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.