I have data measuring a variable Z say at several locations on a fixed network (each location at a fixed longitude and latitude). values of Z are measured daily at the same time.
At the moment I am employing ordinary Kriging to interpolate values for each day (ie. run the procedure separately for each day). I am using the gstat package in R.
However, when I look at the autocorrelation function of Z at a given location I see values are correlated up to a lag of about a week. Can I use this additional information to improve my spatial interpolation estimates using spatio-temporal Kriging? (Just to be clear, I want to interpolate spatially, but to the same times that the data is measured at.) Or is it the case, that by interpolating to the same times as in the data I am essentially using the one dimensional slice of the space-time variogram that corresponds to zero time lag, which means I am just doing ordinary kriging?