Prediction of Peat Properties from Transmission Mid-Infrared Spectra

Teickner, Henning; Knorr, Klaus-Holger

doi:10.5194/egusphere-2025-4955

Preprints

https://doi.org/10.5194/egusphere-2025-4955

Preprints

13 Nov 2025

| 13 Nov 2025

Prediction of Peat Properties from Transmission Mid-Infrared Spectra

Henning Teickner and Klaus-Holger Knorr

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 (ΔG_f⁰)), 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, ΔG_f⁰, 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, ΔG_f⁰, 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.

Received: 07 Oct 2025 – Discussion started: 13 Nov 2025

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Journal article(s) based on this preprint

27 Apr 2026

Prediction of peat properties from transmission mid-infrared spectra

Henning Teickner and Klaus-Holger Knorr

SOIL, 12, 497–519, https://doi.org/10.5194/soil-12-497-2026,https://doi.org/10.5194/soil-12-497-2026, 2026

Short summary

Henning Teickner and Klaus-Holger Knorr

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-4955', Anonymous Referee #1, 10 Dec 2025
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:
The introduction features repeated reference to Bulk density as a predictable property through spectra in mineral soils, on lines 54 and 69. This physical property is influenced in situ by landscape position, biotic interactions and soil composition. Many would not regard it as a property that is commonly considered predictable through spectroscopic approaches, particularly in cases where spectra are sampled in laboratory conditions, after soil processing. Though Dangal and Sanderman have modeled BD observations to moderate success with MIR spectra, other studies, such as Minasny et al 2008 suggest that properties that involve soil-pore relationships are less reliably predicted, which is logically consistent for analysis on disturbed soil.

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.

Similarly, isotope ratios of δ¹³C and δ¹⁵N are introduced following the aims of the paper as a property of interest to model. Given their under-performance, and general absence from modelling in other soil spectral libraries, a deeper dive into both the utility of this information, and justification for what about the variation in δ¹³C and δ¹⁵N ratios can be detected within the MIR region. If this is exploratory in nature, as indicated by the results, and the minimal gap filling achieved in pmird, it is important to temper the reader’s expectations here appropriately.

The prediction domain of the models generated from pmird is highly dependent on the observed variation in peat analytical data and spectra available in the database. A sound understanding of the model calibration space is important to reduce mis-application of the models. As such, some greater exploratory data analysis on the pmird data can provide additional context to the reader, perhaps a table of summary statistics of the properties to be modelled, and the map showing the distribution of peat samples that comprise this dataset. As the pmird database itself is in pre-print, this does not need to be extensive here.

The presented model quality metrics are an important consideration that guide potential users and readers to a greater understanding of the implications of your work. As such, if the work adapts techniques from spectral modelling of mineral soils, It could benefit the ease of interpretation of the work to further adapt some commonly used quality metrics as well. I would suggest initially the MEC, RPIQ and LCCC as useful guides that users more familiar with modelling of mineral soils may useful.

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

Additional clarity issues:
Lines 33–38: The paragraph is vague regarding “sufficient resolution and detail.” Define what constitutes “sufficient.”

Lines 233:236: Sentence is quite long, with many clauses, please break this up.

Line 240: this paragraph repeats context established in the introduction, much of this is redundant within the methods.

Lines 377:385: The discussion on blank effect corrections for isotopic measurements is confusing. It first suggests these effects are only a minor contributor to RMSE, then proposes they may explain large prediction errors. This whole argument is quite confusing, consider omitting lines 384/385 for clarity.

