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|Title:||About Regression-Kriging: from Equations to Case Studies|
|Authors:||HENGL TOMISLAV; HEUVELINK GERARD; ROSSITER DAVID|
|Citation:||COMPUTERS & GEOSCIENCES vol. 33 p. 1301-1315|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Type:||Articles in Journals|
|Abstract:||This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as terrain parameters, remote sensing imagery and thematic maps) with Simple kriging of the regression residuals. It is mathematically equivalent to interpolation method variously called “Universal Kriging” (UK) and “Kriging with External Drift” (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use terrain parameters derived from combined SRTM and contourbased digital elevation models and multitemporal enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. Dependent variables considered are two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). A physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different softwares, both statistical and GIS.|
|JRC Institute:||Institute for Environment and Sustainability|
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