the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography
Jérôme Messmer
Alexander R. Groos
Abstract. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate information on small-scale variations in debris thickness and sub-debris ice melt rates is only available for a few locations worldwide. Here we present a customised low-cost UAV for high-resolution thermal imaging of mountain glaciers and a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a thermal orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the raw thermal orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The corrected thermal orhthophoto reflects spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using in-situ measurements in conjuction with an empirical or inverse surface energy balance model that relates surface temperature to debris thickness. Our results from a small-scale experiment on the Kanderfirn in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼0 °C) was 0.4 ±1.0 °C. The root-mean-square error of the modelled debris thickness was 1.2 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for glacier-wide high-resolution debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.
Jérôme Messmer and Alexander R. Groos
Status: open (until 16 Jun 2023)
-
RC1: 'Comment on tc-2023-41', Sam Herreid, 22 May 2023
reply
The article “A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography” by Messmer and Groos provides an approach to bypass proprietary software to derive accurate surface temperature measurements and solve for debris thickness. The field experiment was thoughtfully set up and the article was a pleasure to read. It goes slightly against the title that the bulk of the analysis was conducted on data processed with proprietary software. This paper is an addition to a growing set of similar papers (mostly well cited within) which support the underlying premise of a coupled signal between surface temperature and debris thickness. This study, however, conveniently avoids the trickier aspects of this line of research by limiting the study site to a location where debris cover is generally thin, <15 cm, where the signal is expected to be strong. The spatial domain of these similar papers is also seemingly stuck to a small swath of a small glacier. Here, the authors put forth some good suggestions on how to break out of this limited domain in a UAV context, but they do not explore them. To further focus the paper, I think the discussion section could be shortened / organized with more subheaders. I also think it would improve the impact of the paper to add a specific discussion section on scaling the method and method repeatability. The in-line comments below are mostly minor.
Abstract
L4: Perhaps less accurate and lower resolution, but it seems a little remiss to not mention Rounce et al., 2021 re: a global map of debris cover.
L5: Maybe it’s “describe” the customization of a low cost UAV and “present” a complete open source..
L8: Is it still raw though? I assume during the SfM step there was some statistical resampling. I’m fine if you still call it raw, but to me that is strictly the values of the individual radiometric images.
L9: provided the dt spanning the set of images is reasonably low. I’m sure you say this in the text.
L13: using which method, empirical or inverse EB? Or both?
L15: is this still for a 0-15 cm class? Or for the whole DC area?
L17: “paves the way” Does it? I’m not sure when we’ll have regional, not to mention glacier-wide, UAV coverage and moving up to fixed wing or helicopter increases the cost by quite a bit.
Introduction
L44: Rounce et al., 2023 seems to temper this urgency, at least in terms of global sea level predictions. “The inclusion of debris thus delays mass loss over the century especially at local scales but has little impact on sea level rise and the number of glaciers lost by 2100.” Regionally, and for addressing problems on shorter timescales, the urgency seems to remain warranted.
L53: Consider saying more about the low accuracy since this is key motivation for finescale work such as this. You can more explicitly constrain “low accuracy” with repeatability arguments and the often stated ~0.5 m detection limit. From Rounce et al. 2021, we now know where we can anticipate this limit to be met.
L55: Higher resolution mapping techniques are required to do what? Brute force solve the problem with e.g. aerial surveys, or inform satellite based methodology either with sensors in orbit today or wait for civilian satellite surface temperature data to become higher resolution?
L59: See also:
Gök, Deniz Tobias, Dirk Scherler, and Leif Stefan Anderson. "High-resolution debris-cover mapping using UAV-derived thermal imagery: limits and opportunities." The Cryosphere 17.3 (2023): 1165-1184.
Aubry-Wake, Caroline, et al. "Using ground-based thermal imagery to estimate debris thickness over glacial ice: fieldwork considerations to improve the effectiveness." Journal of Glaciology 69.274 (2023): 353-369.
L61: The nadir camera angle is without a doubt a benefit, and a UAV can capture a wider swath of a glacier. But still, the narrow swaths of high resolution thermal data presented here and in all other similar studies I have seen, the spatial coverage is not operative for studies beyond proof of concept. Further, there are radiometric resolution trade-offs between a heavy ground based camera and those that meet the payload of a UAV. I’m not making an argument for more ground based studies, I’m just saying proof of concept studies might benefit from the better (heavier) sensors and the spatial gains from today’s UAVs are not orders of magnitude better.
