Pedogenetic processes operating at different intensities inferred by geophysical sensors and machine learning algorithms

Pedogenetic processes such as ferralitization and argilluviation control various soil attributes. Understanding the intensities of pedogenesis can provide answers for several fields of environmental studies, including soil science and the geosciences. Recently, new geotechnologies such as geophysics applied to soil science and machine learning algorithms have proven to be a potential tool in pedosphere studies. In this research, we performed component principal analyses and determined the ideal number of clusters based on geophysical soil data and satellite images. Then, we used the ideal number of clusters, and ferralitization and argilluviation indices, as input data in five modeling (prediction and spatialization) algorithms to infer different ferralitization and argilluviation intensities in soils formed from the same soil parent material. The results showed that avNNet had the best model performance for modeling the clusters showing that the ideal number of clusters was three. The variables which contributed the most to the modeling were the solar diffuse radiation, topographic wetness index, and digital elevation model. The model’s specificity was greater than its sensitivity. The areas over diabase and Nitisols in the east of the study area presented greater ferralitization rates than diabase and Nitisols over western areas. On the other hand, the areas over siltite and Lixisols in the east presented greater argilluviation rates than metamorphosed siltite/siltite and Lixisols over western areas. The relief and topographic position strongly affected the evaluated pedogenetic processes, since they controlled the hydric dynamics in the area. The geophysical variables were related to soil attributes and pedogenesis, which contributed to modeling and clusterization processes.