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

European Journal of Soil Science · 2026-04-28

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.

Defining ‘soil science’

Soil Security · 2025-01-09 · 8 citations

The term ‘soil science’ remains inadequately defined, despite numerous definitions of soil itself. While it is commonly understood as the scientific study of soil, the adjective ‘scientific’ typically denotes a systematic, methodological approach grounded in falsifiable principles. However, few attempts have been made to formally define the discipline of soil science itself. This paper proposes an integrated definition, aligning with contemporary perspectives that view soil not as a discrete entity but as a dynamic component of the Earth system. Our definition is: “Soil science is the study of the soil of the Earth and other planets, using evolving theories and knowledge to understand its role in sustaining ecosystem functioning, tackling environmental challenges, and supporting humanity.”

Urbanization and sealing of fertile soils: A case study in Wisconsin 2001–2021

Soil Security · 2025-04-06

• Rates of urban development and soil sealing are quantified in Wisconsin: 2001–2021. • Development disproportionately affected fertile soils at state and county scales. • 7 ha day -1 of soil was sealed with impervious cover state-wide. • 12 ha day -1 of soil was converted to urban land state-wide: mainly from agriculture. • Future growth threatens fertile cultivated soils at the urban periphery. Globally, human population growth is coupled with increased urbanization. There is increasing competition for land, along with growing demands for food production and the ecosystem services that soils provide in urban and non-urban areas. Here we analyze the increase in developed land across Wisconsin, USA and within three rapidly developing counties (Brown, Dane, and Waukesha counties) between 2001 and 2021 and quantify what soils have been impacted. In those 20 years, state-wide developed land increased by 85,704 ha (+8 %) while the extent of sealed soils increased by 53,358 ha (+20 %), corresponding to 7 ha day -1 of soil sealing. Newly developed areas were mostly converted from agriculture (50 % cultivated crops; 29 % hay or pasture). At both state and county scales, development occurred predominantly on Alfisols and Mollisols and disproportionally impacted soils with high agricultural productivity. Future development (2021–2041) in the three counties will affect important farmland (49–68 %) and a high proportion of cultivated crops (28–57 %). Urbanization in Wisconsin largely affects soil security, and the maintenance and improvement of soil resources. This study provides a systematic approach to analyze changes in urban development and its effect on soils distribution and farmland potential.

Factors driving inorganic carbon levels in the soils of the conterminous USA

CATENA · 2025-02-20 · 15 citations

Prediction accuracy of pXRF, MIR, and Vis‐NIR spectra for soil properties—A review

Soil Science Society of America Journal · 2025-03-01 · 26 citations

Abstract Here, we review the prediction accuracy for soil properties using portable X‐ray fluorescence (pXRF), mid‐infrared (MIR), and visible near‐infrared (Vis‐NIR) and the factors impacting predictions and its accuracy. In total, 305 published papers were reviewed, and most of them were from Australia, Brazil, China, and the United States. About 44% of papers focused on the prediction of soil organic carbon (SOC) using Vis‐NIR spectra. Partial least squares regression was most frequently used. Most studies sampled Alfisols, Inceptisols, and Entisols, and up to 40‐cm depth. Researcher‐based factors (type or brand of spectrometers, which differ in hardware, spectral range, resolution, and calibration protocols; preprocessing methods; prediction models; and soil analysis methods for calibration) and soil‐based factors (horizon and depth) were explored. MIR spectra had better prediction accuracy with a mean R 2 over 0.8 for sand, clay, total N, total C (TC), SOC and soil inorganic carbon (SIC), and cation exchange capacity compared to Vis‐NIR and pXRF. In the past 20 years, prediction accuracy tended to increase for sand, silt, clay, SIC, soil organic matter, and EC when using MIR and Vis‐NIR spectra, and for TC and CaCO 3 when using pXRF spectra. Preprocessing methods, spectral range, calibration, type of the prediction models (i.e., machine and deep learning), and source of soil spectra (Vis‐NIR, MIR, and pXRF), which are used to reduce noise and multicollinearity, calibrate data, and smooth spectra, all affected the prediction. In general, MIR spectra obtained the highest prediction accuracy for most soil properties. Future studies should focus on the effects of soil‐based factors (parent material, soil mineralogy, pedogenesis, soil type, and horizon/depth) on the prediction accuracy of soil physical and chemical properties.

Controlling factors of soil organic and inorganic carbon in North Adana, Türkiye

Geoderma Regional · 2025-02-23 · 1 citations

Soil Survey

Springer biographies · 2025-01-01

Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties

Geoderma · 2025-06-15 · 6 citations

• A pruned Hierarchical Random Forest (pHRF) method was developed to address data imbalance issues in soil surveys. • The pHRF method showed out-of-bag scores over 0.7 at multiple taxonomic levels for soil classification. • This approach provided narrower prediction intervals and reduced the uncertainties in soil property estimates. Soil data and soil maps are crucial for Earth system modeling, water management, agricultural production, and climate change studies, and reducing uncertainties in soil property and soil class maps improves their reliability. Here, we present a pruned Hierarchical Random Forest (pHRF) framework to map soil taxa and properties over the National Ecological Observatory Network (NEON) sites in the Contiguous United States (CONUS). The pHRF method reduces uncertainties in predictions compared to POLARIS v1, providing smaller prediction intervals for the distributions of soil properties. In addition, pHRF addresses two data imbalance issues in soil survey data—uneven spatial distribution of georeferenced soil observations, and secondly underrepresentation of certain soil taxa. Unlike traditional hierarchical soil classification, pHRF conditions the probabilities of finer taxonomic levels based on their parent levels and removes implausible predictions (identified as errors) using field-validated soil taxa, improving prediction intervals. To address the categorical imbalance, soil taxa belonging to minority parent soil taxa are predicted with their own models, without being overlooked compared to using a single model on all soil taxa. For spatial imbalance, each model dynamically adapts its spatial coverage, incorporating more neighboring soil data in areas where georeferenced soil observations are sparse. In data-scarce areas, field-validated soil taxa are resampled to improve the representation of soil variation. The pHRF-derived soil classification showed out-of-bag scores above 0.7 at different taxonomic levels. The probabilistic map of soil series was then used to estimate soil properties, by linking them to a harmonized soil properties database. When evaluated against independent NEON measurements, pHRF performed better than POLARIS v1 for root zone properties (0–60 cm), particularly for sand, clay, and organic matter content. Specifically, pHRF reduced RMSE by 1.15 (sand%), 1.32 (clay%), and 0.21 (log-scaled organic matter%) while improving correlations. For pH, both models showed a reasonable fit (RMSE: ∼0.70, correlation: 0.85). This approach presents a development in refining soil properties mapping, especially in its effectiveness in reducing uncertainties. Future work will focus on reducing uncertainties and correcting biases in soil property estimates.

Factors governing the formation, distribution and taxonomy of soils with glossic horizons and features in the contiguous USA

Geoderma Regional · 2025-06-10 · 1 citations

Vital for Sustainable Agriculture: Pedological Knowledge and Mapping

European Journal of Soil Science · 2025-01-01 · 14 citations

ABSTRACT Over the past 60 years, efforts to enhance agricultural productivity have mainly focussed on optimising strategies such as the use of inorganic fertilisers, advancements in microbiology and improved water management practices. Here, we emphasise the critical role of pedology as a foundation in soil management and long‐term sustainability. We will demonstrate how overlooking the intrinsic properties of soils can result in detrimental effects on soil and overall sustainability. Communication between academia, extension experts, consultants and farmers often results in an overemphasis on the surface layer, for example, 20 to 40 cm, neglecting the functions that occur at depth. Soil health and regenerative agriculture must be coupled with an understanding of how soil functions as a dynamic system. We find that pedological knowledge and digital soil mapping technologies are underused for achieving sustainable agriculture. By bridging the gap between pedology and emerging agricultural technologies, we can provide land users with the tools needed to make informed decisions, ensuring that their practices not only increase production but also preserve the health of the soil for future generations.