About regression-kriging: From equations to case studies

By Tomislav Hengl, Gerard B. M. Heuvelink, David G. Rossiter. Computational Geosciences, 2007

956 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/f5022ac39add9cf2e0af1f8858376faa1301599b

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Citations in the corpus

Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types (2014, methodology): “Regression kriging, yet another estimation procedure (Hengl et al., 2007; Pebesma, 2004; Tadić Perčec, 2010), uses an iterative procedure and should be similarly robust to predictor choice like the likelihood-based estimation used in our study.”
Competition, predation, and migration: Individual choice patterns of Serengeti migrants captured by hierarchical models (2014, methodology): “Because the concentration of nitrogen in the grass is inversely correlated with the mean NDVI (see Appendix C), we regression kriged (Hengl et al. 2007, Bivand et al. 2008) the data from the 148 points with a 9-year mean NDVI layer (2000–2009) to generate an accurate estimate of the spatial…”
SoilGrids1km — Global Soil Information Based on Automated Mapping (2014, methodology): “As a general framework for mapping soil properties and classes we use the regression-kriging method commonly used in geostatistical mapping of soil properties [32,33,38].”
Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods (2010, methodology): “The main idea of SKlm (some authors refer to it as regression kriging, e.g. Hengl et al., 2007) is to replace the known stationary mean in the simple kriging estimate by known varying local means, which can be derived from the secondary variable (Goovaerts, 1997).”
Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach (2015, methodology): “Many studies (Hengl et al., 2007; López-Granados et al., 2005; Odeh et al., 1995; Simbahan et al., 2006; Zhang et al., 2012) have shown that the hybrid prediction methods, such as RK, that account for the spatial structure of the observed data and the environmental correlation usually outperform the”
Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions (2015, background): “A common framework for generating spatial predictions with soil data is the regression-kriging (RK) framework [32, 33].”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “This boils down to “Regression Kriging” (RK), “Universal Kriging”150 (UK) and/or “Kriging with External Drift” (KED) (Goldberger, 1962; Goovaerts, 1997; Christensen,151 2001; Hengl et al., 2007a).”
Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom (2009, neutral): “Variograms estimated from OLS residuals are recognized to be biased and GLS has been proposed as a solution (e.g. Hengl et al., 2007); Cressie (1993) that provides an in-depth discussion about this topic.”
Surface modelling of soil properties based on land use information (2011, neutral): “, 2003), universal kriging (Wackernagel, 1998; Goovaerts, 1999) and regression-kriging (Odeh et al., 1995; Hengl et al., 2004; Hengl et al., 2007).”