L66 Or hinting at its futility? For a method to be robust, the cannon of similar papers will need to all start converging on good news and repeatability or the general concept should probably be sidelined.
L69: I assume it would work on thermal imagery acquired by any means?
L73: debris [surface] temperature.
Study Area
L87: The debris-covered area of the Kanderfirn is already very small, on a bigger debris covered glacier, say, the terminus of Bering Glacier in Alaska as an extreme, how would you decide where to survey? Is there any path for scalability with this method?
Data and Methods
L117: Recommended in the literature or from this study?
Table 1: The smaller TIR area surveyed from the same flight must mean that the overlap was less than for the visible imagery? This difference is due to the view angle? Is the 75% from L118 for RBG or TIR?
L139: Literally random, as in picked by an algorithm before going into the field, or just random sampling while you were in the field?
L148: I’m not sure if it was the correct decision to put the temperature loggers under a rock to shield it from direct radiation since the thermal camera will be measuring the skin temperature and thus include that very warm signal. It’s not unusual to measure 30 degree debris surface temperature on a cool but sunny summer day (as you know from your Fig. 10). It helps a little bit that it was slightly overcast (L113), Herreid, 2021 found overcast conditions to be preferable for TIR / debris thickness / sub-debris melt measurements. Studies like this one that look at a debris cover internal temperature profile along with contact thermistor and TIR surface temperature measurements show smooth and predictable diurnal signals just below the surface and lower (below your shale stone) and more chaotic signals for both TIR and contact thermistors at the very surface (chaotic as a function of clouds passing). Both have the scientific uses, I think in your case the skin measurement is correct, but what you collected should still be close.
L150: If you’re comparing a ground point measurement to a single thermal pixel at this location and the thermal pixel has a resolution of centimeters, it might be good to take a mean surface temp around the location or move the ribbon off a set distance since it will have a different emissivity to the surrounding rock.
L151: How long did you leave the sensors running before the survey? At the surface it should be pretty quick, but the sensors need to become isothermal with their environment.
L159: A few km in the mountains can make a lot of difference, e.g. RH in Table 2. Your final calculations might not be so different with a local sensor, but I think it would be following best practices for a small scale proof of concept study to at least collect local air temperature and RH measurements.
FIg 4. Should “Ice-snow mask” also include debris (L170)? Was this a manual or automated step?
Fig 4. It’s a little confusing if the SfM-MVS steps are open-source or not? L200-214 makes it sound like you used proprietary software but found open source alternatives but didn’t find them suitable for your study? I can completely understand how these decisions happen and end up a bit confusing in text form. However, if there is a simple notebook workflow from start to finish, as Fig. 4 suggests, it would be most clear if this was the workflow used, and then you could have a stand alone validation section with comparisons to common proprietary software packages.
L198: I don’t actually see this step in Fig 4. I guess you are referencing only the open-source pipeline but it reads like the validation step is there.
L198: Already stated in L172
L240: It’s not exactly clear to me where the coefficients came from, from the FLIR algorithm or from a different study or from a post processing step, in which case there should be many for each raw image or only one set from the orthoimage?
Equation 2, 3: can you give the citation again for these? Is it all Tattersall (2021a)?
Equation 4: I don’t understand why you take the square of the correction factor?
L255: I’m not so sure if the GCPs meet the criteria, the motivation for the crinkle is to capture surfaces normal to all different angles, in this case I think you’re getting almost exclusively a signal from the upper atmosphere or overhead clouds which will be quite a bit colder than reflective temperature from, say, a valley wall or nearby moraine. Especially if only extracting a central point. I think this is also somewhat of a localized measurement problem, e.g. if someone is having a bonfire just off frame, that heat will “bleed” into neighboring pixels. Do you know what, at your scale, the source of this correction is accounting for?
Table 3: Why do you differentiate between snow and ice if you apply the same emissivity?
L282: Was there snow in your study area? Last year’s or fresh?
L302-308: I think it’s fine to have the context here but could move to results or discussion.
L310: “can be”, but within some well known limitations. Time of day, e.g. if all surfaces go isothermal at night, or signal decoupling from thick debris.
