Articles | Volume 10, issue 1
https://doi.org/10.5194/soil-10-231-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-10-231-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Short communication
| 
10 Apr 2024
Short communication |
| 10 Apr 2024
| 10 Apr 2024
Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland
Simon Oberholzer
Institute of Geography, University Bern, 3012 Bern, Switzerland
Laura Summerauer
CORRESPONDING AUTHOR
Department of Environmental System Science, ETH Zürich, 8092 Zürich, Switzerland
Markus Steffens
Institute of Geography, University Bern, 3012 Bern, Switzerland
Department of Soil Science, Research Institute of Organic Agriculture, 5070 Frick, Switzerland
Chinwe Ifejika Speranza
Institute of Geography, University Bern, 3012 Bern, Switzerland
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Cited
16 citations as recorded by crossref.
- The employment of Vis-NIR and FTIR spectroscopy in combination with chemometrics for the authentication of Patin fish oil R. Martien et al. https://doi.org/10.1016/j.focha.2026.101298
- Integrated multispectral analysis of pyrogenic and total carbon in fire-managed cerrado soils A. Zanatta et al. https://doi.org/10.1016/j.still.2026.107062
- Non-destructive assessment of tilapia texture using Raman–infrared fusion and an attention-based LSTM model Y. Zhang et al. https://doi.org/10.1016/j.foodcont.2025.111539
- Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral Data M. Xiang et al. https://doi.org/10.3390/ijgi14100403
- Effective degradation of DELTA pesticide in soil by an atmospheric-pressure cold plasma-DBD system S. Jangra et al. https://doi.org/10.1039/D5RA08882H
- Cover cropping in organic reduced tillage systems: Maximizing soil cover or plant above ground biomass input? S. Oberholzer et al. https://doi.org/10.1111/ejss.70012
- A Gated Ridge Regression-Based Multimodal Spectral Fusion Approach for Predicting Soil Organic Matter G. Wang et al. https://doi.org/10.3390/agriculture15232402
- Cross-Site Prediction of Soil Organic Carbon in Plantation Forests Using Vis-NIR Spectroscopy and Target-Spectrum-Guided Source-Domain Weighting Y. Deng & Z. Meng https://doi.org/10.3390/f17080954
- Calibration-domain design and property-specific modeling strategies for the prediction of soil carbon and nitrogen using Vis–NIR spectroscopy R. Wang et al. https://doi.org/10.1016/j.microc.2026.119872
- A multi-branch Mamba-Transformer framework for hyperspectral soil property estimation W. Qi et al. https://doi.org/10.1016/j.engappai.2026.115769
- Monitoring Urban Land Use Intensity with Remote Sensing and Urban Traits: A Review A. Lausch et al. https://doi.org/10.3390/smartcities9070107
- Comparative Study of NIR-Based Machine Learning for Predicting Soil Nutrients in Indonesian Farmlands A. Adnan et al. https://doi.org/10.2478/ata-2026-0004
- Quantitative determination of soil alkali-hydrolyzable nitrogen and total nitrogen based on near-infrared spectroscopy combined with easily accessible physicochemical properties Y. Lu et al. https://doi.org/10.1016/j.infrared.2026.106923
- Rapid assessment of soil nutrients under continuous Gastrodia elata cropping using near- and mid-infrared spectroscopy combined with multi-strategy machine learning W. Zhang et al. https://doi.org/10.1016/j.saa.2026.128453
- Explainable machine learning for predicting POXC and organic carbon from mid-infrared spectra under varying soil scanning conditions Y. Gamagedara et al. https://doi.org/10.1016/j.soisec.2026.100252
- Applicability of Visible–Near-Infrared Spectroscopy to Predicting Water Retention in Japanese Forest Soils R. Sekiguchi et al. https://doi.org/10.3390/f16071182
16 citations as recorded by crossref.
- The employment of Vis-NIR and FTIR spectroscopy in combination with chemometrics for the authentication of Patin fish oil R. Martien et al. https://doi.org/10.1016/j.focha.2026.101298
- Integrated multispectral analysis of pyrogenic and total carbon in fire-managed cerrado soils A. Zanatta et al. https://doi.org/10.1016/j.still.2026.107062
- Non-destructive assessment of tilapia texture using Raman–infrared fusion and an attention-based LSTM model Y. Zhang et al. https://doi.org/10.1016/j.foodcont.2025.111539
- Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral Data M. Xiang et al. https://doi.org/10.3390/ijgi14100403
- Effective degradation of DELTA pesticide in soil by an atmospheric-pressure cold plasma-DBD system S. Jangra et al. https://doi.org/10.1039/D5RA08882H
- Cover cropping in organic reduced tillage systems: Maximizing soil cover or plant above ground biomass input? S. Oberholzer et al. https://doi.org/10.1111/ejss.70012
- A Gated Ridge Regression-Based Multimodal Spectral Fusion Approach for Predicting Soil Organic Matter G. Wang et al. https://doi.org/10.3390/agriculture15232402
- Cross-Site Prediction of Soil Organic Carbon in Plantation Forests Using Vis-NIR Spectroscopy and Target-Spectrum-Guided Source-Domain Weighting Y. Deng & Z. Meng https://doi.org/10.3390/f17080954
- Calibration-domain design and property-specific modeling strategies for the prediction of soil carbon and nitrogen using Vis–NIR spectroscopy R. Wang et al. https://doi.org/10.1016/j.microc.2026.119872
- A multi-branch Mamba-Transformer framework for hyperspectral soil property estimation W. Qi et al. https://doi.org/10.1016/j.engappai.2026.115769
- Monitoring Urban Land Use Intensity with Remote Sensing and Urban Traits: A Review A. Lausch et al. https://doi.org/10.3390/smartcities9070107
- Comparative Study of NIR-Based Machine Learning for Predicting Soil Nutrients in Indonesian Farmlands A. Adnan et al. https://doi.org/10.2478/ata-2026-0004
- Quantitative determination of soil alkali-hydrolyzable nitrogen and total nitrogen based on near-infrared spectroscopy combined with easily accessible physicochemical properties Y. Lu et al. https://doi.org/10.1016/j.infrared.2026.106923
- Rapid assessment of soil nutrients under continuous Gastrodia elata cropping using near- and mid-infrared spectroscopy combined with multi-strategy machine learning W. Zhang et al. https://doi.org/10.1016/j.saa.2026.128453
- Explainable machine learning for predicting POXC and organic carbon from mid-infrared spectra under varying soil scanning conditions Y. Gamagedara et al. https://doi.org/10.1016/j.soisec.2026.100252
- Applicability of Visible–Near-Infrared Spectroscopy to Predicting Water Retention in Japanese Forest Soils R. Sekiguchi et al. https://doi.org/10.3390/f16071182
Saved (final revised paper)
Latest update: 18 Oct 2026
Short summary
This study investigated the performance of visual and near-infrared spectroscopy in six fields in Switzerland. Spectral models showed a good performance for soil properties related to organic matter at the field scale. However, spectral models performed best in fields with low mean carbonate content because high carbonate content masks spectral features for organic carbon. These findings help facilitate the establishment and implementation of new local soil spectroscopy projects.
This study investigated the performance of visual and near-infrared spectroscopy in six fields...
