Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

Abstract

Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This work aimed to evaluate the efficiency of such tools for mapping soil classes and properties in tropical conditions. Soils were classified and sampled at 39 locations in a regular-grid design with a 200-m distance between samples. A pXRF and a magnetometer were used in all samples, and DTM values were obtained for every sampling site. Through visual analysis, boxplots were used to identify the best variables for distinguishing soil classes, which were further mapped using fuzzy logic. The map was then validated in the field. An ordinary least square regression model was used to predict sand and clay contents using DTM, pXRF and the magnetometer as predicting variables. Variables obtained with pXRF showed a greater ability for predicting soil classes (overall accuracy of 78% and 0.67 kappa index), as well as for estimating sand and clay contents than those acquired with DTM and the magnetometer. This study showed that pXRF offers additional variables that are key for mapping soils and predicting soil properties at a detailed scale. This would not be possible using only DTM or magnetic susceptibility.

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

“…In addition, by providing results quickly and inexpensively, it may favor gathering more observations (points visited) in the field and also, through predictions, reduce the number of laboratory analyses. The use of pXRF to improve spatial and non-spatial soil predictions was also found by Silva et al (2016b), who used magnetic susceptibility and pXRF data, as well as continuous variables derived from digital elevation model for soil classes and properties prediction in Brazil, finding that magnetic susceptibility and pXRF data increased the models accuracy when associated with terrain data. Weindorf; Bakr; Zhu (2014), after presenting examples of correlations among the element contents obtained by pXRF and results of laboratory analysis, suggested that many works using this equipment would be performed focusing on predicting soil properties in the years to come.…”

“…This procedure aimed to evaluate the possibility of using pXRF data as a basis for mapping soil properties (Duda et al, 2017;Silva et al, 2016b), providing easily obtainable variables, at low cost, rapidly and with no generation of chemical residues.…”

“…However, when working in smaller areas, mainly in developing countries, it is common to face difficulties in obtaining data with high spatial resolution, which tends to make the use of these variables unfeasible. In this sense, pXRF can be an alternative to obtain a large amount of punctual data that, after being spatialized, may contribute to spatial predictions (Silva et al, 2016b).…”

Portable X-ray fluorescence (pXRF) applications in tropical Soil Science

“…These authors found a significant correlation between Fe and clay content (R 2 = 0.94) in soils of the state of Louisiana (USA). This relationship was also found in Latosols, highly weathered tropical soils, developed from gabbro and gneiss, in Minas Gerais State, Brazil, for sand and clay contents using ordinary least square multiple linear regression (Silva et al, 2016). Weindorf et al (2013) satisfactorily determined the calcium sulphate content in soils using pXRF.…”

“…Regarding soil survey, classification and digital mapping perspectives, Silva et al (2016) employed pXRF elemental data in addition to soil magnetic susceptibility and high resolution digital terrain models (slope gradient, topographic wetness index, etc.) to aid in distinguishing four types of Latosols (Oxisols) according to their chemical properties related to parent material, and mapping their spatial distribution over the study area, in Minas Gerais (Brazil).…”