Articles | Volume 7, issue 1
SOIL, 7, 193–215, 2021
SOIL, 7, 193–215, 2021
Original research article
14 Jun 2021
Original research article | 14 Jun 2021

Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library

Anatol Helfenstein et al.

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Cited articles

Araújo, S. R., Wetterlind, J., Demattê, J. A. M., and Stenberg, B.: Improving the Prediction Performance of a Large Tropical Vis-NIR Spectroscopic Soil Library from Brazil by Clustering into Smaller Subsets or Use of Data Mining Calibration Techniques, Eur. J. Soil Sci., 65, 718–729,, 2014. a
Bader, C., Müller, M., Szidat, S., Schulin, R., and Leifeld, J.: Response of Peat Decomposition to Corn Straw Addition in Managed Organic Soils, Geoderma, 309, 75–83,, 2018. a
Baumann, P.: Simplerspec, GitHub, available at: (last access: 12 November 2020), 2020. a
Baumann, P., Helfenstein, A., Gubler, A., Keller, A., Meuli, R. G., Wächter, D., Lee, J., Viscarra Rossel, R., and Six, J.: Developing the Swiss soil spectral library for local estimation and monitoring, SOIL Discuss. [preprint],, in review, 2021. a, b, c, d, e, f, g, h, i, j
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.-M., and McBratney, A.: Critical Review of Chemometric Indicators Commonly Used for Assessing the Quality of the Prediction of Soil Attributes by NIR Spectroscopy, TrAC Trends Anal. Chem., 29, 1073–1081,, 2010. a
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.