| 13 Nov 2025
Prediction of Peat Properties from Transmission Mid-Infrared Spectra
Abstract. Better understanding of peatland dynamics requires more data on more peat properties than provided by existing databases. These data needs may be addressed with resource efficient measurement tools, such as models that predict peat properties from mid-infrared spectra (MIRS). High-quality spectral prediction models are already used for mineral soils, but similar developments for peatland-focused research lag behind.
Here, we present transmission-MIRS prediction models for peat which are openly available, easy to use, have basic quality checks for prediction quality, and propagate prediction errors. The models target element contents (C, N, H, O, P, S, K, Ca, Si, Ti), element ratios (C/N, H/C, O/C), isotope values (δ13C, δ15N), physical properties (bulk density, loss on ignition (LOI), macroporosity, non-macroporosity, volume fraction of solids, hydraulic conductivity, specific heat capacity, dry thermal conductivity), thermodynamic properties (Gibbs free energy of formation (ΔGf0)), and nominal oxidation state of carbon (NOSC). They are representative for more diverse peat samples than currently existing peat-exclusive models while having a competitive predictive accuracy. Relatively accurate predictions can be made for example for many element contents (C, N, O, S, Si, Ca, ΔGf0, O/C, H/C, bulk density, and LOI).
Many of these properties are not predicted by existing high-quality prediction models focusing on mineral soils. For some of the target variables, high-quality prediction models focusing on mineral soils exist. These models may be more accurate, but reported predictive accuracies are not directly comparable due to imbalances in the amount of organic vs mineral soil samples in the training data. We suggest that some soil properties are easier to predict for peat, whereas others are easier to predict for mineral soils, emphasizing that we need new approaches to meaningfully compare prediction errors of spectral models computed on datasets with variable amounts of organic soils. Our tests also indicate that replacing δ13C and δ15N measurements by MIRS models probably is not feasible due to large prediction errors. Future studies should address the lack of open training and validation data for some peat properties (O, H, NOSC, ΔGf0, LOI, H/C, O/C), the lack of mineral-rich peat samples, and improve and standardize model validation and comparison for models trained on data with very different proportions of peat soils. This study is a step to catch up with high quality standards set by models for mineral soils and it provides novel models for several peat properties. By filling data gaps in the Peatland Mid-Infrared Database, we make a step to provide the data required to better understand peatland dynamics.
This manuscript provides an overview of a variety of open, accessible chemometric models produced to provide rapid assessment tools for peat soils akin to the many spectroscopic tools available for mineral soils. The work describes the research gaps that currently exist for global assessments of peatland, outlining a diverse range of important analytes that are not commonly collected on mineral soils (important elemental contents, thermodynamic properties, etc), and which due to complex project and sampling demands, are not always assessed on all peat samples. It highlights how the developments in spectral prediction pipelines for mineral soils, particularly the focus on prediction domain analysis, error propagation, open access code and data and user friendly tooling, have allowed for the inference of analytes to soils where they were not originally collected in a robust and repeatable manner, and sets the scene for the development of similar models for peatlands.
The authors produced an assemblage of models from the pmird database of varying qualities, and using modelled parameters along with pedotransfer derived properties, gap-filled the database to provide a broad range of peat properties over thousands of additional samples.
In all, the paper presents an important contribution towards improving tools and databases for the monitoring and reporting of under-measured peatland. It extends recent developments in spectral inference for mineral soils to comparatively under-assessed peat environments. Further, the application of such techniques for relatively un-explored properties, such as new elemental contents and ratios, and peat specific thermodynamic properties is quite novel. while the manuscript is generally well written, moderate revision and refinement of the messaging in the introduction and discussion could improve clarity for the reader.
Comments:
As this potential to model BD underpins the proposed pedotransfer functions for porosity, hydraulic conductivity, specific heat capacity and thermal conductivity, the paper requires more depth in the introduction on developments or suggestions that would argue against this thesis.
See: Minasny, B., McBratney, A. B., Tranter, G., & Murphy, B. W. (2008). Using soil knowledge for the evaluation of mid-infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical properties. European Journal of Soil Science, 59(5), 960–971. https://doi.org/10.1111/j.1365-2389.2008.01058.
See: Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.-M., & McBratney, A. (2010). Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TrAC Trends in Analytical Chemistry, 29(9), 1073–1081. https://doi.org/10.1016/j.trac.2010.05.006
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