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Markov chain geostatistics is a methodology for simulating categoricalfields. Its fundamental model for conditional simulation is the Markov chainrandom field (MCRF) model, and its basic spatial correlation measure is thetransiogram. There are different ways to get transiogram models (i.e.,continuous-lag transiograms) for MCRF simulation based on sample data andexpert knowledge: linear interpolation method, mathematical model joint-fittingmethod, and a mixed method of the former two. Two case studies were conductedto show how simulated results, including optimal prediction maps and simulatedrealization maps, would respond to different sets of transiogram modelsgenerated by the three different transiogram jointing modeling methods. Resultsshow that the three transiogram joint modeling methods are applicable; the MCRFmodel is generally not very sensitive to the transiogram models produced bydifferent methods, especially when sample data are sufficient to generatereliable experimental transiograms; and the differences between overallsimulation accuracies based on different sets of transiogram models are notsignificant. However, some minor classes show obvious improvement in simulationaccuracy when theoretical transiogram models (generated by mathematical modelfitting with expert knowledge) are used for minor classes. In general, thisstudy indicates that the methods for deriving transiogram models fromexperimental transiograms can perform well in conditional simulations ofcategorical soil variables when meaningful experimental transiograms can beestimated. Employing mathematical models for transiogram modeling of minorclasses provides a way to incorporate expert knowledge and improve thesimulation accuracy of minor classes.

Sensitivity Analysis of the MCRF Model to Different Transiogram Joint Modeling Methods for Simulating Categorical Spatial Variables