Efficient sampling for geostatistical surveys

A.M.J.C. Wadoux*, B.P. Marchant, R.M. Lark

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)


A geostatistical survey for soil requires rational choices regarding the sampling strategy. If the variogram of the property of interest is known then it is possible to optimize the sampling scheme such that an objective function related to the survey error is minimized. However, the variogram is rarely known prior to sampling. Instead it must be approximated by using either a variogram estimated from a reconnaissance survey or a variogram estimated for the same soil property in similar conditions. For this reason, spatial coverage schemes are often preferred, because they rely on the simple dispersion of sampling units as uniformly as possible, and are similar to those produced by minimizing the kriging variance. If extra sampling locations are added close to those in a spatial coverage scheme then the scheme might be broadly similar to one produced by minimizing the total error (i.e. kriging variance plus the prediction error due to uncertainty in the covariance parameters). We consider the relative merits of these different sampling approaches by comparing their mean total error for different specified random functions. Our results showed the considerable benefit of adding close‐pairs to a spatial coverage scheme, and that optimizing with respect to the total error generally gave a small further advantage. When we consider the example of sampling for geostatistical survey of clay content of the soil, an optimized scheme based on the average of previously reported clay variograms was fairly robust compared to the spatial coverage plus close‐pairs scheme. We conclude that the direct optimization of spatial surveys was only rarely worthwhile. For most cases, it is best to apply a spatial coverage scheme with a proportion of additional sampling locations to provide some closely spaced pairs. Furthermore, our results indicated that the number of observations required for an effective geostatistical survey depend on the variogram parameters.
Original languageEnglish
Pages (from-to)975-989
JournalEuropean Journal of Soil Science
Issue number5
Early online date18 Feb 2019
Publication statusPublished - Sept 2019


  • geostatistics
  • ordinary kriging
  • pedometrics
  • sample size
  • spatial coverage scheme
  • uncertainty assessment


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