Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/40811
metadata.artigo.dc.title: Tracing tropical soil parent material analysis via portable X-ray fluorescence (pXRF) spectrometry in Brazilian Cerrado
metadata.artigo.dc.creator: Mancini, Marcelo
Weindorf, David C.
Chakraborty, Somsubhra
Silva, Sérgio Henrique Godinho
Teixeira, Anita Fernanda dos Santos
Guilherme, Luiz Roberto Guimarães
Curi, Nilton
metadata.artigo.dc.subject: Parent material mapping
Elemental contents
Proximal sensor
Prediction models
metadata.artigo.dc.publisher: Elsevier B.V.
metadata.artigo.dc.date.issued: Mar-2019
metadata.artigo.dc.identifier.citation: MANCINI, M. et al. Tracing tropical soil parent material analysis via portable X-ray fluorescence (pXRF) spectrometry in Brazilian Cerrado. Geoderma, [S.I.], v. 337, p. 718-728, Mar. 2019.
metadata.artigo.dc.description.abstract: Parent material (PM) type is crucial for understanding the distribution of soils across the landscape. However, such information is not available at a detailed scale in Brazil. Thus, portable X-ray fluorescence (pXRF) spectrometry can aid in PM characterization by measuring elemental concentrations. This work focused on mapping soil PM (specifically variations of phyllite) using pXRF data and evaluating which soil horizon (A, B, or C) provides optimal PM identification in the Brazilian Cerrado. A total of 120 soil samples were collected from A, B, and C horizons across the study area as well as associated PMs; all were subjected to pXRF analysis. Artificial neural network, support vector machine, and random forest were used to model and predict PMs through pXRF data to the entire area. The nine maps (3 soil horizons data × 3 algorithms) generated for PM prediction were validated through overall accuracy, Kappa coefficient, producer's, and user's accuracy. The most accurate PM maps were obtained by using C horizon information (overall accuracy of 0.87 and Kappa coefficient of 0.79) via support vector machine algorithm. Land use dramatically influenced the results. In sum, pXRF data can be successfully used to predict soil PMs by robust algorithms. Specifically, V, Ni, Sr, and Pb were optimal for predicting PM regardless of land use.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/abs/pii/S0016706118316069?via%3Dihub#!
http://repositorio.ufla.br/jspui/handle/1/40811
metadata.artigo.dc.language: en
Appears in Collections:DCS - Artigos publicados em periódicos

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