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

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State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality. / Gong, M.; Miller, C.; Scott, M. et al.
In: Stochastic Environmental Research and Risk Assessment, Vol. 35, No. 12, 31.12.2021, p. 2521-2536.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gong, M, Miller, C, Scott, M, O’Donnell, R, Simis, S, Groom, S, 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, vol. 35, no. 12, pp. 2521-2536. https://doi.org/10.1007/s00477-021-02017-w

APA

Gong, M., Miller, C., Scott, M., O’Donnell, R., Simis, S., Groom, S., 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, 35(12), 2521-2536. https://doi.org/10.1007/s00477-021-02017-w

Vancouver

Gong M, Miller C, Scott M, O’Donnell R, Simis S, Groom S et al. State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality. Stochastic Environmental Research and Risk Assessment. 2021 Dec 31;35(12):2521-2536. Epub 2021 Apr 21. doi: 10.1007/s00477-021-02017-w

Author

Gong, M. ; Miller, C. ; Scott, M. et al. / State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality. In: Stochastic Environmental Research and Risk Assessment. 2021 ; Vol. 35, No. 12. pp. 2521-2536.

Bibtex

@article{328e01e20ac04641ba4215abc0d09871,
title = "State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality",
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-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. ",
keywords = "AECM algorithm, Functional principal component analysis, Lake chlorophyll-a, Remote sensing images, State space model, Chlorophyll, Lakes, Remote sensing, Satellites, Space optics, Uncertainty analysis, Water quality, Environmental variables, European Space Agency, Medium resolution imaging spectrometers, Principal Components, Principal components analysis, Satellite remote sensing, Spatiotemporal patterns, Quality control",
author = "M. Gong and C. Miller and M. Scott and R. O{\textquoteright}Donnell and S. Simis and S. Groom and A. Tyler and P. Hunter and E. Spyrakos",
year = "2021",
month = dec,
day = "31",
doi = "10.1007/s00477-021-02017-w",
language = "English",
volume = "35",
pages = "2521--2536",
journal = "Stochastic Environmental Research and Risk Assessment",
issn = "1436-3240",
publisher = "Springer New York",
number = "12",

}

RIS

TY - JOUR

T1 - State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality

AU - Gong, M.

AU - Miller, C.

AU - Scott, M.

AU - O’Donnell, R.

AU - Simis, S.

AU - Groom, S.

AU - Tyler, A.

AU - Hunter, P.

AU - Spyrakos, E.

PY - 2021/12/31

Y1 - 2021/12/31

N2 - 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-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.

AB - 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-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.

KW - AECM algorithm

KW - Functional principal component analysis

KW - Lake chlorophyll-a

KW - Remote sensing images

KW - State space model

KW - Chlorophyll

KW - Lakes

KW - Remote sensing

KW - Satellites

KW - Space optics

KW - Uncertainty analysis

KW - Water quality

KW - Environmental variables

KW - European Space Agency

KW - Medium resolution imaging spectrometers

KW - Principal Components

KW - Principal components analysis

KW - Satellite remote sensing

KW - Spatiotemporal patterns

KW - Quality control

U2 - 10.1007/s00477-021-02017-w

DO - 10.1007/s00477-021-02017-w

M3 - Journal article

VL - 35

SP - 2521

EP - 2536

JO - Stochastic Environmental Research and Risk Assessment

JF - Stochastic Environmental Research and Risk Assessment

SN - 1436-3240

IS - 12

ER -