The use of satellite measurements in climate studies promises many new scientific insights if
those data can be efficiently exploited. Due to sparseness of daily data sets, there is a need …

Spatio‐temporal statistics is prone to the curse of dimensionality: one manifestation of this is
inversion of the data–covariance matrix, which is not in general feasible for very‐large‐to …

The information content of multivariable spatio‐temporal data depends on the underlying
spatial sampling scheme. The most informative case is represented by the isotopic …

Understanding the usage patterns for bike-sharing systems is essential in terms of
supporting and enhancing operational planning for such schemes. Studies have …

We propose a new Kalman filter algorithm to provide a formal statistical analysis of space–
time data with an autoregressive structure. The Kalman filter technique allows to capture the …

Prediction of particulate matter concentrations is of particular interest in the field of air
pollution control. We focus on the spatio-temporal geostatistical approach to predicting …

The normalized difference vegetation index (NDVI) is an important indicator for evaluating
vegetation change, monitoring land surface fluxes or predicting crop models. Due to the …

Satellite remote sensing can provide indicative measures of environmental variables that
are crucial to understanding the environment. The spatial and temporal coverage of satellite …

Beach profile data sets provide valuable insight into the morphological evolution of sandy
shorelines. However, beach monitoring schemes often show large variability in temporal …

Satellite remote sensing data have become available in meteorology, agriculture, forestry,
geology, regional planning, hydrology or natural environment sciences since several …