Spatial prediction of soil properties using EBLUP with the Matérn covariance function

By Budiman Minasny, Alex B. McBratney. 2007

241 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1737fd514e9752e98b677f0d9b24689e5012be49

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Citations in the corpus

Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation (2011, methodology): “The second approach uses REML with details given in Lark and Cullis (2004) and Minasny and McBratney (2007).”
Mapping the mean annual precipitation of China using local interpolation techniques (2014, methodology): “LRK is a derivative of the RK method (Odeh et al. 1995; Minasny and McBratney 2007).”
Spatial interpolation methods applied in the environmental sciences: A review (2014, background): “Therefore, residual maximum likelihoodempirical best linear unbiased predictor (REML-EBLUP) was recommended over various RK types unless datasets are very large because REML-EBLUP is applicable only when the sample size is small (<200) (Minasny and McBratney, 2007).”
Mapping soil salinity in the Yangtze delta: REML and universal kriging (E-BLUP) revisited (2015, unknown): “We separated the two sets of effects and estimated their parameters by residual maximum likelihood (REML), now recognized as best practice (Lark et al., 2006; Minasny and McBratney, 2007).”
Improving the Prediction Accuracy of Soil Mapping through Geostatistics (2012, unknown): “Spatial prediction and surface modeling of soil properties has become a common topic in soil science research [8,9].”
Using model averaging to combine soil property rasters from legacy soil maps and from point data (2014, unknown): “For example Minasny and McBratney (2007) in comparing REML-EBLUP, universal kriging, and ordinary kriging spatial predictionmethods formapping soil pH (0–10 cm) across the Lower Hunter Valley, Australia found a RMSE of 0.674, 0.682, and 0.690 for each respective method from an independent…”
Kriging a soil variable with a simple nonstationary variance model (2009, methodology): “The approach has been taken up by statisticians (Welham et al. 2004) and environmental scientists (e.g., Minasny and McBratney 2007 ).”
A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors (2011, background): “The significant impact of CV observed in this review supports the view that improvements in prediction do not rely on more sophisticated methods, but rather on gathering more useful and high quality data (Minasny and McBratney, 2007).”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “…is now used in a43 variety of fields, ranging from physical geography (Oliver and Webster, 1990), geology and soil science44 (Goovaerts, 1999; Minasny and McBratney, 2007), hydrology (Skøien et al., 2005), epidemiology (Moore45 and Carpenter, 1999; Graham et al., 2004), natural hazard…”
Multi-criteria characterization of recent digital soil mapping and modeling approaches (2009, methodology): “Vasques et al. (2008) compared five different methods to predict SOC using VNIR spectra and Minasny and McBratney (2007b) compared four different methods to predict clay, cotton yield, pH and Zn.”
A Practical Guide to Geostatistical Mapping (2009, methodology): “REML approach), we are able to produce more realistic models, the quality of the output maps depends much more on the quality of input data (Minasny and McBratney, 2007).”