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A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties
Author(s) -
Goovaerts P.
Publication year - 2011
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.1365-2389.2011.01368.x
Subject(s) - kriging , variogram , interpolation (computer graphics) , residual , geostatistics , field (mathematics) , point (geometry) , soil map , discretization , spatial analysis , spatial variability , multivariate interpolation , statistics , soil science , mathematics , geology , algorithm , computer science , geometry , soil water , animation , mathematical analysis , computer graphics (images) , pure mathematics , bilinear interpolation
Information available for mapping continuous soil attributes often includes point field data and choropleth maps such as soil or geology maps that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents two approaches to incorporate both point and areal data in the spatial interpolation of continuous soil attributes. In the first instance, area‐to‐point kriging is used to map the variability within soil units while ensuring the coherence of the prediction so that the average of disaggregated estimates is equal to the original areal datum. The resulting estimates are then used as local means in residual kriging. The second approach proceeds in one step and capitalizes on (i) a general formulation of kriging that allows the combination of both point and areal data through the use of area‐to‐area, area‐to‐point and point‐to‐point covariances in the kriging system, (ii) the availability of Geographical Information Systems (GIS) to discretize polygons of irregular shape and size and (iii) knowledge of the point‐support variogram model that can be inferred directly from point measurements, thereby eliminating the need for deconvolution procedures. The two approaches are illustrated using the geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura. Sensitivity analysis indicates that the new procedures improve prediction over ordinary kriging and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit.