This article develops a soil sampling strategy for spatially correlated variables using the tools of geostatistical analysis. With a minimum of equations, the logic of geostatistical analysis is traced from the modeling of a semi-variogram to the output isomaps of pollution estimates and their standard deviations. These algorithms provide a method to balance precision, accuracy, and costs. Their axiomatic assumptions dictate a two-stage sampling strategy. The first stage is a sampling survey using a radial grid to collect enough data to define, by a semi-variogram, the ranges of influence and the orientation of the correlation structure of the pollutant plume. The second stage is a census of the suspected area with grid shape, sizes and orientation dictated by the semi-variogram. The subsequent kriging analysis of this data gives isopleth maps of the pollution field and the standard error isomap of this contouring. (Copyright (c) 1984 by D. Reidel Publishing Company.)