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dc.creatorSilva, Sérgio H. G.-
dc.creatorWeindorf, David C.-
dc.creatorFaria, Wilson M.-
dc.creatorPinto, Leandro C.-
dc.creatorMenezes, Michele D.-
dc.creatorGuilherme, Luiz R. G.-
dc.creatorCuri, Nilton-
dc.date.accessioned2022-02-10T20:33:24Z-
dc.date.available2022-02-10T20:33:24Z-
dc.date.issued2021-08-
dc.identifier.citationSILVA, S. H. G. et al. Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil. Pedosphere, [S.l.], v. 31, n. 4, p. 615-626, Aug. 2021. DOI: 10.1016/S1002-0160(21)60007-3.pt_BR
dc.identifier.urihttps://doi.org/10.1016/S1002-0160(21)60007-3pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49250-
dc.description.abstractPortable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO2 contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourcePedospherept_BR
dc.subjectMagnetic susceptibilitypt_BR
dc.subjectMagnetometerpt_BR
dc.subjectSoil classpt_BR
dc.subjectSoil spatial analysispt_BR
dc.subjectSpatial resolutionpt_BR
dc.subjectTerrain variablespt_BR
dc.subjectSuscetibilidade magnéticapt_BR
dc.subjectMagnetômetropt_BR
dc.subjectClasse do solopt_BR
dc.subjectAnálise espacial do solopt_BR
dc.subjectResolução espacialpt_BR
dc.subjectVariáveis do terrenopt_BR
dc.titleProximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazilpt_BR
dc.typeArtigopt_BR
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