Comparison of kriging with external drift and simple linear regression for predicting soil horizon thickness with different sample densities.

By H. Bourennane, D. King, A. Couturier. 2000

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Spatial interpolation methods applied in the environmental sciences: A review (2014, methodology): “KED and linear regression model (LM) were applied for sample sizes of 40, 50, 75, 100, 125 and 150 (Bourennane et al., 2000).”
Quantitative mapping of soil types based on regression kriging of taxonomic distances with landform and land cover attributes (2002, background): “, 1987), and kriging interpolation (Walter, 1990; Bourennane et al., 2000).”
A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors (2011, result): “…in relation to low sample density (1783 km2 per sample) in terms of RRMSE The impacts of sample density on the estimation depend on the spatial interpolation methods (Bourennane et al., 2000; Wang et al., 2005; Hengl, 2007; Li et al., 2007), which is consistent with the findings in this review.”
A generic framework for spatial prediction of soil variables based on regression-kriging (2004, background): “Bourennane et al. (1996, 2000) showed that prediction of horizon thickness is more accurate with the use of a slope map as external drift.”