Quantification and characterization of pedogenetic maghemite and digital soil mapping using magnetic measurements

Abstract

Maghemite (Mh) is a ferrimagnetic mineral, so it can be used as an environmental tracer. This study aimed to compare the various methods for quantification of Mh in soils, as well as to characterize Mh of soils developed on different parent materials; and to evaluate the efficiency of proximal sensors (magnetometer and portable X-ray fluorescence -pXRF) as support for the digital mapping in the separation of Oxisols in an area with variable geology. In the first part of the study, the quantification of Mh was performed from four methods: (1) area of the reflections obtained by X-ray diffraction (XRD) (standard method); (2) Rietveld refinement; (3) selective dissolution with H2 SO4 and; (4) based on magnetic susceptibility (χ). The characterization of pedogenic Mh was performed from hysteresis sloops and crystallographic parameters. The dissolution-based methods with H 2SO4 overestimated the Mh content over the standard method based on XRD, whereas Mh values estimated by χ and Rietveld refinement were closer to those obtained by the standard method. Independently of the material of origin, the particles of Mh have simple domain, however they can acquire multidomain characteristics due to the isomorphic substitution and the hematite aggregation. In the second part of the study, 39 soils profiles were classified and analyzed by pXRF and magnetometer (χ). For each sampling site, the values of the digital terrain models (DTMs) were also obtained. Through the visual analysis, the boxplots were used to identify the best variables to distinguish the soil classes, which were further mapped using fuzzy logic and later validated. The multiple linear model were used to predict the sand and clay content. The DTMs, the elements obtained by pXRF and the χ were used as predictive variables. The DTMs were not able to distinguish most of the Oxisols in the study area, while the magnetic susceptibility data, together with SiO 2 and Fe evaluated by pXRF, aided in the prediction of soil classes. To estimate the sand and clay content, the variables obtained with pXRF showed greater predictability than those obtained with DTM and χ, demonstrating the potential of such proximal sensor as support for digital soil mapping.