Please cite this article as: C. Cappello, S. De Iaco, M. Palma et al., Spatio-temporal modeling of an environ- mental trivariate vector combining air and soil measurements from Ireland. Spatial Statistics (2020) 100455, https://doi.org/10.1016/j.spasta.2020.100455. Spatial Statistics xxx (xxxx) xxx Contents lists available at ScienceDirect Spatial Statistics journal homepage: www.elsevier.com/locate/spasta Spatio-temporal modeling of an environmental trivariate vector combining air and soil measurements from Ireland C. Cappello, S. De Iaco ∗ , M. Palma, D. Pellegrino DES-Sect. of Mathematics and Statistics, University of Salento, Italy article info Article history: Received 26 November 2019 Received in revised form 22 April 2020 Accepted 4 June 2020 Available online xxxx Keywords: Soil variables Air variables Space–time coregionalization model Fitting procedure abstract 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 mul- tivariate 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, mini- mum 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. © 2020 Elsevier B.V. All rights reserved. 1. Introduction In various scientific fields, such as Hydrology, Meteorology, Ecology, Geology and Climate Change, new techniques for handling multivariate spatial or spatio-temporal data are in great demand. In this context, multivariate Geostatistics plays a significant role since it provides tools to analyze primary and secondary variables, for a more accurate description and interpolation of ∗ Corresponding author. E-mail addresses: claudia.cappello@unisalento.it (C. Cappello), sandra.deiaco@unisalento.it (S. De Iaco), monica.palma@unisalento.it (M. Palma), daniela.pellegrino@unisalento.it (D. Pellegrino). https://doi.org/10.1016/j.spasta.2020.100455 2211-6753/© 2020 Elsevier B.V. All rights reserved.