In figure 4, fixing the y axis, and grouping the facets by derivative of spectra may make the prediction domains easier to interpret across the different properties being assessed.
Citation: https://doi.org/10.5194/egusphere-2025-4955-RC1
- AC2: 'Reply on RC1', Henning Teickner, 18 Jan 2026
  
  Please find our replies in the attached PDF document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4955-AC2
RC2:
'Comment on egusphere-2025-4955', Anonymous Referee #2, 12 Dec 2025

Title of paper reviewed: Prediction of Peat Properties from Transmission Mid-Infrared Spectra

This paper aims to develop high-quality, openly accessible mid-infrared spectral models for key peat properties, establish prediction domains for reliability, propagate uncertainties, and fill gaps in the PMIRD peat database. This work is important because peatlands hold major carbon stocks, yet spectral models for peat are far less developed than for mineral soils; improving them will strengthen peatland research and monitoring. Overall, the paper is structured relatively well, addresses a gap in peatland research. Nonetheless, there are several contradictory statements within the text that should be rectified. Furthermore, I did not see any error propagation that was mentioned in the objectives.
Comments on specific lines:

L20: Adding couple sentences describing the usage of11 these peat properties would be good. Are these properties related to the ability of soil as a carbon sink or something else?

L153-155: I found the sentences confusing, and I could not follow or understand what the authors are trying to imply here. Is DOM a relevant property for peat soil? Is it analysed? if not simply remove it from the text all together.

L176: check package name

L210: how are the testing dataset is being selected? the training is trained with Kennard Stone as mentioned in the paragraph. If the aim is to build prediction model that covers a large space of prediction domains, should not all the dataset be used to train the model?? The number of samples shown on Table 2 does not reflect the sentence here.

L224-225: It is still not clear here how the test samples are being obtained?

L227: define ELPD, or equation used for this before explaining threshold limit.

L245-253: Why do you need to check if the input data are within testing OR training instead of just the training domain? If the spectra are out of the prediction domain of the training dataset, won't it give unreliable estimates as mentioned?

L284: authors claimed to provide comparison of the models with other studies, but later in L292 claims the comparison is not comparable. Please elaborate.

Fig2: what is being plotted here? training/testing?? labels and legends would be helpful.

L347: Please clarify the difficulty of estimating S here. It looks like prediction fits the 1:1 line quite nicely.

L400-405: Authors explains that the testing prediction domain is a small fraction of the training prediction domain, and because of that the prediction might be erroneous. However, the authors described it differently in 2.6; in which it is recommended that the new data should be within the prediction or training domain. Fig 4 shows that this testing domain clearly falls within the training domain. Yet the sentence is contradictory. Please clarify the use of the prediction domains within text and rectify results and explanations accordingly.

Citation: https://doi.org/10.5194/egusphere-2025-4955-RC2
- AC1: 'Reply on RC2', Henning Teickner, 18 Jan 2026
  
  Please find our replies in the attached PDF document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4955-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-4955', Anonymous Referee #1, 10 Dec 2025
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:
The introduction features repeated reference to Bulk density as a predictable property through spectra in mineral soils, on lines 54 and 69. This physical property is influenced in situ by landscape position, biotic interactions and soil composition. Many would not regard it as a property that is commonly considered predictable through spectroscopic approaches, particularly in cases where spectra are sampled in laboratory conditions, after soil processing. Though Dangal and Sanderman have modeled BD observations to moderate success with MIR spectra, other studies, such as Minasny et al 2008 suggest that properties that involve soil-pore relationships are less reliably predicted, which is logically consistent for analysis on disturbed soil.

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.

Similarly, isotope ratios of δ¹³C and δ¹⁵N are introduced following the aims of the paper as a property of interest to model. Given their under-performance, and general absence from modelling in other soil spectral libraries, a deeper dive into both the utility of this information, and justification for what about the variation in δ¹³C and δ¹⁵N ratios can be detected within the MIR region. If this is exploratory in nature, as indicated by the results, and the minimal gap filling achieved in pmird, it is important to temper the reader’s expectations here appropriately.

The prediction domain of the models generated from pmird is highly dependent on the observed variation in peat analytical data and spectra available in the database. A sound understanding of the model calibration space is important to reduce mis-application of the models. As such, some greater exploratory data analysis on the pmird data can provide additional context to the reader, perhaps a table of summary statistics of the properties to be modelled, and the map showing the distribution of peat samples that comprise this dataset. As the pmird database itself is in pre-print, this does not need to be extensive here.

