1.
Hengl, Tomislav; Heuvelink, Gerard B. M.; Rossiter, David G.
About regression-kriging: From equations to case studies Journal Article
In: Computers & Geosciences, vol. 33, no. 10, pp. 1301-1315, 2007, ISSN: 0098-3004, (Spatial Analysis).
Abstract | Links | BibTeX | Tags: Environmental predictors, GSTAT, MODIS, Multiple regression, Spatial prediction, SRTM
@article{HENGL20071301,
title = {About regression-kriging: From equations to case studies},
author = {Tomislav Hengl and Gerard B. M. Heuvelink and David G. Rossiter},
url = {https://www.sciencedirect.com/science/article/pii/S0098300407001008},
doi = {https://doi.org/10.1016/j.cageo.2007.05.001},
issn = {0098-3004},
year = {2007},
date = {2007-01-01},
journal = {Computers & Geosciences},
volume = {33},
number = {10},
pages = {1301-1315},
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 land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the 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 land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, 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 software environments, both statistical and GIS.},
note = {Spatial Analysis},
keywords = {Environmental predictors, GSTAT, MODIS, Multiple regression, Spatial prediction, SRTM},
pubstate = {published},
tppubtype = {article}
}
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 land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the 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 land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, 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 software environments, both statistical and GIS.
