environmental Research and Risk Assessment. 35:2521-2536
Publisher Information: Springer Science and Business Media LLC, 2021.
Publication Year: 2021
Subject Terms: Functional principal component analysis, 15. Life on land, 01 natural sciences, 6. Clean water, Lake chlorophyll-a, 13. Climate action, 14. Life underwater, 0101 mathematics, Remote sensing images, State space model, AECM algorithm, 0105 earth and related environmental sciences
Description: Satellite remote sensing can provide indicative measures of environmental variables that are crucial to understanding the environment. The spatial and temporal coverage of satellite images allows scientists to investigate the changes in environmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is challenging due to the complexity of the data, which can be large in volume yet sparse within individual images. This paper proposes a new approach, state space functional principal components analysis (SS-FPCA), to identify the spatiotemporal patterns in processed satellite retrievals and simultaneously reduce the dimensionality of the data, through the use of functional principal components. Furthermore our approach can be used to produce interpolations over the sparse areas. An algorithm based on the alternating expectation–conditional maximisation framework is proposed to estimate the model. The uncertainty of the estimated parameters is investigated through a parametric bootstrap procedure. Lake chlorophyll-adata hold key information on water quality status. Such information is usually only available from limited in situ sampling locations or not at all for remote inaccessible lakes. In this paper, the SS-FPCA is used to investigate the spatiotemporal patterns in chlorophyll-adata of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectrometer.
Document Type: Article
File Description: application/pdf
Language: English
ISSN: 1436-3259
1436-3240
DOI: 10.1007/s00477-021-02017-w
Access URL: https://link.springer.com/content/pdf/10.1007/s00477-021-02017-w.pdf
https://dspace.stir.ac.uk/handle/1893/32564
https://link.springer.com/article/10.1007/s00477-021-02017-w
https://eprints.lancs.ac.uk/id/eprint/154876/
https://dspace.stir.ac.uk/bitstream/1893/32564/1/Gong2021_Article_StateSpaceFunctionalPrincipalC.pdf
https://link.springer.com/content/pdf/10.1007/s00477-021-02017-w.pdf
http://eprints.gla.ac.uk/238340/
Rights: CC BY
Accession Number: edsair.doi.dedup.....1830748c0e24ae7d98cdb8e0d115bc54
Database: OpenAIRE
Description
Abstract:Satellite remote sensing can provide indicative measures of environmental variables that are crucial to understanding the environment. The spatial and temporal coverage of satellite images allows scientists to investigate the changes in environmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is challenging due to the complexity of the data, which can be large in volume yet sparse within individual images. This paper proposes a new approach, state space functional principal components analysis (SS-FPCA), to identify the spatiotemporal patterns in processed satellite retrievals and simultaneously reduce the dimensionality of the data, through the use of functional principal components. Furthermore our approach can be used to produce interpolations over the sparse areas. An algorithm based on the alternating expectation–conditional maximisation framework is proposed to estimate the model. The uncertainty of the estimated parameters is investigated through a parametric bootstrap procedure. Lake chlorophyll-adata hold key information on water quality status. Such information is usually only available from limited in situ sampling locations or not at all for remote inaccessible lakes. In this paper, the SS-FPCA is used to investigate the spatiotemporal patterns in chlorophyll-adata of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectrometer.
ISSN:14363259
14363240
DOI:10.1007/s00477-021-02017-w