A generic framework for spatial prediction of soil variables based on regression-kriging

• Published in: Geoderma Vol. 120; no. 1; pp. 75 - 93
• Main Authors: Hengl, Tomislav, Heuvelink, Gerard B.M., Stein, Alfred
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-05-2004; Elsevier
• Subjects: Areal geology. Maps; Earth sciences; Earth, ocean, space; Environmental correlation; Exact sciences and technology; Factor analysis; Geologic maps, cartography; Logit transformation; Soils; Spatial prediction; Surficial geology; Visualisation; Southern Europe; Croatia; Visualisation; Spatial prediction; Factor analysis; Environmental correlation; Logit transformation; algorithms; maps; thickness; Europe; kriging; regression analysis; digital elevation models; geographic information systems; spatial distribution; factor analysis; interpolation; semivariograms; plains; soil profiles; prediction; relief; soils
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2003.08.018

A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets.