Considering covariates in the covariance structure of spatial processes

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Second-order non-stationary modeling approaches for univariate geostatistical data (2017, methodology): “Schmidt et al. (2011) propose an approach for integrating covariates in the space deformation model to better estimate the deformation function.”
Modeling Nonstationary Processes Through Dimension Expansion (2010, methodology): “A Bayesian image warping approach which allows covariate effects to be included in the correlation structure has recently been suggested by Schmidt et al. (2011).”
Spatial models with explanatory variables in the dependence structure (2014, methodology): “This approach has been further developed in Schmidt and O’Hagan (2003); Schmidt et al. (2011); Bornn et al. (2012).”
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014, methodology): “Deformation methods obtain a non-stationary model by deformation of a latent space where the process is stationary (Sampson and Guttorp, 1992), and the axes of this latent space can be defined by explanatory variables (Schmidt et al., 2011).”