Characterizing soils using a portable X-ray fluorescence spectrometer: 1. Soil texture

Zhu, Yongnan; Weindorf, David C.; Zhang, Wentai

doi:10.1016/j.geoderma.2011.08.010

Cited by 172 publications

(101 citation statements)

References 48 publications

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“…Results showed that pXRF devices provide adequate analytical accuracy when compared to conventional laboratory-based methods [17,32,34,35]. On the other hand, few efforts have been made to apply proximal sensors on predictions of soil physical properties [33]. Furthermore, parent material and the intensity of both weathering and pedogenesis may exert strong influences on soil physical properties, such as soil particle size distribution [36], because its pattern represents a unique combination of primary and secondary minerals, reflecting the elemental composition of soils [33].…”

Section: Study Area and Laboratory Analysesmentioning

confidence: 99%

“…In this sense, digital soil mapping and modeling are viewed as an alternative to increase not only soil information [3], but also the accuracy required for detailed soil maps, by the adoption of new tools and techniques to analyze, integrate and visualize soil and environmental datasets [4]. In recent years, extra effort has been put into the creation and use of new covariates that represent soil-forming factors [5,6], which are crucial for achieving adequate accuracy in soil mapping and a properties [17,[31][32][33]. In theory, a pXRF is able to detect many elements of the periodic table, since each one has a typical fluorescence energy.…”

Section: Introductionmentioning

confidence: 99%

“…In theory, a pXRF is able to detect many elements of the periodic table, since each one has a typical fluorescence energy. Such sensors have the advantage of being a portable proximal sensing tool that provides immediate estimates of contents of various chemical elements in soils, with none or minimum sample pre-processing [32,33]. Results showed that pXRF devices provide adequate analytical accuracy when compared to conventional laboratory-based methods [17,32,34,35].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Section: Study Area and Laboratory Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Portable X-ray fluorescence spectrometer (pXRF) is a tool used in works of several fields of study for identification and quantification of chemical elements present in varied materials (Ioannides et al, 2016;Milić, 2014;Peinado et al, 2010;Rouillon;Taylor, 2016;Terra et al, 2014;Zhang, 2011). This equipment emits high-energy X-ray beams, which cause the displacement of electrons from inner to outer orbits as they hit the atoms of the elements in the sample.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P 2 O 5 , Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R 2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.Index terms: Soil analyses; spatial prediction; proximal sensor. RESUMOA determinação de atributos do solo auxilia no correto manejo da sua fertilidade. O equipamento portátil de fluorescência de raios-X (pXRF) foi recentemente adotado para determinar o teor total de elementos químicos em solos, permitindo inferências sobre atributos do solo. No entanto, esses estudos ainda são escassos no Brasil e em outros países. Os objetivos deste trabalho foram prever atributos do solo a partir de dados do pXRF, comparando-se os métodos de regressão linear múltipla stepwise (SMLR) e de random forest (RF), além de mapear e validar atributos do solo. 120 amostras de solo foram coletadas em três profundidades e submetidas a análises laboratoriais. Utilizou-se o pXRF para leitura das amostras e determinou-se o teor total de elementos. A partir dos dados do pXRF, foram utilizadas SMLR e RF para predizer resultados laboratoriais, que refletem atributos do solo, e os modelos foram validados. O melhor método foi utilizado para espacializar os atributos do solo. Utilizando SMLR, os modelos apresentaram valores elevados de R² (≥0,8), porém maior acurácia foi obtida na modelagem com RF. A capacidade de troca de cátions potencial e efetiva, matéria orgânica do solo, pH, saturação por bases e teores trocáveis de Ca, Al e Mg apresentaram ajustes adequados e predições acuradas com RF. Dos dez atributos do solo preditos por RF a partir de dados do pXRF, sete apr...

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“…Previously, PXRF and VisNIR DRS have been independently used to successfully predict a wide range of soil physicochemical properties, including organic C (Morgan et al, 2009;Chakraborty et al, 2013), gypsum content (Weindorf et al, 2009(Weindorf et al, , 2013a, salinity (Swanhart et al, 2014), pH (Sharma et al, 2014), texture (Zhu et al, 2011), cation exchange capacity , diagnostic subsurface horizons and features (Weindorf et al, 2012c), moisture , and organic and inorganic pollutants (Weindorf et al, 2012a(Weindorf et al, , 2013bChakraborty et al, 2010;Paulette et al, 2015). Most importantly, Weindorf et al (2012b) showed that PXRF could be used for enhanced soil horizonation whereby horizons could be differentiated using elemental data from PXRF in soil profiles where morphological differentia were unremarkable.…”

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confidence: 99%

Lithologic Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy

Weindorf

Chakraborty

Abdalsatar

et al. 2015

Soil Science Society of America Journal

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Lithologic discontinuity identification can be arduous and erroneous in instances where distinct morphological differences between parent materials are absent. Often, investigators must wait for laboratory data to help differentiate parent materials via physicochemical properties. This study used visible near-infrared diffuse reflectance spectroscopy (VisNIR DRs) and portable X-ray fluorescence (PXRF) spectrometry for establishing parent material differentia more quickly. Ten pedons containing 135 samples were scanned in situ in the united states, Italy, and Hungary, morphologically described by trained pedologists, then sampled for standard laboratory characterization. Compared with laboratory data and/or morphologically described discontinuities, PXRF data were associated with large, abrupt changes in standardized PXRF differences of elements (DEs), noted in data plots as DE maxima and minima-areas of likely discontinuity. standardized VisNIR DRs calculated differences (CDs) in reflectance spectra (350-2500 nm) were also associated with discontinuities based on CD reflectance maxima and minima. Notably within both types of data plots, lithologic discontinuities were not well captured by the proximal sensors when CD or DE values fell in the data plot midsection (i.e., not at maxima or minima within the data plots). Across the pedons evaluated, PXRF was more useful for detecting discontinuities than VisNIR DRs. summarily, PXRF showed good alignment with morphologically established discontinuities in eight out of 10 pedons, while VisNIR DRs showed good alignment in only five pedons. Both PXRF and VisNIR DRs provided useful information for lithologic discontinuity recognition, especially in soils with nondescript morphology.Abbreviations: CD, calculated difference; DE, difference of elements; EC, electrical conductivity; LD, lithologic discontinuity; PCA, principal component analysis; PXRF, portable X-ray fluorescence; SOM, soil organic matter; VisNIR DRS, visible near-infrared diffuse reflectance spectroscopy.

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Characterizing soils using a portable X-ray fluorescence spectrometer: 1. Soil texture

Cited by 172 publications

References 48 publications

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

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

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

Lithologic Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy

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