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

By Tomislav Hengl, Gerard B. M. Heuvelink, Alfred Stein. 2004

1,074 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/663beaa82473a8dcb30547bf532f741f6903ff4c

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Citations in the corpus

Birnbaum-Saunders spatial regression models: Diagnostics and application to chemical data (2018, positive): “For example, Hengl et al. (2004) reported successful results when regression-Kriging is considered.”
Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network (2009, background): “The main advantage of universal krig- ing is the ability to take into account trends present in the data , in our case soil type ( see Hengl et al .”
Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach (2015, methodology): “RFRK is an extension of RF; and it is very similar to the widely used hybrid prediction technique regression kriging (RK) (Hengl et al., 2004; Odeh et al., 1995; Piccini et al., 2014; Sun et al., 2012; Zhang et al., 2012).”
On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E‐BLUP) with REML (2006, methodology): “A solution sometimes advanced for this problem (e.g. by Hengl et al., 2004) is to use the variogram from the OLS residual to re-estimate the parameters b 0 and b 1 by generalized least squares (GLS).”
Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models (2013, methodology): “However, this can be done separately in two steps with an approach termed regression-kriging (Hengl et al. 2003, 2004).”