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7.8 Deriving OCS using spatiotemporal models.7.7 Deriving OCS from soil profile data (the 3D approach).7.6 Predicting OCS from point data (the 2D approach).7.4 Estimation of Bulk Density using a globally-calibrated PTF.7.3 Derivation of OCS and OCD using soil profile data.7.2 Measurement and derivation of soil organic carbon.7 Spatial prediction and assessment of Soil Organic Carbon.6.2.5 Spatial prediction of binomial variables.6.2.4 Spatial prediction 2D variable with covariates using RFsp.6.2.3 Spatial prediction 2D continuous variable using RFsp.6.2 A generic framework for spatial prediction using Random Forest.6.1.6 Ensemble predictions using SuperLearner package.6.1.5 Ensemble predictions using h2oEnsemble.6.1.4 Spatial prediction of 3D (numeric) variables.6.1.3 Modelling numeric soil properties using h2o.6.1.2 Spatial prediction of soil classes using MLA’s.6.1 Spatial prediction of soil properties and classes using MLA’s.6 Machine Learning Algorithms for soil mapping.5.3.7 Mapping accuracy and soil survey costs.5.3.6 Universal measures of mapping accuracy.5.3.5 Derivation and interpretation of prediction interval.5.3.4 Accuracy of the predicted model uncertainty.5.3.3 Cross-validation and its limitations.5.3.1 Mapping accuracy and numeric resolution.5.3 Accuracy assessment and the mapping efficiency.5.2.16 Predicting with multiscale and multisource data.5.2.14 Selecting spatial prediction models.5.2.11 Predictions at point vs block support.5.2.10 Regression-kriging and polygon averaging.5.2.9 Regression-kriging examples using the GSIF package.5.2.7 Universal kriging prediction error.5.2.6 Spatial Prediction using multiple linear regression.5.2.5 Regression-kriging (generic model).5.2.4 Geostatistics-driven soil mapping (pedometric mapping).5.2 Spatial prediction of soil variables.5.1.4 Vertical aggregation of soil properties.5.1.3 Modelling the variation of soil with depth.5.1.2 Universal model of soil variation.5.1 Aspects of spatial variability of soil variables.
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5 Statistical theory for predictive soil mapping.4.2.5 Overlaying and subsetting raster stacks and points.4.2.4 Filtering out missing pixels and artifacts.4.2.3 Deriving DEM parameters using SAGA GIS.4.2.2 Downscaling or upscaling (aggregating) rasters.
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4.2.1 Converting polygon maps to rasters.4.1.3 Soil covariate data sources (250 m resolution or coarser).4.1.2 Soil covariate data sources (30–100 m resolution).
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