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 presence of monitoring networks for soil variables 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.

在环境科学中，观察涉及多个相关变量的时空多个数据是非常普遍的，这些数据在监视的空间域中随时间测量。在多元地统计学中，对其行为的评估通常基于时空多元协方差结构的知识。由于最后一个通常是未知的，因此必须对其进行估计和建模。本文提出了三个相关环境指标的时空多变量分析，其中包括10厘米土壤温度，最低和最高气温。这项研究因其对生态学的反思以及由于存在以不同时空细节水平为特征的土壤变量和空气变量监测网络的存在而缺乏信息而特别受到关注。