Spatiotemporal forecasting (STF) extends traditional time series forecasting or spatial
interpolation problem to space and time dimensions. Here, we review the statistical, physical …

In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …

We present new spatio-temporal geostatistical modelling and interpolation capabilities of the
R package gstat. Various spatio-temporal covariance models have been implemented, such …

This work considers a number of properties of space–time covariance functions and how
these relate to the spatial-temporal interactions of the process. First, it examines how the …

Environmental and geophysical processes such as atmospheric pollutant concentrations,
precipitation fields and surface winds are characterized by spatial and temporal variability. In …

The problem of the optimal combination of rain‐gauge measurements and radar
precipitation estimates has been investigated. A method that attempts to generalize well …

ABSTRACT A global map** data of atmospheric carbon dioxide (CO2) concentrations can
help us to better understand the spatiotemporal variations of CO2 and the driving factors of …

By extending the product and product–sum space-time covariance models, new families are
generated as integrated products and product–sums. These include nonintegrable space …

In recent years, interest has grown in modeling spatio-temporal data generated from
monitoring networks, satellite imaging, and climate models. Under Gaussianity, the …

Groundwater monitoring is fundamental to understanding system dynamics, trends in
storage, and the long‐term sustainability of an aquifer. Water‐level data are the key source …