Spatio-temporal modeling of an environmental trivariate vector combining air and soil measurements from Ireland

In environmental sciences, it is very common to observe spatio-temporal multiple data concerning several correlated variables which are measured in time over a monitored spatial domain. In multivariate Geostatistics, the evaluation of their behavior is often based on the knowledge of the spatio-temporal multivariate covariance structure. Since this last is often unknown it has to be estimated and modeled. In this paper, a spatio-temporal multivariate analysis of three relevant environmental indicators, which include 10-centimeter soil temperature, minimum and maximum air temperature, is proposed. This study is of particular interest for its reflection in ecology and the lack of information due to the presence of monitoring networks for soil and air variables characterized by different levels of spatial and temporal detail. A space-time linear coregionalization model (ST-LCM) with suitable models for the latent components of the variables under study is selected by using a simple procedure. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a spatio-temporal multivariate analysis of soil and air variables to address their reflection in ecology and the lack of information due to monitoring network limitations. The chosen method is a space-time linear coregionalization model with suitable models for the latent components of the variables under study.