Optimization of large scales ocean sampling for minimization of the Kriging variance

When monitoring spatial phenomena, we are not just interested in measurements at sensed locations but also at locations where no sensors were placed. To estimate the scalar field where no sensors are deployed, we need to interpolate the data. This paper shows how sampling schemes can be optimized by minimizing the integrated Kriging variance based on known covariance (variogram). We propose two approaches to reduce the computation time of searching for the optimal sampling locations. The proposed method was verified that the computing speed gets faster and faster with the number of sampling increasing by simulation.