I propose a multiple time series model for data from a network of monitoring stations that have both temporal and spatial correlation. The model includes a separate mean and trend for each monitoring station and obtains spatial estimates of mean and trend by smoothing the observed values over a rectangular grid using a discrete smoothing prior. Smoothing parameters and covariance estimates can be chosen subjectively or selected using indirect generalized cross-validation. The gridded values and their standard errors can be used for several purposes, including inference on regional means or trends and improving monitoring networks via station rearrangement.