Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

• Published in: Pedosphere Vol. 31; no. 4; pp. 615 - 626
• Main Authors: SILVA, Sérgio H.G., WEINDORF, David C., FARIA, Wilson M., PINTO, Leandro C., MENEZES, Michele D., GUILHERME, Luiz R.G., CURI, Nilton
• Format: Journal Article
• Language: English
• Published: Beijing Elsevier Ltd 01.08.2021; Elsevier Science Ltd; Department of Soil Science,Federal University of Lavras,Lavras 37200-000,Brazil%Department of Earth and Atmospheric Sciences,Central Michigan University,Mount Pleasant 48858,USA
• Subjects: Algorithms; Complexity; Digital Elevation Models; Digital mapping; Fluorescence; Iron; Magnetic permeability; Magnetic susceptibility; magnetometer; Magnetometers; Mapping; portable X-ray fluorescence spectrometer; Sensors; Silicon dioxide; soil class; Soil classification; Soil improvement; Soil mapping; Soil maps; soil spatial analysis; Soils; Spatial discrimination; Spatial resolution; Spectrometry; Terrain; terrain variables; Titanium; Tropical environments; Tropical soils; Variables; X-ray fluorescence; Brazil; soil class; terrain variables; portable X-ray fluorescence spectrometer; magnetometer; spatial resolution; magnetic susceptibility; soil spatial analysis
• DOI: 10.1016/S1002-0160(21)60007-3
• ISSN: 1002-0160

Portable 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.