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Impact Of Multi-Scale Predictor Selection For Modeling Soil Properties, Bradley A. Miller, Sylvia Koszinski, Marc Wehrhan, Michael Sommer

Bradley A Miller

Applying a data mining tool used regularly in digital soil mapping, this research focuses on the optimal inclusion of predictors for soil–landscape modeling by utilizing as wide of a pool of variables as possible. Predictor variables for digital soil mapping are often chosen on the basis of data availability and the researcher's expert knowledge. Predictor variables commonly overlooked include alternative analysis scales for land-surface derivatives and additional remote sensing products. For this study, a pool of 412 potential predictors was assembled, which included qualitative location classes, elevation, land-surface derivatives (with a wide range of analysis scales), hydrologic indicators, as well …