Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/41402
Title: Prediction of soil attributes via pXRF spectrometry, magnetic susceptibility, and terrain attributes in a highly heterogeneous tropical area
Other Titles: Predição de atributos do solo através da espectrometria pXRF, susceptibilidade magnética e atributos terrenos em uma área tropical altamente heterogênica
Authors: Silva, Sérgio Henrique Godinho
Silva, Sérgio Henrique Godinho
Curi, Nilton
Barbosa, Julierme Zimmer
Keywords: Micronutrients
Granulometric fractions
Random forest
Proximal sensing
Pedometric
Portable X-ray fluorescence spectrometer (pXRF)
Micronutrientes
Frações granulométricas
Sensores próximos
Pedometria
Espectrômetro de florescência de raios-X portátil
Issue Date: 10-Jun-2020
Publisher: Universidade Federal de Lavras
Citation: PIERANGELI, L. M. P. Prediction of soil attributes via pxrf spectrometry, magnetic susceptibility, and terrain attributes in a highly heterogeneous tropical area. 2020. 61 p. Dissertação (Mestrado em Ciência do Solo)–Universidade Federal de Lavras, Lavras, 2020.
Abstract: Digital elevation models (DEM) and their derived variables, terrain attributes (TA), are commonly used in soil mapping. The use of proximal sensors, such as portable X-ray fluorescence spectrometer (pXRF) and susceptibilimeter, which determines magnetic susceptibility (MS), provides additional information that has improved the results obtained using only TAs. This work is composed of two chapters, whose studies were conducted at the Palmital Experimental Farm, belonging to the Federal University of Lavras (UFLA). The chapters are related to the use of proximal sensors in conjunction with TA for the prediction of physical and chemical attributes of soils. The first chapter contemplates the use of two proximal sensors, pXRF and MS, together with TA for the prediction of clay, silt, and sand contents through the random forest algorithm. The second chapter discusses the use of pXRF and MS in conjunction with TA in predicting available contents of B, Cu, Fe, Mn, and Zn. The maps were generated for the Palmital farm and validated for each predicted attribute, comparing the efficiency of each model. For the prediction of clay, silt, and sand, all models used the information acquired by pXRF in the final models. On the other hand, for the prediction of B and Zn, only the TA information was sufficient to achieve satisfactory R2 values. Clay and sand showed moderate accuracy, while silt showed low accuracy. For the prediction of chemical attributes, Cu, Fe, Mn, and Zn presented high to moderate accuracy. However, B reached low accuracy. This shows that pXRF is a powerful tool to assist in the accurate prediction of some soil attributes in a punctual and spatial way, contributing to the digital soil mapping.
URI: http://repositorio.ufla.br/jspui/handle/1/41402
Appears in Collections:Ciência do Solo - Mestrado (Dissertações)



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