Parent material distribution mapping from tropical soils data via machine learning and portable X-ray fluorescence (pXRF) spectrometry in Brazil

• Published in: Geoderma Vol. 354; p. 113885
• Main Authors: Mancini, Marcelo, Weindorf, David C., Silva, Sérgio Henrique Godinho, Chakraborty, Somsubhra, Teixeira, Anita Fernanda dos Santos, Guilherme, Luiz Roberto Guimarães, Curi, Nilton
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2019
• Subjects: B horizons; Brazil; Digital soil mapping; discriminant analysis; fluorescence; land use; Machine learning; Parent material; Pedology; prediction; Prediction models; principal component analysis; soil sampling; support vector machines; Tropical soils; X-radiation; X-ray fluorescence spectroscopy; Brazil; Pedology; RF; Digital soil mapping; Prediction models; Machine learning; LDA; Tropical soils; Parent material; SVM; PM; PCA

Knowledge about parent material (PM) is crucial to understand the properties of overlying soils. Assessing PM of very deep soils, however, is not easy. Previous studies have predicted PM via proximal sensors and machine learning algorithms, but within small areas and with low soil variety. This stud...