the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Apr 2022
Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions
Eliza Maher Hasselquist
Johan Stendahl
Mats B. Nilsson
Siddhartho S. Paul
Abstract. A critical tool to succeed in sustainable spatial planning is accurate and detailed maps. To meet the sustainable development goals and enable sustainable management and protection of peatlands, there is a strong need for improving the mapping of peatlands. Here we present a novel approach to identify peat soils based on a high-resolution digital soil moisture map that was produced by combining airborne laser scanning-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish landscape with high accuracy (Kappa = 0.69, MCC = 0.68). As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5 479 soil plots across the country) and the continuous SLU soil moisture map (R2 = 0.66, p < 0.001). We generated categorical maps of peat occurrence using three different definitions of peat (30, 40 and 50 cm thickness of the organic layer) and a continuous map of organic layer thickness. The predicted peat maps had a higher overall quality (MCC = 0.69–0.73) compared to traditional quaternary deposits maps (MCC = 0.65) and topographical maps (MCC = 0.61) and captured the peatlands with a recall of ca 80 % compared to 50–70 % on the traditional maps. The predicted peat maps identified more peatland area than previous maps, and the areal coverage estimates fell within the same order as upscaling estimates from national field surveys. Our method was able to identify smaller peatlands resulting in more accurate maps of peat soils, which was not restricted to only large peatlands visible from airplanes – the historical approach of mapping. Most importantly we also provided a continuous map of the organic layer, which ranged 6–95 cm organic layer thickness, with an R2 of 0.67 and RMSE of 19 cm. The continuous map exhibits a smooth transition of organic layers from mineral soil to peat soils and likely provides a more natural representation of the distribution of soils. The continuous map also provides an intuitive uncertainty estimate in the delineation of peat soils, critically useful for sustainable spatial planning, e.g. green-house gas or biodiversity inventories and landscape ecological research.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(2076 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Anneli M. Ågren et al.
Interactive discussion
Status: closed
-
EC1: 'Comment on egusphere-2022-79', David Dunkerley, 28 Jun 2022
This paper presents a study that sought to revise the mapping of peat soils at national scale across Sweden.
The 'Methods' section reports that the primary data used are detailed elevation data from airborne laser scanning (at 2 m resolution), and a computed soil moisture map previously developed by Ågren et al. The soil moisture map in turn relied on unspecified 'digital terrain indices' (ms. line 143-144), together with "ancillary data on quaternary deposits, soil depth, annual and seasonal runoff etc " (ms. line 145) that were used as input for a machine learning model, to predict soil moisture across Sweden. This section of the Methods presentation seemed inadequate to me. What were the topographic indices? How and at what scale were they derived? How were annual and seasonal runoff quantified, and what was the resolution and quality of these data? Runoff data can surely have been at no finer scale than that of catchment level, in most cases. If so, how can it assist in mapping peat at 2 m resolution? The authors need to explain much more thoroughly the data used and the methods used in the machine learning model. In turn, more commentary was needed on the resolution and quality of the soil moisture maps. What, for instance, is the extent of seasonal variability? Is the parameter calculated perhaps an annual mean or median value?
The predicted soil moisture data were then related to field-mapped peat depths collected from forestry surveys in which pits were excavated, and a regression model was fitted to the data. This is then used to predict peat thicknesses elsewhere across Sweden. However, the relationship between predicted organic layer thickness and measured thickness from the field survey data (Figure 4 in the ms.) shows enormous scatter. The bulk of the data points appear to be for quite thin organic layers (bottom left-hand corner of Fig 4), with relatively few observations > 60 cm (right hand part of Fig 4).
The authors do not actually describe the process of producing their predicted organic layer maps from the soil moisture data, but rather simply jump from Fig 3 to a discussion of the resulting maps. This needs to be corrected.
Given the enormous scatter in Fig 4, the authors at several places say that their thickness maps should not be 'taken literally' (e.g. line 460, line 466) and yet there is no real quantification of the probable magnitude of error at any location. This could have been done by comparing with the field data acquired from pits. The RMSE was reported as 19 cm (line 306) but this is a huge uncertainty given that most of the organic layers appear to be less than 20-30 cm in thickness. Is this level of uncertainty actually acceptable, and are the predicted depths sufficiently reliable for the estimation of carbon stocks, for instance?
Overall, I was left unsure about how much confidence could be placed in the thickness maps generated by the authors. I think that a fuller discussion of actual thicknesses and the likely uncertainty (surely varying with topographic position, and perhaps areal extent of particular organic or peat deposits) in the predictions is required. The authors claim excellent resolution in mapping peat deposits covering just 4 m2 (e.g. line 405) - i.e., just a single pixel in data at 2 m resolution. Do such tiny peat deposits actually exist? If so, what accounts for their isolated accumulation? The authors need to comment.
There are minor errors scattered throughout the ms. In particular, I would suggest that as a formal geological Period, 'Quaternary' should be capitalised. This is written 'quaternary' at many places in the ms., and all instances need correcting. The authors are occasionally inconsistent with this, such that Table 2 for instance contains 'Quaternary' as does the heading for Section 2.4, but elsewhere, mostly lower-case letters are used.
Citation: https://doi.org/10.5194/egusphere-2022-79-EC1 - AC1: 'Reply on EC1', Anneli Ågren, 27 Sep 2022
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RC1: 'Comment on egusphere-2022-79', Anonymous Referee #1, 16 Aug 2022
Ågren et al. map the spatial distribution of peat soils and organic layer thickness in Sweden using an existing soil moisture map and national-level field inventory data. The manuscript is well written and mostly sound, but I was left partly confused when reading the manuscript. I have the following major points
- Why did you predict thickness of organic layer based on soil moisture map and not from the original predictor variables that were used to produce the soil moisture map? This seems to be quite odd as there is now double uncertainty in the estimates, as the prediction of soil moisture was already a little uncertain. You should justify your approach better. It would be also interesting to compare different predictor variable sets (e.g. topography variables, satellite imagery etc.) to produce the thickness of organic layer and not just use one existing map.
- Based on Figure 4, the fit of the model between soil moisture and organic layer thickness is not very good. This should be discussed in more detail. Actually, there has been a lot of discussion that R2 should not be used for nonlinear regressions. You should justify why you evaluated model performance with R2 and you should also report how you calculated R2.
- Precision was lower in your map than in the topographic map. It seems that your mapping approach seems to overpredict peatlands, at least to some extent. This seems to be the case also when visually interpreting the material in Figs. 5 and A1 and looking at the information in Table 3. This should be accounted for and discussed in more detail. You could discuss e.g. why your approach seems to overpredict the extent of peatlands and overestimate thickness of thin organic layers. The overestimation of thickness of thin organic layers is probably due to the selected cubic model. You could potentially also use other (non-linear) regression models and discuss the pros and cons of different models.
Additionally, I have the following more detailed but mostly minor comments:
Abstract:
- Please remove the first sentence. It is not necessary.
- l17-19: it is not needed to report the results from an existing study. Please rephrase and shorten the sentence
- l25: please report also the precision results
- l28: “peatlands visible from airplanes” could be written “peatlands that can be visually detected from aerial imagery”
- l29: delete “most importantly”
Introduction:
- l80-87: the direct quote is unnecessarily long. Do you need to include it?
- l113: is the fourth objective necessary to include?
- l114: write “study provides a guide to map…”
Methods:
- l142-155: this paragraph could be shortened as it describes results from an earlier study, not the methods of this study
- Did you account for spatial autocorrelation when e.g., constructing the model and dividing the calibration and validation datasets?
- l195: This is difficult to understand. Does it mean that 1:25 000 map covers 1.7% of Sweden and so on?
- Why did you include the used accuracy metrics? Kappa has been heavily criticized (see e.g., https://doi.org/10.1016/j.rse.2019.111630). You could also have included F-score.
- Section 2.6: How were the field inventory datasets upscaled? Does this simply mean that you calculated national level statistics from the datasets using different methods?
Results
- the heading of 3.4 could be changed. Should it be “visual interpretation of peatland maps”?
Discussion and conclusions
- l375-376: This is misleading as you used ALS data very indirectly.
- l460: you write multiple times that the map should not be taken literally. It is not necessary to mention this multiple times.
- The section “The novelty of the developed maps” could be shortened and merged with conclusion section. Some text can also be moved to other parts of discussion.
- l509: delete “coarse”, “global mapping” is sufficient.
- l504-510: Sentinel-2 has 10 m resolution and it surely can be used for quite detailed planning. There is also other remote sensing than just ALS data that can be used in detailed planning.
Citation: https://doi.org/10.5194/egusphere-2022-79-RC1 - AC2: 'Reply on RC1', Anneli Ågren, 27 Sep 2022
-
RC2: 'Comment on egusphere-2022-79', Anonymous Referee #2, 23 Aug 2022
In this paper the authors present an approach for the identification of peat soils by fitting an empirical relationship between the thickness of the organic layer - measured across Sweden - and a continuous soil moisture map. The article is very interesting and quite well written, but I would prefer to see a bit more detailed description of the procedure followed by the authors.
