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Full-Text Articles in Plant Sciences
Impact Of Multi-Scale Predictor Selection For Modeling Soil Properties, Bradley A. Miller, Sylvia Koszinski, Marc Wehrhan, Michael Sommer
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 …
Machine Learning For Predicting Soil Classes In Three Semi-Arid Landscapes, Colby W. Brungard, Janis L. Boettinger, Michael C. Duniway, Skye A. Wills, Thomas C. Edwards Jr.
Machine Learning For Predicting Soil Classes In Three Semi-Arid Landscapes, Colby W. Brungard, Janis L. Boettinger, Michael C. Duniway, Skye A. Wills, Thomas C. Edwards Jr.
Plants, Soils, and Climate Faculty Publications
Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set …