Articles | Volume 7, issue 2
https://doi.org/10.5194/soil-7-525-2021
© 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-7-525-2021
© 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
| 
18 Aug 2021
Original research article |
| 18 Aug 2021
| 18 Aug 2021
Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring
Philipp Baumann
CORRESPONDING AUTHOR
Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
Swiss Competence Center for Soils (KOBO), School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
Anatol Helfenstein
Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
Soil Geography and Landscape Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Andreas Gubler
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Armin Keller
Swiss Competence Center for Soils (KOBO), School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences BFH, Bern, Switzerland
Reto Giulio Meuli
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Daniel Wächter
Swiss Soil Monitoring Network (NABO), Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland
Juhwan Lee
Department of Smart Agro-industry, Gyeongsang National University, Jinju 52725, Republic of Korea
Raphael Viscarra Rossel
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Johan Six
Institute of Agricultural Sciences, Department of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
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Cited
19 citations as recorded by crossref.
- Data mining of urban soil spectral library for estimating organic carbon Y. Hong et al. 10.1016/j.geoderma.2022.116102
- Development of Hungarian spectral library: Prediction of soil properties and applications M. MOHAMMEDZEİN et al. 10.18393/ejss.1275149
- Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy E. Mammadov et al. 10.3390/land13020154
- Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms A. Karami et al. 10.1007/s11368-024-03825-7
- Performance of in situ vs laboratory mid-infrared soil spectroscopy using local and regional calibration strategies I. Greenberg et al. 10.1016/j.geoderma.2021.115614
- Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery H. Chandra & R. Rao Nidamanuri 10.1109/LGRS.2025.3564386
- Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification Y. Hong et al. 10.1016/j.geoderma.2022.116284
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
- The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication J. Demattê et al. 10.3390/rs14030740
- Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations W. Żelazny & T. Šimon 10.3390/agriculture12050682
- Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset W. Ng et al. 10.1002/saj2.20697
- Spectral variable selection for estimation of soil organic carbon content using mid‐infrared spectroscopy J. Wang et al. 10.1111/ejss.13267
- The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon S. Li et al. 10.1111/ejss.13202
- Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties S. Grunwald et al. 10.3390/s24216855
- Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation Y. Hong et al. 10.1016/j.catena.2023.107628
- An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. 10.1016/j.earscirev.2024.104797
- The global standard bearers of soil governance L. Peake & C. Robb 10.1016/j.soisec.2022.100055
- A soil spectral library of New Zealand Y. Ma et al. 10.1016/j.geodrs.2023.e00726
- Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library A. Helfenstein et al. 10.5194/soil-7-193-2021
18 citations as recorded by crossref.
- Data mining of urban soil spectral library for estimating organic carbon Y. Hong et al. 10.1016/j.geoderma.2022.116102
- Development of Hungarian spectral library: Prediction of soil properties and applications M. MOHAMMEDZEİN et al. 10.18393/ejss.1275149
- Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy E. Mammadov et al. 10.3390/land13020154
- Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms A. Karami et al. 10.1007/s11368-024-03825-7
- Performance of in situ vs laboratory mid-infrared soil spectroscopy using local and regional calibration strategies I. Greenberg et al. 10.1016/j.geoderma.2021.115614
- Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery H. Chandra & R. Rao Nidamanuri 10.1109/LGRS.2025.3564386
- Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification Y. Hong et al. 10.1016/j.geoderma.2022.116284
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
- The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication J. Demattê et al. 10.3390/rs14030740
- Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations W. Żelazny & T. Šimon 10.3390/agriculture12050682
- Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset W. Ng et al. 10.1002/saj2.20697
- Spectral variable selection for estimation of soil organic carbon content using mid‐infrared spectroscopy J. Wang et al. 10.1111/ejss.13267
- The cost‐effectiveness of reflectance spectroscopy for estimating soil organic carbon S. Li et al. 10.1111/ejss.13202
- Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties S. Grunwald et al. 10.3390/s24216855
- Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation Y. Hong et al. 10.1016/j.catena.2023.107628
- An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. 10.1016/j.earscirev.2024.104797
- The global standard bearers of soil governance L. Peake & C. Robb 10.1016/j.soisec.2022.100055
- A soil spectral library of New Zealand Y. Ma et al. 10.1016/j.geodrs.2023.e00726
1 citations as recorded by crossref.
Latest update: 08 Jul 2025
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
We developed the Swiss mid-infrared spectral library and a statistical model collection across...
