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Abstract
Simple and ordinary kriging assume a constant mean and variance of the soil variable of interest. This assumption is often implausible because the mean and/or variance are linked to terrain attributes, parent material or other soil forming factors. In kriging with external drift (KED) nonstationarity in the mean is accounted for by modelling it as a linear combination of covariates. In this study, we applied an extension of KED that also accounts for nonstationary variance. Similar to the mean, the variance is modelled as a linear combination of covariates. The set of covariates for the mean may differ from the set for the variance. The best combinations of covariates for the mean and variance are selected using Akaike's information criterion. Model parameters of the selected model are then estimated by differential evolution using the Restricted Maximum Likelihood (REML) in the objective function. The methodology was tested in a small area of the Hunter Valley, NSW Australia, where samples from a fine grid with gamma K measurements were treated as measurements of the variable of interest. Terrain attributes were used as covariates. Both a nonstationary variance and a stationary variance model were calibrated. The mean squared prediction errors of the two models were somewhat comparable. However, the uncertainty about the predictions was much better quantified by the nonstationary variance model, as indicated by the mean and median of the standardized squared prediction error and by accuracy plots. We conclude that the nonstationary variance model is more flexible and better suited for uncertainty quantification of a mapped soil property. However, parameter estimation of the nonstationary variance model requires more attention due to possible singularity of the covariance matrix.
Original language  English 

Pages (fromto)  138147 
Number of pages  10 
Journal  Geoderma 
Volume  324 
DOIs  
Publication status  Published  15 Aug 2018 
Keywords
 Geostatistics
 Kriging
 Nonstationarity
 Pedometrics
 REML
 Uncertainty assessment
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Projects
 1 Finished

QUICS: Quantifying Uncertainty in Integrated Catchment Studies
1/06/14 → 31/05/18
Project: EU research project