Using pXRF and vis-NIR spectra for predicting properties of soils developed in loess
Section snippets
INTRODUCTION
Proximal soil sensing techniques have been widely used to provide information on soils. Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectra have been used as rapid, environment-friendly, and low-cost techniques to measure or predict soil properties with high accuracy, and in a non-destructive way compared to traditional chemical methods (Taylor et al., 2004; Waiser et al., 2007; Rossel et al., 2010, 2011; Rouillon and Taylor, 2016). Vis-NIR and pXRF spectrometers have
Soil sampling and analysis
The study was conducted in the Driftless area, Dane County, Wisconsin, USA. The study area was approximately 7 000 ha and was mainly agricultural land, with maize, soybean, and alfalfa as the most prevalent field crops. The climate in this region is characterized by warm, moist summer and cold, dry winter. The mean annual precipitation and mean annual temperature are 857 mm and 7.3 °C, respectively. Loess materials cover much of the landscape in the study area. The soils were described and
Characterization of soil horizons
The distributions of soil particle size fractions (clay, silt, and sand), TC, pH, and elements of the 348 soil samples are shown in Fig. 2 for A, B, and A+B horizons. The A horizon had an average of 12% sand, 66% silt, and 22% clay. The Bt horizon had an average of 15% sand, 58% silt, and 27% clay. In the soil profiles, clay, Mg, Al, Fe, and Sr increased from A to Bt horizon, whereas TC, TN, pH, Ca, Mn, and Zn noticeably decreased from A to Bt horizon. The soil samples had higher concentrations
Model performance using pXRF and vis-NIR spectra for prediction of soil properties
In our study, good results were obtained for predicting clay, silt, sand, pH, and TC from vis-NIR and pXRF spectra. The PCA+Cubist method predicted clay, silt, and sand from vis-NIR spectra with good accuracy (validation R2 = 0.84, 0.60, and 0.71, respectively). Clay and sand were more accurately predicted than silt, which may be due to the special characteristics of clay (O–H in water, FeOH–, AlOH–, and MgOH– in the structure of mineral crystal lattice) and corresponding spectral features in
CONCLUSIONS
The Cubist model was used with individual or combined pXRF and vis-NIR spectra through PCA to investigate its performance in predicting clay, silt, sand, TC, and pH of soils developed in loess. Its prediction performance was evaluated for data from A, B, and A+B horizons. We found that the models using individual pXRF spectra produced better predictions for clay, silt, and sand, whereas those using individual vis-NIR spectra performed better in TC and pH predictions. Combining vis-NIR and pXRF
ACKNOWLEDGEMENTS
The authors are grateful to Mr. Dave Evans, who sampled these pedons for his master project. The first author is supported by the Scientific Research Projects (BAP) (No. 2019-2757) of Eskisehir Osmangazi University for postdoc research at the Department of Soil Science, University of Wisconsin-Madison.
SUPPLEMENTARY MATERIAL
Supplementary material for this article can be found in the online version.
References (54)
- et al.
Non-saturated soil organic horizon characterization via advanced proximal sensors
Geoderma
(2017) - et al.
Soil characterization across catenas via advanced proximal sensors
Geoderma
(2017) - et al.
Digital soil mapping of a red clay subsoil covered by loess
Geoderma
(2014) - et al.
Terra rossa catenas in Wisconsin, USA
Catena
(2014) - et al.
Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study
Geoderma
(2008) - et al.
Towards digital soil morphometrics
Geoderma
(2014) - et al.
Tracing tropical soil parent material analysis via portable X-ray fluorescence (pXRF) spectrometry in Brazilian Cerrado
Geoderma
(2019) - et al.
Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy
Chemometr Intell Lab Syst
(2008) - et al.
Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra
Geoderma
(2019) - et al.
An assessment of model averaging to improve predictive power of portable vis-NIR and XRF for the determination of agronomic soil properties
Geoderma
(2016)
Determination of base saturation percentage in agricultural soils via portable X-ray fluorescence spectrometer
Geoderma
Can field portable X-ray fluorescence (pXRF) produce high quality data for application in environmental contamination research?
Environ Pollut
Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soil reaction (pH)
Geoderma
Characterizing soils via portable X-ray fluorescence spectrometer: 4. Cation exchange capacity (CEC)
Geoderma
Utilizing portable X-ray fluorescence spectrometry for in-field investigation of pedogenesis
Catena
XRF-measured rubidium concentration is the best predictor variable for estimating the soil clay content and salinity of semi-humid soils in two catenas
Geoderma
Synthesized use of VisNIR DRS and PXRF for soil characterization: Total carbon and total nitrogen
Geoderma
Advances in portable X-ray fluorescence (PXRF) for environmental, pedological, and agronomic applications
Adv Agron
X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content
Geoderma
Soil horizon delineation using vis-NIR and pXRF data
Catena
Characterizing soils using a portable X-ray fluorescence spectrometer: 1. Soil texture
Geoderma
Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils
Soil Biol Biochem
Prediction of soil fertility via portable X-ray fluorescence (pXRF) spectrometry and soil texture in the Brazilian Coastal Plains
Geoderma
Assessing models for prediction of some soil chemical properties from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian Coastal Plains
Geoderma
Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra
Appl Spectrosc
Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties
Soil Sci Soc Am J
Soil subgroup prediction via portable X-ray fluorescence and visible near-infrared spectroscopy
Geoderma
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2022, Computers and Electronics in AgricultureCitation Excerpt :In addition, limited studies have explored sampling strategies' effect on prediction accuracy (Zhang and Hartemink, 2017; 2021; Chatterjee et al., 2021; Gozukara et al., 2022c) of soil properties and none of the studies to date have focused on the effect of soil to water ratios on prediction accuracy of EC and pH using individual or combined pXRF and Vis-NIR spectra. However, some studies have focused on the effect of individual or combined pXRF and Vis-NIR spectra on prediction performance of EC and pH (Weindorf et al., 2016; O’Rourke et al., 2016a; Wan et al., 2019; Zhang and Hartemink, 2020; Gozukara et al., 2022a-b-c), whereas some studies have focused on vertical and horizontal variation across the soil profile wall (Adhikari et al., 2016; Grauer-Gray and Hartemink, 2018; Mancini et al., 2021; Gozukara et al., 2022b), sampling strategies (Zhang and Hartemink, 2017), and different machine learning algorithms (Yang et al., 2019) on the prediction performance of EC and pH. Although each of these factors can significantly affect prediction performances, no studies have evaluated these factors together. In particular, there is an increasing need for quantitative soil data and information using spatial analysis, which has been utilized by many studies compared to traditional soil sampling based on high labor and extensive soil sampling.
Soil particle size prediction using Vis-NIR and pXRF spectra in a semiarid agricultural ecosystem in Central Anatolia of Türkiye
2022, CatenaCitation Excerpt :It is well known that soil surface often contains higher organic materials than subsurface (Jiang et al., 2017; Yost et al., 2019; Gozukara et al., 2021b, 2022a) and soil mineralogy varies along with the soil profile compared to the soil surface (Yost et al., 2019; Saricaoğlu et al., 2021; Kowalska et al., 2022). These differences between topsoils and subsoils layers affect the prediction accuracy for sand, silt, and clay content (Silva et al., 2020; Gozukara et al., 2022a). However, no studies have been conducted in which differences such as individual or combined soil spectra in the soil surface, soil profile, and soil surface and profile wall datasets for prediction of sand, silt, and clay distribution in semiarid regions.