Multi-scale digital terrain analysis and feature selection for digital soil mapping

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Spatial prediction of USDA‐ great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types (2012, methodology): “Thus, the model with the smallest AIC and residual deviance was selected; anova was conducted to evaluate predictor importance as it requires the minimal processing time (Behrens et al., 2010).”
Selecting appropriate machine learning methods for digital soil mapping (2020, methodology): “[71] Silt (0-20cm) (g kg -1 ) 399 Str RS+DTA - - 0.”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, methodology): “In ecology, a first attempt was made by Bel et al. (2005) and later Bel et al. (2009) to decluster the sampling units used in the calibration of a CART model.”
An illustrated introduction to general geomorphometry (2017, background): “Catchment and dispersive areas as well as topographic index are widely used in hydrological and related soil, plant and geomorphic studies (Behrens et al., 2010; Beven, 1997; Florinsky et al., 2002; Mitusov et al., 2013, 2014; Moore et al., 1993; Omelko et al., 2012; Sharaya and Shary, 2011; Shary a”