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

Gong, M; Miller, C; Scott, M; O’Donnell, R; Simis, SGH; Groom, SB; Tyler, A; Hunter, P; Spyrakos, E. 2021 State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-021-02017-w

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Official URL: http://dx.doi.org/10.1007/s00477-021-02017-w

Abstract/Summary

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 envi�ronmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is chal�lenging 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�a data 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-a data of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectrometer

Item Type: Publication - Article
Additional Keywords: Functional principal component analysis State space model AECM algorithm Remote sensing images Lake chlorophyll-a
Divisions: Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications
Depositing User: S Hawkins
Date made live: 30 Apr 2021 09:10
Last Modified: 30 Apr 2021 09:10
URI: http://plymsea.ac.uk/id/eprint/9200

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