Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-193-2021
© Author(s) 2021. 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-7-193-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
14 Jun 2021
Original research article |
| 14 Jun 2021
| 14 Jun 2021
Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library
Anatol Helfenstein
CORRESPONDING AUTHOR
Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, Universitätsstrasse 2, 8092 Zurich, Switzerland
Soil Geography and Landscape Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Philipp Baumann
Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, Universitätsstrasse 2, 8092 Zurich, Switzerland
Raphael Viscarra Rossel
School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, Perth, Western Australia, Australia
Andreas Gubler
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Stefan Oechslin
School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
Johan Six
Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH Zurich, Universitätsstrasse 2, 8092 Zurich, Switzerland
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Cited
17 citations as recorded by crossref.
- Prediction of peat properties from transmission mid-infrared spectra H. Teickner & K. Knorr https://doi.org/10.5194/soil-12-497-2026
- Mid-infrared spectroscopy as a potential tool for monitoring the success of tropical peatland restoration A. Kunarso et al. https://doi.org/10.1016/j.jenvman.2025.127327
- Rapid detection and quantification of atmospheric heavy metal deposition on plant leaves using machine learning-enhanced NIR spectroscopy Z. Huang et al. https://doi.org/10.1016/j.infrared.2025.106200
- When spectral libraries are too complex to search: Evolutionary subset selection for domain-adaptive calibration L. Ramirez-Lopez et al. https://doi.org/10.1016/j.aca.2026.345651
- An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. https://doi.org/10.1016/j.earscirev.2024.104797
- Mid-Infrared spectroscopy for soil organic carbon estimation. Part I: Global review and meta-analysis T. Li et al. https://doi.org/10.1016/j.still.2026.107236
- Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring P. Baumann et al. https://doi.org/10.5194/soil-7-525-2021
- Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada M. Schiedung et al. https://doi.org/10.1016/j.catena.2022.106194
- Peatland Mid-Infrared Database H. Teickner et al. https://doi.org/10.1038/s41597-026-06986-x
- Selecting the right samples rather than more samples: A new spectral–environmental similarity strategy for local soil spectral modeling L. Li et al. https://doi.org/10.1016/j.geoderma.2026.117710
- Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam N. Hau et al. https://doi.org/10.1016/j.chemolab.2024.105253
- Analytical Study of the Detection Model for Sulphate Saline Soil Based on Mid-Infrared Spectrometry H. Wei et al. https://doi.org/10.3390/chemosensors13050173
- Mid-infrared spectroscopy as a tool to monitor molecular changes in fens upon restoration K. Kim et al. https://doi.org/10.1016/j.orggeochem.2026.105154
- Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China S. Zhao et al. https://doi.org/10.1016/j.jenvman.2024.121311
- Mid-infrared spectroscopy for soil organic carbon estimation. Part II: Evaluating preprocessing at global and national scales T. Li et al. https://doi.org/10.1016/j.still.2026.107237
- Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy L. Lotfollahi et al. https://doi.org/10.1016/j.jenvman.2023.118854
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. https://doi.org/10.5194/essd-16-2941-2024
17 citations as recorded by crossref.
- Prediction of peat properties from transmission mid-infrared spectra H. Teickner & K. Knorr https://doi.org/10.5194/soil-12-497-2026
- Mid-infrared spectroscopy as a potential tool for monitoring the success of tropical peatland restoration A. Kunarso et al. https://doi.org/10.1016/j.jenvman.2025.127327
- Rapid detection and quantification of atmospheric heavy metal deposition on plant leaves using machine learning-enhanced NIR spectroscopy Z. Huang et al. https://doi.org/10.1016/j.infrared.2025.106200
- When spectral libraries are too complex to search: Evolutionary subset selection for domain-adaptive calibration L. Ramirez-Lopez et al. https://doi.org/10.1016/j.aca.2026.345651
- An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. https://doi.org/10.1016/j.earscirev.2024.104797
- Mid-Infrared spectroscopy for soil organic carbon estimation. Part I: Global review and meta-analysis T. Li et al. https://doi.org/10.1016/j.still.2026.107236
- Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring P. Baumann et al. https://doi.org/10.5194/soil-7-525-2021
- Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada M. Schiedung et al. https://doi.org/10.1016/j.catena.2022.106194
- Peatland Mid-Infrared Database H. Teickner et al. https://doi.org/10.1038/s41597-026-06986-x
- Selecting the right samples rather than more samples: A new spectral–environmental similarity strategy for local soil spectral modeling L. Li et al. https://doi.org/10.1016/j.geoderma.2026.117710
- Estimation of soil organic carbon content using visible and near-infrared spectroscopy in the Red River Delta, Vietnam N. Hau et al. https://doi.org/10.1016/j.chemolab.2024.105253
- Analytical Study of the Detection Model for Sulphate Saline Soil Based on Mid-Infrared Spectrometry H. Wei et al. https://doi.org/10.3390/chemosensors13050173
- Mid-infrared spectroscopy as a tool to monitor molecular changes in fens upon restoration K. Kim et al. https://doi.org/10.1016/j.orggeochem.2026.105154
- Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China S. Zhao et al. https://doi.org/10.1016/j.jenvman.2024.121311
- Mid-infrared spectroscopy for soil organic carbon estimation. Part II: Evaluating preprocessing at global and national scales T. Li et al. https://doi.org/10.1016/j.still.2026.107237
- Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy L. Lotfollahi et al. https://doi.org/10.1016/j.jenvman.2023.118854
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. https://doi.org/10.5194/essd-16-2941-2024
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at...