English language and style are quite fine, only a minor spell check is required.
Most of the references are listed without specifying the number of pages. Please carefully check them, since other mistakes are present. All the references in other languages than English should be clearly indicated.
Other specific comments:
- Line 54: Sewell et al (2020) is not reported in the reference list.
- Line 142: please define the acronym SLU, since the readers could not be familiar with it.
- Line 148: please add references for these statistics.
- Line 387: Arrouays et al (2014) is not reported in the reference list.
- Line 388: Jackson et al (2017) is not reported in the reference list.
- Line 458: Nijp et al (2019) is not reported in the reference list.
I recommend to revise the article. Minor revision is necessary.
Citation: https://doi.org/10.5194/egusphere-2022-79-RC2 - AC3: 'Reply on RC2', Anneli Ågren, 27 Sep 2022
Interactive discussion
Status: closed
-
EC1: 'Comment on egusphere-2022-79', David Dunkerley, 28 Jun 2022
This paper presents a study that sought to revise the mapping of peat soils at national scale across Sweden.
The 'Methods' section reports that the primary data used are detailed elevation data from airborne laser scanning (at 2 m resolution), and a computed soil moisture map previously developed by Ågren et al. The soil moisture map in turn relied on unspecified 'digital terrain indices' (ms. line 143-144), together with "ancillary data on quaternary deposits, soil depth, annual and seasonal runoff etc " (ms. line 145) that were used as input for a machine learning model, to predict soil moisture across Sweden. This section of the Methods presentation seemed inadequate to me. What were the topographic indices? How and at what scale were they derived? How were annual and seasonal runoff quantified, and what was the resolution and quality of these data? Runoff data can surely have been at no finer scale than that of catchment level, in most cases. If so, how can it assist in mapping peat at 2 m resolution? The authors need to explain much more thoroughly the data used and the methods used in the machine learning model. In turn, more commentary was needed on the resolution and quality of the soil moisture maps. What, for instance, is the extent of seasonal variability? Is the parameter calculated perhaps an annual mean or median value?
The predicted soil moisture data were then related to field-mapped peat depths collected from forestry surveys in which pits were excavated, and a regression model was fitted to the data. This is then used to predict peat thicknesses elsewhere across Sweden. However, the relationship between predicted organic layer thickness and measured thickness from the field survey data (Figure 4 in the ms.) shows enormous scatter. The bulk of the data points appear to be for quite thin organic layers (bottom left-hand corner of Fig 4), with relatively few observations > 60 cm (right hand part of Fig 4).
The authors do not actually describe the process of producing their predicted organic layer maps from the soil moisture data, but rather simply jump from Fig 3 to a discussion of the resulting maps. This needs to be corrected.
Given the enormous scatter in Fig 4, the authors at several places say that their thickness maps should not be 'taken literally' (e.g. line 460, line 466) and yet there is no real quantification of the probable magnitude of error at any location. This could have been done by comparing with the field data acquired from pits. The RMSE was reported as 19 cm (line 306) but this is a huge uncertainty given that most of the organic layers appear to be less than 20-30 cm in thickness. Is this level of uncertainty actually acceptable, and are the predicted depths sufficiently reliable for the estimation of carbon stocks, for instance?
Overall, I was left unsure about how much confidence could be placed in the thickness maps generated by the authors. I think that a fuller discussion of actual thicknesses and the likely uncertainty (surely varying with topographic position, and perhaps areal extent of particular organic or peat deposits) in the predictions is required. The authors claim excellent resolution in mapping peat deposits covering just 4 m2 (e.g. line 405) - i.e., just a single pixel in data at 2 m resolution. Do such tiny peat deposits actually exist? If so, what accounts for their isolated accumulation? The authors need to comment.
There are minor errors scattered throughout the ms. In particular, I would suggest that as a formal geological Period, 'Quaternary' should be capitalised. This is written 'quaternary' at many places in the ms., and all instances need correcting. The authors are occasionally inconsistent with this, such that Table 2 for instance contains 'Quaternary' as does the heading for Section 2.4, but elsewhere, mostly lower-case letters are used.
