Adaptive sampling and reconnaissance surveys for geostatistical mapping of the soil

The effort required to survey a soil variable depends upon the acceptable uncertainty of estimates and the variogram of the variable. The variogram is unknown prior to sampling, so it must be inferred from a reconnaissance survey before an efficient survey can be designed. The results of reconnaissance surveys are subject to uncertainty, which depends upon the variogram and the number and location of observations. Here, we develop an adaptive approach for optimizing reconnaissance surveys. The observations within these reconnaissance surveys are collected in distinct phases. After each phase, a probability density function of the required sampling density of the main survey is calculated within a Bayesian framework. The number and location of observations within further phases are selected to reduce efficiently the uncertainty of the estimate of the required sampling density. In simulation studies, the number and location of observations in Bayesian adaptive reconnaissance surveys vary according to the variogram of the property of interest. For variograms with a short range, the reconnaissance surveys are intensive with a large proportion of clustered locations. Fewer, more evenly spread locations are required for variables with a long range. Bayesian adaptive reconnaissance surveys lead to more efficient surveys than conventional approaches because the reconnaissance survey is specifically designed for the variable of interest. A hand-held field system is implemented and tested in a survey of soil moisture content over a field.