Machine learning for predicting soil classes in three semi-arid landscapes

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Addressing the issue of digital mapping of soil classes with imbalanced class observations (2019, background): “Statistical and geostatistical models are routinely applied throughout the world for mapping of soil classes in different spatial scales and extents (Brungard et al., 2015; Heung et al., 2016; Ma et al., 2019).”
Machine learning and soil sciences: a review aided by machine learning tools (2019, methodology): “This method has been recently applied in some DSM studies (Vaysse and Lagacherie, 2017; Sirsat et al., 2018; Cao et al., 2019).”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, methodology): “For example, Brungard et al. (2015) used a set of covariates selected a priori by an expert on the area under study.”
Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, methodology): “In addition to FFS, we also used recursive feature elimination (RFE, explained in Kuhn & Johnson, 2013) to compare state-of-the art procedures (see e.g. Brungard et al., 2015; Meyer et al., 2017a,b; Ghosh & Joshi, 2014; Stevens et al., 2013, in the ﬁeld of environmental mapping).”