Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/40810
metadata.artigo.dc.title: Parent material distribution mapping from tropical soils data via machine learning and portable X-ray fluorescence (pXRF) spectrometry in Brazil
metadata.artigo.dc.creator: Mancini, Marcelo
Weindorf, David C.
Silva, Sérgio Henrique Godinho
Chakraborty, Somsubhra
Teixeira, Anita Fernanda dos Santos
Guilherme, Luiz Roberto Guimarães
Curi, Nilton
metadata.artigo.dc.subject: Pedology
Parent material
Machine learning
Digital soil mapping
Prediction models
Tropical soils
Pedologia
Aprendizagem de máquina
Mapeamento digital do solo
Modelos de predição
Solos tropicais
metadata.artigo.dc.publisher: Elsevier B.V.
metadata.artigo.dc.date.issued: Nov-2019
metadata.artigo.dc.identifier.citation: MANCINI, M. et al. Parent material distribution mapping from tropical soils data via machine learning and portable X-ray fluorescence (pXRF) spectrometry in Brazil. Geoderma, [S.I.], v. 354, Nov. 2019. Não paginado.
metadata.artigo.dc.description.abstract: 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 study evaluates the efficiency of using portable X-ray fluorescence (pXRF) spectrometry together with machine learning algorithms in a large area populated with a wide variety of soil classes and land uses to build accurate PM distribution maps from soil data. Samples from A and B horizons were collected from 117 sites, totaling 234 samples, along with representative PM samples of the predominant PMs in the region. Elemental contents of PM and soil samples were obtained using pXRF. Random Forest (RF), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models built with and without Principal Component Analysis (PCA) were used to predict PMs from A and B horizon samples separately. For validation, PM was identified in 23 different spots and compared with the predicted PM via overall accuracy and Kappa coefficient. Maps built with models excluding PCA had an overall accuracy ranging from 0.87 to 0.96 and kappa coefficient ranging from 0.74 to 0.91. Maps generated with PCA based models reached 100% overall accuracy. Prediction of PM using pXRF with samples from either A or B horizon and machine learning algorithms offers solid results even when applied in large areas with high land use and soil class variability.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/abs/pii/S0016706119307888#!
http://repositorio.ufla.br/jspui/handle/1/40810
metadata.artigo.dc.language: en
Appears in Collections:DCS - Artigos publicados em periódicos

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