Abstract
Linear geostatistics can provide, by kriging from soil and environmental data, local estimates that are unbiased and have minimum and known variance, and are in this sense optimal. The procedure assumes a model, usually that the variables are realizations of random processes and intrinsic sensu Matheron. It can also be used for global estimation. Variograms, or covariance functions, are vital and must themselves be estimated from data. That demands fairly heavy sampling. Classical sampling also provides global estimates. It needs no assumptions about the nature of the variation, but to avoid bias and to estimate the errors sampling must be randomized.
The bases of both techniques are described. The advantages of kriging for global estimation are by no means clear. Environmental scientists should judge the relative merits of the two approaches in relation to their aims and the resources they have for sampling.
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Webster, R. (1993). Dealing with Spatial Variation. In: Schulin, R., Desaules, A., Webster, R., von Steiger, B. (eds) Soil Monitoring. Monte Verità. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-7542-4_23
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DOI: https://doi.org/10.1007/978-3-0348-7542-4_23
Publisher Name: Birkhäuser, Basel
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