The presented model quality metrics are an important consideration that guide potential users and readers to a greater understanding of the implications of your work. As such, if the work adapts techniques from spectral modelling of mineral soils, It could benefit the ease of interpretation of the work to further adapt some commonly used quality metrics as well. I would suggest initially the MEC, RPIQ and LCCC as useful guides that users more familiar with modelling of mineral soils may useful.

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

Additional clarity issues:
Lines 33–38: The paragraph is vague regarding “sufficient resolution and detail.” Define what constitutes “sufficient.”

Lines 233:236: Sentence is quite long, with many clauses, please break this up.

Line 240: this paragraph repeats context established in the introduction, much of this is redundant within the methods.

Lines 377:385: The discussion on blank effect corrections for isotopic measurements is confusing. It first suggests these effects are only a minor contributor to RMSE, then proposes they may explain large prediction errors. This whole argument is quite confusing, consider omitting lines 384/385 for clarity.

In figure 4, fixing the y axis, and grouping the facets by derivative of spectra may make the prediction domains easier to interpret across the different properties being assessed.
Citation: https://doi.org/10.5194/egusphere-2025-4955-RC1
- AC2: 'Reply on RC1', Henning Teickner, 18 Jan 2026
  
  Please find our replies in the attached PDF document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4955-AC2
RC2:
'Comment on egusphere-2025-4955', Anonymous Referee #2, 12 Dec 2025

Title of paper reviewed: Prediction of Peat Properties from Transmission Mid-Infrared Spectra

This paper aims to develop high-quality, openly accessible mid-infrared spectral models for key peat properties, establish prediction domains for reliability, propagate uncertainties, and fill gaps in the PMIRD peat database. This work is important because peatlands hold major carbon stocks, yet spectral models for peat are far less developed than for mineral soils; improving them will strengthen peatland research and monitoring. Overall, the paper is structured relatively well, addresses a gap in peatland research. Nonetheless, there are several contradictory statements within the text that should be rectified. Furthermore, I did not see any error propagation that was mentioned in the objectives.
Comments on specific lines:

L20: Adding couple sentences describing the usage of11 these peat properties would be good. Are these properties related to the ability of soil as a carbon sink or something else?

L153-155: I found the sentences confusing, and I could not follow or understand what the authors are trying to imply here. Is DOM a relevant property for peat soil? Is it analysed? if not simply remove it from the text all together.

L176: check package name

L210: how are the testing dataset is being selected? the training is trained with Kennard Stone as mentioned in the paragraph. If the aim is to build prediction model that covers a large space of prediction domains, should not all the dataset be used to train the model?? The number of samples shown on Table 2 does not reflect the sentence here.

L224-225: It is still not clear here how the test samples are being obtained?

L227: define ELPD, or equation used for this before explaining threshold limit.

L245-253: Why do you need to check if the input data are within testing OR training instead of just the training domain? If the spectra are out of the prediction domain of the training dataset, won't it give unreliable estimates as mentioned?

L284: authors claimed to provide comparison of the models with other studies, but later in L292 claims the comparison is not comparable. Please elaborate.

Fig2: what is being plotted here? training/testing?? labels and legends would be helpful.

L347: Please clarify the difficulty of estimating S here. It looks like prediction fits the 1:1 line quite nicely.

L400-405: Authors explains that the testing prediction domain is a small fraction of the training prediction domain, and because of that the prediction might be erroneous. However, the authors described it differently in 2.6; in which it is recommended that the new data should be within the prediction or training domain. Fig 4 shows that this testing domain clearly falls within the training domain. Yet the sentence is contradictory. Please clarify the use of the prediction domains within text and rectify results and explanations accordingly.

