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Spatio-temporal statistics in Earth sciences

Conference Paper


Abstract


  • This paper looks at spatio‑temporal statistical modelling through the prism

    of hierarchical statistical modelling, emphasising the role of dynamical temporal

    models of spatial fields. A purely spatial dataset of soil mineralisation is used to

    motivate the Spatial Random Effects statistical model, which is adept at handling

    massive, nonstationary datasets. This is then generalised to a Spatio‑Temporal

    Random Effects model, which is applied to a large global remote‑ sensing dataset of

    aerosol optical depth (from the Multi‑angle Imaging SpectroRadiometer instrument

    on the National Aeronautics and Space Administration’s Terra satellite). This results

    in optimally interpolated and filtered estimates of the true underlying process, along

    with a statistical measure of the estimates’ uncertainties. Finally, the problem of

    combining data from multiple remote‑sensing instruments is discussed; the Spatial

    Random Effects model is adapted to handle this problem.

Publication Date


  • 2012

Citation


  • Cressie, N. A. (2012). Spatio-temporal statistics in Earth sciences. Water Information Research and Development Alliance (WIRADA) Science Symposium Proceedings (pp. 323-329). Australia: CSIRO.

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2789

Start Page


  • 323

End Page


  • 329

Abstract


  • This paper looks at spatio‑temporal statistical modelling through the prism

    of hierarchical statistical modelling, emphasising the role of dynamical temporal

    models of spatial fields. A purely spatial dataset of soil mineralisation is used to

    motivate the Spatial Random Effects statistical model, which is adept at handling

    massive, nonstationary datasets. This is then generalised to a Spatio‑Temporal

    Random Effects model, which is applied to a large global remote‑ sensing dataset of

    aerosol optical depth (from the Multi‑angle Imaging SpectroRadiometer instrument

    on the National Aeronautics and Space Administration’s Terra satellite). This results

    in optimally interpolated and filtered estimates of the true underlying process, along

    with a statistical measure of the estimates’ uncertainties. Finally, the problem of

    combining data from multiple remote‑sensing instruments is discussed; the Spatial

    Random Effects model is adapted to handle this problem.

Publication Date


  • 2012

Citation


  • Cressie, N. A. (2012). Spatio-temporal statistics in Earth sciences. Water Information Research and Development Alliance (WIRADA) Science Symposium Proceedings (pp. 323-329). Australia: CSIRO.

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2789

Start Page


  • 323

End Page


  • 329