Proximal sensors for modeling clay mineralogy and characterization of soil textural fractions developed from contrasting parent materials

Abstract

Proximal sensors combined with X-ray diffraction (XRD) have optimized soil characterization, but scarce studies have focused on predicting the contents of minerals under this scope. The objectives herein were to: a) use the portable X-ray fluorescence (pXRF) spectrometry, diffuse reflectance spectroscopy in the range of visible and near-infrared (Vis-NIR), magnetic susceptibility (χ) and XRD to characterize the mineralogy of soils derived from representative Brazilian soil parent materials, and b) create models to quantify the minerals obtained via XRD. Twenty-two soil profiles developed from gabbro, gneiss, quartzite, mineral and organic sediments were described with 53 soil horizons sampled. Each sample had the sand, silt and clay fractions separated and analyzed with XRD, pXRF, χ, and Vis-NIR. Models were created using the Random Forest algorithm permuting the following predictor variables (separately and combined): pXRF, parent material (PM), χ, soil texture (sand, silt, and clay content), and Vis-NIR. Models’ accuracy was calculated using the leave-one-out cross-validation method. Si, Al, Fe, Ca, K, and Ti contents obtained by pXRF and the χ discriminated the soil particle size fractions according to the parent material. XRD analysis allowed the evaluation of the pedogenetic development of soils and their relation to the respective parent material. The best models for mineral contents were found for hematite (Hm) (1 0 4)+(Gt) (1 3 0) (R2 = 0.85), Hm (1 1 0) + Mh (1 3 1) (R2 = 0.76), kaolinite (Kt) (0 0 1) (R2 = 0.73), Kt (0 0 2) (R2 = 0.80), mica (Mc) (0 0 1) (R2 = 0.77), and Mc (0 2 0) + Kt (0 2 0) (R2 = 0.81). Clay mineralogy content was accurately modeled using only pXRF and parent material data. This approach can facilitate and speed up detailed soil mineralogy characterization. Further studies are encouraged to model the content of minerals found in the sand and silt fractions of soils with diverse mineralogy via proximal sensors and using larger data sets.