the number of sampling (equivalently Euclidean grid locations in the independent variable space) locations employed, with a large number of grid locations leading to large-scale state-space models that are difficult to manage. An alternative approach to modeling spatiotemporal functional evolution relies on modeling the correlation between any two sampling locations through a smooth covariance kernel [@cressie2011statistics]. The model of the evolution is then formed through a linear, weighted combination of the kernels, and the hyperparameters of the spatiotemporal covariance kernel and the weights are learned by solving an optimization problem. The power and flexibility of this approach lies in the fact that kernels can be defined over abstract objects, and not just Euclidean grid locations, leading to a modeling technique that is domain agnostic. For example, kernel embeddings are available for graphical models studied in decentralized control [@johansson2014global], images [@ren2012coupled], and many other domains. However, formulating control-usable kernel-based models of spatiotemporal phenomena can be challenging due to the need to take into account the spatiotemporal dependence. Many recent techniques in spatiotemporal modeling have focused on covariance kernel design and associated hyperparameter learning algorithms [@garg2012AAAI; @ma2003nonstationary; @RasmussenWilliams2005; @plagemann2008nonstationary]. The main benefit of careful design of covariance kernels over approaches that simply 