Capabilities of optical and radar Earth observation data for up-scaling methane emissions linked to subsidence and permafrost degradation in sub-Arctic peatlands
Abstract. Permafrost thaw in Arctic regions is increasing methane (CH4) emissions to the atmosphere but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth Observation (EO) data are critical for assessing both permafrost thaw, associated ecosystem change, and increased CH4 emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider – that landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions and the difficulty in detecting the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high resolution unoccupied aerial vehicle (UAV) data, together with Sentinel-1 and -2 data to extrapolate field measurements of CH4 emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high resolution UAV data can map spatial variation in vegetation relevant to variation in CH4 emissions and extrapolate these across the wider landscape. We further show how Sentinel-1 and Sentinel-2 can be used. By way of a soft classification, and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH4 emissions matched closely that generated using the UAV data. InSAR assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH4 emissions. We estimate that a transition of an area currently experiencing subsidence to fen type vegetation are estimated to increase emissions from 116 kg CH4 season−1 from the 50 ha study area, to emissions as high as 6500 to 13000 kg CH4 season−1. The key outcome from this study is that a fusion of EO data types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types and vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 from the Arctic over space and time with confidence.
Sofie Sjogersten et al.
Status: open (until 17 Apr 2023)
- RC1: 'Comment on bg-2023-17', Anonymous Referee #1, 09 Mar 2023 reply
Sofie Sjogersten et al.
Sofie Sjogersten et al.
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The authors present an interesting and novel investigation that attempts to establish a new method for estimating landscape-scale methane emissions from degrading palsa wetlands and for detecting the initial stages of palsa degradation, which represent two urgently pressing issues in ongoing permafrost peatland research. The study combines ultra-high resolution imagery from a UAV with Sentinel-1 and -2 data to extrapolate field-measured methane emissions to the landscape-scale, using relationships with key vegetation types. The authors estimate that for the 50 ha study area, ongoing degradation could increase methane emissions by up to two orders of magnitude, indicating the importance of such observations and measurements for estimating carbon release from palsa sites. The distinction between vegetation types for estimation of CH4 emission rates is much needed for improving our understanding of degradation impacts, as many current studies use more simplistic comparisons between palsas and fens, as the authors note. The introduction, methods and discussion are generally well-written and interesting. I note some points for consideration below.
However, some further work is required on the analysis and presentation of the measured data before this manuscript is ready for publication. Specifically:
Overall, this research is a novel and useful contribution to the field of permafrost peatland research, but currently requires some polish to its presentation to be ready for publication.
L29: It is very unclear both here in the abstract, and in the main text, how the results for future CH4 emissions have been reached. As this is the headline finding of the paper, it is important that further clarification is provided for the methods used to calculate these statistics, and some interpretation in the main text for why there is such a large range between these figures.
L41–43: Although the study’s focus is on methane, permafrost thaw can also cause substantial carbon dioxide (CO2) release. It would be useful in the introduction to include some comparison of the relative warming potential or persistence of methane compared to CO2.
L44-46: Given the current limited mapping of palsas across much of Siberia (e.g. see Fewster et al., 2022), I don't believe there is enough evidence to state a confident areal extent of palsa peatlands across the total permafrost region. Instead, perhaps it would be better to state geographic regions in which they are most commonly found.
L46: Tarnocai et al. (2009) do not discuss palsa extents so this reference is unsuitable for making this point.
L46-47: These references for peat carbon stocks do not provide estimates for the total carbon stored specifically in palsa mires. Both studies are now > 10 years old and peat carbon estimates been more recently been improved. It seems more appropriate to describe the permafrost peatland carbon store more generally, using updated carbon maps – for example, see Hugelius et al. (2020) (https://doi.org/10.1073/pnas.1916387117).
L107: What reference data were used to calculate these MAT and MAP ranges (i.e. weather station observations or gridded climatologies)? A reference to this climate data is important. Please also provide specifics on the time period considered.
L130: Figure 1: I would recommend revising the colour scheme used in Figure 1, because the vibrant green and red classes are not colourblind friendly and it is difficult to distinguish the lake shoreline from the terrestrial green classes.
L135-136: This sentence is slightly unclear to me. Were these measurements taken from intact palsa tops? Was there a reason why all vegetation types not studied in both subsiding and intact areas?
L236-237: How variable were the measured methane emissions within each landcover type? Do the results change substantially if an alternative averaging method is used, e.g. the median?
L236-237: When considering the wider applications of these methods, were the methane measurements from these land covers similar or dissimilar to existing measurements from nearby palsa mires? Do sites require ground truthing of methane measurements before such methods can be employed? Some further comparisons to previous measurements in the discussion would be interesting and useful.
L278-280: It is unclear what temperature is being measured here – is this soil temperature? Please clarify.
L285: Figure 2: This figure requires revision. Panel D is missing any data bars. Standard error whiskers only extend above each bar, but should also extend below. For panel C I would recommend the removal of the dry lichen sub. class as it currently reads as no methane flux, when actually no measurements were recorded. In the caption, it is important to clarify where the means and SE are shown (e.g. with bars and whiskers).
L295: Figure 3: Recommend offsetting each data point slightly in the horizontal axis to avoid overwriting.
L399: Figure 4: Additional information is needed in the caption, detailing what values are shown by boxplot centre lines (medians?), whiskers and closed circles.
L309: Was there an objective cutoff point for determining a suitable accuracy? How do these performance results compare to previous classification studies?
L330: Does the misclassification of several raised palsa areas in the study area indicate that there may also be an overestimate of predicted emissions in extrapolated results?
L375: Table 3: The caption seems to indicate that four sets of results should be in the table (i.e. the “one model using the UAV data” appears to be missing). The mean and SE should be labelled in the table.
L383-385: If such a strong bias-correction is required to correct the results for the trained study site, are these methods really suitable for estimating classifications outside of the study domain, where bias-correction would be more difficult without ground-truthed data? Perhaps this evaluation is worth greater discussion.
L388-389: It is suprising that the hard classification still returned a highly significant correlation to the UAV estimates when the R2 value is much lower than the other relationships. Does this indicate the low numbers of point estimates (four per model) are unsuitable for significance testing?
L396: Here it would be useful to indicate a range and average for the measured subsidence in mm.
L413-414: Figure 7 does not have any lettered panels. Should this relate to Figure 9?
L428-430: This section is very vague, and appears to represent a headline finding for the study. Please clarify in greater detail the vegetation types used to estimate the future emissions, and how these calculations were applied. I did not follow the calculation of these estimates.
L433: Figure 9: The lettered panel labels are very hard to read. North arrows were only present in one figure panel – please add to all panels.
L439-441: How comparable are these estimates? If possible, it would be helpful to report a range of estimated values from these studies.
L449-453: Similarly, it would be useful to report the ranges to show readers how close these estimates are to the upper end.
L477: Is this referring to the bias-corrected hard classification? It would be helpful to use consistent terms throughout.
L482-484: How easily available are high-resolution remote sensing data for permafrost peatland regions (e.g. central and eastern Siberia are currently very poorly studied)? Some discussion of the availability of these data would contextualise how easy these methods would be to upscale.
L56: “which have been”.
L106: add degree symbol.
L115: Remove additional bracket.
L146: consider removing “point”?
L280: degree symbol missing.
L314: Should this read “shadows are not included”?
L316: missing symbol before “Sites”
L342: change “methane” to CH4?
L344: change “has” to “have”
L372: change “were” to “where”
L447: change “were” to “where”
L477: “Hence improved understanding of the..”