Citation: https://doi.org/10.5194/egusphere-2022-79-EC1 - AC1: 'Reply on EC1', Anneli Ågren, 27 Sep 2022
-
RC1: 'Comment on egusphere-2022-79', Anonymous Referee #1, 16 Aug 2022
Ågren et al. map the spatial distribution of peat soils and organic layer thickness in Sweden using an existing soil moisture map and national-level field inventory data. The manuscript is well written and mostly sound, but I was left partly confused when reading the manuscript. I have the following major points
- Why did you predict thickness of organic layer based on soil moisture map and not from the original predictor variables that were used to produce the soil moisture map? This seems to be quite odd as there is now double uncertainty in the estimates, as the prediction of soil moisture was already a little uncertain. You should justify your approach better. It would be also interesting to compare different predictor variable sets (e.g. topography variables, satellite imagery etc.) to produce the thickness of organic layer and not just use one existing map.
- Based on Figure 4, the fit of the model between soil moisture and organic layer thickness is not very good. This should be discussed in more detail. Actually, there has been a lot of discussion that R2 should not be used for nonlinear regressions. You should justify why you evaluated model performance with R2 and you should also report how you calculated R2.
- Precision was lower in your map than in the topographic map. It seems that your mapping approach seems to overpredict peatlands, at least to some extent. This seems to be the case also when visually interpreting the material in Figs. 5 and A1 and looking at the information in Table 3. This should be accounted for and discussed in more detail. You could discuss e.g. why your approach seems to overpredict the extent of peatlands and overestimate thickness of thin organic layers. The overestimation of thickness of thin organic layers is probably due to the selected cubic model. You could potentially also use other (non-linear) regression models and discuss the pros and cons of different models.
Additionally, I have the following more detailed but mostly minor comments:
Abstract:
- Please remove the first sentence. It is not necessary.
- l17-19: it is not needed to report the results from an existing study. Please rephrase and shorten the sentence
- l25: please report also the precision results
- l28: “peatlands visible from airplanes” could be written “peatlands that can be visually detected from aerial imagery”
- l29: delete “most importantly”
Introduction:
- l80-87: the direct quote is unnecessarily long. Do you need to include it?
- l113: is the fourth objective necessary to include?
- l114: write “study provides a guide to map…”
Methods:
- l142-155: this paragraph could be shortened as it describes results from an earlier study, not the methods of this study
- Did you account for spatial autocorrelation when e.g., constructing the model and dividing the calibration and validation datasets?
- l195: This is difficult to understand. Does it mean that 1:25 000 map covers 1.7% of Sweden and so on?
- Why did you include the used accuracy metrics? Kappa has been heavily criticized (see e.g., https://doi.org/10.1016/j.rse.2019.111630). You could also have included F-score.
- Section 2.6: How were the field inventory datasets upscaled? Does this simply mean that you calculated national level statistics from the datasets using different methods?
Results
- the heading of 3.4 could be changed. Should it be “visual interpretation of peatland maps”?
Discussion and conclusions
- l375-376: This is misleading as you used ALS data very indirectly.
- l460: you write multiple times that the map should not be taken literally. It is not necessary to mention this multiple times.
- The section “The novelty of the developed maps” could be shortened and merged with conclusion section. Some text can also be moved to other parts of discussion.
- l509: delete “coarse”, “global mapping” is sufficient.
- l504-510: Sentinel-2 has 10 m resolution and it surely can be used for quite detailed planning. There is also other remote sensing than just ALS data that can be used in detailed planning.
Citation: https://doi.org/10.5194/egusphere-2022-79-RC1 - AC2: 'Reply on RC1', Anneli Ågren, 27 Sep 2022
-
RC2: 'Comment on egusphere-2022-79', Anonymous Referee #2, 23 Aug 2022
In this paper the authors present an approach for the identification of peat soils by fitting an empirical relationship between the thickness of the organic layer - measured across Sweden - and a continuous soil moisture map. The article is very interesting and quite well written, but I would prefer to see a bit more detailed description of the procedure followed by the authors.
English language and style are quite fine, only a minor spell check is required.
Most of the references are listed without specifying the number of pages. Please carefully check them, since other mistakes are present. All the references in other languages than English should be clearly indicated.
Other specific comments:
- Line 54: Sewell et al (2020) is not reported in the reference list.
- Line 142: please define the acronym SLU, since the readers could not be familiar with it.
- Line 148: please add references for these statistics.
- Line 387: Arrouays et al (2014) is not reported in the reference list.
- Line 388: Jackson et al (2017) is not reported in the reference list.
- Line 458: Nijp et al (2019) is not reported in the reference list.
I recommend to revise the article. Minor revision is necessary.
Citation: https://doi.org/10.5194/egusphere-2022-79-RC2 - AC3: 'Reply on RC2', Anneli Ågren, 27 Sep 2022
Peer review completion
Journal article(s) based on this preprint
Anneli M. Ågren et al.
Anneli M. Ågren et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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