Citation: https://doi.org/10.5194/egusphere-2025-4955-RC2
- AC1: 'Reply on RC2', Henning Teickner, 18 Jan 2026
  
  Please find our replies in the attached PDF document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4955-AC1

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Publish subject to revisions (further review by editor and referees) (05 Feb 2026) by Nicolas P.A. Saby

AR by Henning Teickner on behalf of the Authors (06 Feb 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (13 Feb 2026) by Nicolas P.A. Saby

RR by Anonymous Referee #1 (06 Mar 2026)

ED: Publish as is (11 Mar 2026) by Nicolas P.A. Saby

ED: Publish subject to technical corrections (16 Mar 2026) by Raphael Viscarra Rossel (Executive editor)

Based on my evaluation of the manuscript, I see this as a potentially valuable peat-focused spectral resource, but with serious gaps in essential methodological information and several overstated or misleading claims about what spectroscopy and global models can currently deliver.

The paper requires a major revision before I can recommend it for publication. Please address the following points:

Data and spectroscopy description:
The manuscript does not yet provide the minimum information needed to judge data quality and model robustness.

- Provide clear quantitative information on the pmird subset used: number of samples/cores/sites per target variable, the geographic extent and main peat types represented.

- Describe, at least in summary, how each target property was measured (analytical methods, laboratories, key QA/QC procedures), so readers can assess the reliability and comparability of the reference data.

- Add a dedicated description of the spectroscopy: instrument(s) used for transmission MIRS, optical configuration (e.g.\ pellets or cells), spectral range, resolution, number of scans, and how differences between contributing datasets and instruments were handled.

Clarify the study aims:
The current aims mix resource-building, methodological aspects, and broader aspirations without a clear hierarchy.

- Revise the aims so they are concise, specific, and directly traceable into the Methods and Results. For example, distinguish explicitly between: (i) developing and internally validating peat spectral models for a defined set of properties, (ii) implementing these models (with error propagation) in open tools, and (iii) using them to 'gap-fill' pmird.

- Avoid implying aims (such as universal applicability (or accuracy) across all organic soils) that are not actually tested in the manuscript.

Reframe spectroscopy’s role and avoid misleading claims:
Several statements about spectroscopy overstate its capabilities and status.

- Remove or rephrase statements suggesting that spectroscopy "replaces'' conventional laboratory analyses or achieves "partly competitive'' per-sample accuracy - these are incorrect. Emphasise instead that spectroscopy supplements and scales up conventional measurements, enabling many more samples to be analysed at lower marginal cost but generally with lower per-sample precision than reference methods - there is literature on the cost-effectiveness of soil spectroscopy.

- Explicitly state that spectroscopy only *partly* addresses the sampling and analysis bottleneck: sampling, subsampling, drying, crushing, grinding and a calibration subset with conventional analyses remain necessary. Again, there is literature on the cost effectiveness of spectroscopy, considering these (see attached).

Quantify predictive performance and remove vague language:
Terms such as "competitive predictive accuracy'', "relatively accurate'', "high quality'', "sufficient spatial resolution'' and other simular are subjective and currently unquantified.

- Replace such qualitative descriptors with quantitative ranges of appropriate metrics (e.g. R2, Nash--Sutcliffe efficiency, RMSE expressed relative to data range), especially in the Abstract, Introduction and Conclusions.

- Apply this consistently wherever you characterise model quality or compare to "traditional'' methods, so that readers can clearly understand what level of performance is meant.

Clarify generalisation, validation strategy, and the limits of global models
The present validation design and narrative do not fully reflect current understanding of global vs local performance (I have suggested some recent literature on the latter in the attached annotated file).

- Make explicit what your current validation does test (generalisation within the pmird domain, with non-independent train/test at the site level) and what it does not (performance on new sites, other peat types, or different measurement protocols).

- Add, or at least clearly discuss as future work, more appropriate generalisation tests such as grouped k-fold cross-validation by site or region, in line with current soil spectroscopy practice.

- In the Introduction and Discussion, explicitly acknowledge that large, mixed spectral libraries and general models often do not generalise well to local conditions without localisation (e.g. localisation, or transfer learning - see paper on "think global but fit local with transfer learning" for data on this). Clarify that your models, while trained on a relatively broad peat dataset, still require local evaluation and may need adaptation when applied to new regions, peat types or instruments.

