XRF-measured rubidium concentration is the best predictor variable for estimating the soil clay content and salinity of semi-humid soils in two catenas

Tóth, Tibor; Kovács, Zsófia Adrienn; Rékási, Márk

doi:10.1016/j.geoderma.2019.02.011

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…Such calibrations could be specific in terms of geographical area or sample type. It would also be useful to find calibrations allowing to infer other relevant properties for the characterization of amendments or organic fertilizers, such as electrical conductivity, as has been already done in soils [39]. Future work is also envisaged to investigate the potential combination of data from XRF with that obtained using other techniques like near- and mid-infrared spectroscopy that may provide complementary information to infer other variables relative to the composition or particular properties of organic amendments.…”

Section: Resultsmentioning

confidence: 99%

Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry

López-Núñez

Ajmal-Poley

González‐Pérez

et al. 2019

IJERPH

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The determination of heavy metals in soils and organic amendments, such as compost, manure, biofertilizer, and sludge, generally involves the digestion of samples with aqua regia, and the determination of those in the solution using various techniques. Portable X-ray fluorescence (PXRF) has many advantages in relation to traditional analytical techniques. However, PXRF determines the total elemental content and, until now, its use for the analysis of organic amendments has been limited. The objective of this work is the calibration of a PXRF instrument to determine the aqua regia-soluble elemental contents directly in solid samples of organic amendments. Our proposal will avoid the digestion step and the use of other laboratory techniques. Using a training set of samples, calibration functions were obtained that allow the determination of the aqua regia-soluble contents from the PXRF readings of total contents. The calibration functions (obtained by multiple linear regression) allowed the quantitative determination of the aqua regia-soluble contents of Fe, K, P, S, Zn, Cu, Pb, Sr, Cr, and Mn, as well as the organic matter content and a semi-quantitative assessment of Al, Ca, V, Ba, Ni, and As contents. The readings of Si, Fe, Al, Ca, K, or S were used as correction factors, indicating that the calibrations functions found are truly based on the chemical composition of the sample matrix. This study will allow a fast, cheap, and reliable field analysis of organic amendments and of other biomass-based materials.

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Section: Resultsmentioning

confidence: 99%

Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry

López-Núñez

Ajmal-Poley

González‐Pérez

et al. 2019

IJERPH

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“…For example, several elements (e.g., Ti, Fe, Co, and Cu) were positively correlated with clay content in soils of Louisiana with high clay content but low sand content, whereas these elements were negatively correlated with clay content in New Mexico soils with high sand and silt content but low clay content (Zhu et al, ). Tóth et al () showed that Rb concentration measured by PXRF was a good predictor for estimating clay content. Its correlation with clay content was positive (r = 0.974) in a transect ranging from coarse sandy soils (Arenosol) to soils with more clay and SOC (Chernozem), but negative (r = −0.895) in a transect ranging from no saline and non‐sodic soils (Chernozem) to saline‐sodic soils (Solonetz) (Tóth et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Tóth et al () showed that Rb concentration measured by PXRF was a good predictor for estimating clay content. Its correlation with clay content was positive (r = 0.974) in a transect ranging from coarse sandy soils (Arenosol) to soils with more clay and SOC (Chernozem), but negative (r = −0.895) in a transect ranging from no saline and non‐sodic soils (Chernozem) to saline‐sodic soils (Solonetz) (Tóth et al, ). The mineral fraction of soils with particle size ranging from fine clay to coarse sand essentially consists of various primary and secondary minerals (Sparks, ).…”

Section: Discussionmentioning

confidence: 99%

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Zhang

Hartemink

2019

European J Soil Science

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We tested one front-end data fusion method to combine visible near-infrared (vis-NIR) and portable X-ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south-central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air-dried soil samples were scanned with vis-NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis-NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front-end data fusion approach, including: (a) 10 PCs from vis-NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis-NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF-estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models.PXRF spectra and elemental data can predict soil texture with validation R 2 > 0.85, better than using solely vis-NIR spectra, and combining vis-NIR and PXRF spectra improved the prediction. Combining PXRF and vis-NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis-NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis-NIR and PXRF spectra via PCA should be used to estimate TC and TN. Highlights• The use of front-end data fusion method to combine vis-NIR and PXRF spectra for predicting soil properties.

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“…This can be attributed to the fact that large parts of the Hungarian lowlands have been affected by stagnant water and consequently have fine sediments with some minor soluble salt concentration. Since the values of EC reflect not only soluble salts but also fine particles and organic matter [71], the categories of slight and moderate salinity also reflect clayey soils, which are quite widespread in the eastern part of Hungary. These areas are classified as "potential salt-affected soils" in Szabolcs's map (see Figure S4), meaning that these areas could be potentially threated by salt problems.…”

Section: Classified Maps Of Salt Severitymentioning

confidence: 99%

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

et al. 2020

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Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

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XRF-measured rubidium concentration is the best predictor variable for estimating the soil clay content and salinity of semi-humid soils in two catenas

Cited by 14 publications

References 13 publications

Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry

Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

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