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Integrating soil map information in modelling the spatial variation of continuous soil properties
Author(s) -
GOOVAERTS P.,
JOURNEL A.G.
Publication year - 1995
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/j.1365-2389.1995.tb01336.x
Subject(s) - variation (astronomy) , soil map , spatial variability , soil science , environmental science , digital soil mapping , geology , hydrology (agriculture) , soil water , geotechnical engineering , mathematics , statistics , physics , astrophysics
Summary This paper presents two indicator algorithms that integrate soil map information into modelling the spatial variation of continuous soil properties: these are simple indicator kriging with varying means and the Markov–Bayes algorithm. Both methods are used to evaluate probabilities for copper and cobalt deficiencies in the Borders Region of Scotland. Results are compared with maps obtained by the polygonal method (Thiessen polygons) and an indicator kriging algorithm that does not use soil map information. Accounting for soil map information is shown to improve delineation of the deficient areas, especially where the sampling is sparse. Test locations are classified as deficient or not so as to minimize an expected cost of mis‐classification that is derived from local probability distributions of copper or cobalt and functions measuring the cost of overestimating or underestimating metal concentrations. The comparison of classification results with actual copper and cobalt concentrations at test locations shows that the two proposed algorithms can decrease substantially the economic loss attached to misclassification.