Distinguishing Soil Horizons of Alfisols Using <scp>MIR</scp> , Vis– <scp>NIR</scp> , and <scp>XRF</scp> Spectra

ABSTRACT We studied horizonation of 150 Alfisols on a research farm in Wisconsin, USA. Most of these Alfisols had developed in loess covering dense glacial till; common horizons were an Ap over a glossic horizon (E/Bt or Bt/E) over a Bt covering the glacial till (2Bt). In addition to field identification, we distinguished the horizons by combining soil properties and spectra (Vis–NIR, MIR, and XRF) using a random forest (RF) model. A total of 503 soil samples from 150 pedons were analyzed. Soil samples were scanned using visible–near infrared (Vis–NIR) (350–2500 nm), mid‐infrared (MIR) (4000–600 cm −1 ), and X‐ray fluorescence (XRF) (0–50 keV) spectrometers. Higher silt, pH, soil organic carbon (SOC), Ca, K, P, Si, Ti, Zn, and Zr contents were measured in the Ap horizon, whereas the glossic horizons were characterized by higher CIE L * (lightness) and b * (yellow‐blue) values. The underlying Bt horizon had higher clay, Al, and Fe contents, while the 2B(t) horizon was distinguished by higher sand, coarse fragments, and CIE a * values. The combination of soil properties and spectra (Vis–NIR, MIR, XRF) achieved a higher overall accuracy for predicting soil horizons compared to their individual use. The Ap horizons were best predicted using MIR or XRF (R 2 = 0.98), the glossic horizons using combined Vis–NIR and XRF spectra (R 2 = 0.98), the Bt horizons using MIR (R 2 = 0.90), whereas the 2B(t) horizons were best predicted using combined spectra (Vis–NIR + MIR + XRF) (R 2 = 0.50). In the absence of soil data, these spectra can be used with high accuracy to distinguish A and various B horizons of Alfisols.

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Publication Details

Journal
European Journal of Soil Science
Published
2026-04-28
DOI
https://doi.org/10.1111/ejss.70337
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.00

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article

Distinguishing Soil Horizons of Alfisols Using MIR , Vis– NIR , and XRF Spectra

Alfred E. Hartemink, Eric O. Young, Gafur Gözükara, Yakun Zhang
European Journal of Soil Science
Soil Geostatistics and Mapping
article

Distinguishing Soil Horizons of Alfisols Using MIR , Vis– NIR , and XRF Spectra

Alfred E. Hartemink, Eric O. Young, Gafur Gözükara, Yakun Zhang
article en

Abstract

ABSTRACT We studied horizonation of 150 Alfisols on a research farm in Wisconsin, USA. Most of these Alfisols had developed in loess covering dense glacial till; common horizons were an Ap over a glossic horizon (E/Bt or Bt/E) over a Bt covering the glacial till (2Bt). In addition to field identification, we distinguished the horizons by combining soil properties and spectra (Vis–NIR, MIR, and XRF) using a random forest (RF) model. A total of 503 soil samples from 150 pedons were analyzed. Soil samples were scanned using visible–near infrared (Vis–NIR) (350–2500 nm), mid‐infrared (MIR) (4000–600 cm −1 ), and X‐ray fluorescence (XRF) (0–50 keV) spectrometers. Higher silt, pH, soil organic carbon (SOC), Ca, K, P, Si, Ti, Zn, and Zr contents were measured in the Ap horizon, whereas the glossic horizons were characterized by higher CIE L * (lightness) and b * (yellow‐blue) values. The underlying Bt horizon had higher clay, Al, and Fe contents, while the 2B(t) horizon was distinguished by higher sand, coarse fragments, and CIE a * values. The combination of soil properties and spectra (Vis–NIR, MIR, XRF) achieved a higher overall accuracy for predicting soil horizons compared to their individual use. The Ap horizons were best predicted using MIR or XRF (R 2 = 0.98), the glossic horizons using combined Vis–NIR and XRF spectra (R 2 = 0.98), the Bt horizons using MIR (R 2 = 0.90), whereas the 2B(t) horizons were best predicted using combined spectra (Vis–NIR + MIR + XRF) (R 2 = 0.50). In the absence of soil data, these spectra can be used with high accuracy to distinguish A and various B horizons of Alfisols.

European Journal of Soil ScienceVol. 77(3)
University of Wisconsin System (US), Soil Science Society of America (US), U.S. Dairy Forage Research Center (US)
U.S. Department of Agriculture
Life in Land
Openalex Percentile: Top 12%
Soil Geostatistics and Mapping
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