Prediction domains are standard in modelling pipelines:
Prediction-domain checks are useful and part of the modelling pipeline. They should not be positioned as an innovation in a separete section.

- Treat prediction-domain checking is commone and standard good practice. No need for a specific section. Condense the dedicated prediction-domain section into a concise couple of sentences in the preprocessing section, keeping the key point that domain checks reduce, but do not eliminate, the risk of poor predictions even within the domain.

- Reframe the paper’s novelty: the main contribution is a peat-specific spectral library subset, a broad set of calibrated peat spectral models and gap-filled data products. Avoid claims that suggest new modelling concepts or globally/universally general models beyond what is actually demonstrated.

Position the usefulness of the models, tools and gap-filled products in light of localisation needs:
The open models and tools are potentially valuable, but their utility is constrained by known limitations of global models.

- Make clear that the open models, R packages, and gap-filled pmird tables are intended as a broadly calibrated, peat-specific starting point. They are *likely* most reliable for peat types and spectral conditions similar to those represented in pmird.

- Emphasise that, given the known limitations of globally calibrated spectral models, these resources should be *locally evaluated* and, where possible, adapted (e.g. via transfer learning or local augmentation) before use in new regions, instruments, or sample types. They complement rather than replace local calibration.

Gap-filling and error propagation:
This is potentially a useful contribution but must be presented with appropriate caution.

- When describing gap-filling of pmird, clearly state that only independently validated models (with acceptable test-set performance and no serious convergence issues) were used, and that predictions are accompanied by uncertainty estimates and prediction-domain flags.

- Emphasise in the Discussion that gap-filled values are *model-based estimates*, not equivalents of direct measurements, and that this is particularly important near the edges of the prediction domain or for weakly supported peat types

Grammar and scientific writing
The grammar and scientific writing style are generally acceptable, but need improving. Several sentences are vague or overqualified; I have indicated examples in the annotated manuscript and tried to help by lightly editing the Abstract. Other specific comments are there too.

If these issues are addressed thoroughly, the manuscript could become a useful reference for peatland and soil spectroscopy research, providing a valuable peat-specific spectral baseline that other groups can evaluate and localise for their own applications.

Raphael VISCARRA ROSSEL
EE, SOIL.

Comments to the Author: PDF

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AR by Henning Teickner on behalf of the Authors (29 Mar 2026) Author's response Manuscript

Journal article(s) based on this preprint

27 Apr 2026

Prediction of peat properties from transmission mid-infrared spectra

Henning Teickner and Klaus-Holger Knorr

SOIL, 12, 497–519, https://doi.org/10.5194/soil-12-497-2026,https://doi.org/10.5194/soil-12-497-2026, 2026

Short summary

Henning Teickner and Klaus-Holger Knorr

Supplement

https://doi.org/10.5194/egusphere-2025-4955-supplement

Data sets

Peatland Mid-Infrared Database (1.0.0) H. Teickner et al. https://doi.org/10.5281/zenodo.17092587

Gap-filled subset of the Peatland Mid-Infrared Database (1.0.0) H. Teickner and K.-H. Knorr https://doi.org/10.5281/zenodo.17187559

Model code and software

Compendium of R code and data for "Prediction of Peat Properties from Transmission Mid-Infrared Spectra" H. Teickner and K.-H. Knorr https://doi.org/10.5281/zenodo.17209177

irpeatmodels: Mid-infrared prediction models for peat H. Teickner https://doi.org/10.5281/zenodo.17187912

irpeat 0.3.0: Functions to Analyze Mid-Infrared Spectra of Peat Samples H. Teickner and S. Hodgkins https://doi.org/10.5281/zenodo.17200517

Henning Teickner and Klaus-Holger Knorr

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Short summary

We developed models that predict physical and chemical peat properties from mid-infrared spectra (MIRS). These peat properties are necessary for modeling peatland dynamics. Compared to direct measurements of these properties, measurements of MIRS require less sample material and save time. Unlike existing models that focus on peat, the models developed here are openly available, relatively easy to use and have basic quality checks and estimates for prediction errors.


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