L316: Add citation to statement.
Figure 5: Add n= to each class; maybe stylistic, but I would continue the curve through the origin since 0 h_d will theoretically pass 0C.
L327: Probably best stated with results, putting them into context.
L355: Thermal diffusivity is at least straightforward to solve for from field data and common in debris cover research. I thought the idea behind your page worth of equations is that there isn’t a tuning parameter? Maybe say here that you limited k to realistic values. Unless I’m misreading the purpose of Table 5, add the range/set of values of k rather than just one. Is 1 the best performing that you tried? What did Evatt et al., 2015 use? What metric did you optimize on?
Results
L360: Debris thickness measurements are always a little subjective depending on where you measure. Somewhere, maybe study area section, could you say some qualitative observations of your measurements e.g. in debris less than 1 cm there were fines that still covered the surface or single clasts at the measured debris thickness and bare ice visible between clasts. To those familiar with these field measurements we’ll know if this has the potential to have a higher melt rate that bare ice (in the case of the fines) or about the same as bare ice (if it’s just stones doing a small scale version of the ice pedestal process).
L360: Maybe “in diameter”, of course it’s clear as it is, but sounds a little funny.
L362: [were] not measured or mapped.
Figure 6: I think “modeled” not “mapped” on x-axis
Figure 6: The problem with Fig. 6 is that we look to measured for what the distribution should be, but actually the other two are probably more “true” because they are more inclusive. I think you need to add two more: emp and phy with only intersecting points to measured.
Figure 7: Same comment as above but compounded by being fundamentally different quantities. Still nice that the shape is similar, but are any of your readers skeptical about the physics behind a thermal image? I’m not 100% sure I see the point of this figure. I think there is no good reason to leave the GCP points in the fig.
Figure 8: This figure seems mostly unnecessary and could be merged with Fig. 1. Maybe others feel differently, but I don’t get any new information from seeing a raw DSM, the glacier outline is a pretty unrecognizable shape, and I think most specialists reading the paper will take you at your word about high resolution data resolving boulders, debris and ice cliffs/crevasses.
L376-385: Move to Study Area section
L384: give fig ref with labeled “M”
L384: I have never heard of supraglacial moraines having a lower tail. I think what you mean is a medial moraine structure is crosscut (maybe add: and dispersed) by surface water flowing orthogonal to ice flow. Semantically, all of the rock in your study area is supraglacial moraine.
L389-390: I don’t recall this being a research question raised in the introduction, and I’m not sure if it needs much attention. I would say a network of field based Ts measurements to evaluate the signal of a medium resolution satellite thermal pixel addresses a scientific need, but here I think I trust your drone images to measure Ts more than contact thermistors below the surface. I would flip the axes of Fig 9 to say you can use T below the rock to approximate Ts :)
L399: “More interesting” but in a supplemental figure :) (I’m not saying change it)
L403: It’s important and good that you used both proprietary and open source software, but slightly concerning that you preference the proprietary while open source is a main contribution of this work.
Figure 10: I thought emissivity was segmented for the different surfaces?
Figure 10: Is there a mechanism for sub freezing temperatures or is that a processing error?
L404: Notably the snow patch is lower than 0C and what looks like depressions near the bare ice. Any idea if this is signal or error? Maybe places to suggest putting T ground control for future studies?
L405: I think more technically it is atmospheric temperature not surface temperature. The actual surface temperature of the aluminum is near isothermal with the ground.
L408: What is reporting the average temperature of a random glacier patch for a random time good for? What is a reader learning?
Figure 11: Do you think it's a coincidence that these very cold areas are on the edges of the spatial domain? Maybe a surface resampling residual? Although that seems unlikely with such dense data. Camera angle / vignetting? I think that might be a problem with smaller thermal cameras vs bigger handheld ones. Can you look at the actual raw thermal tiles and see if they also have that cold signal? What’s tripping me up is the surface stream that seems to go cold warm cold, but there is some cross cutting. Could you do an oblique 3D of this so we can see the gradients? The forcings on the ground should be pretty straight forward, 0C water and protection from incoming solar. The change in color of the rock in Fig. 8 at the bottom makes me think it could be a rock wetness / emissivity difference, not so clear about higher/northern cold spots.
L419: That’s fine to mention the camera detection limit, but there’s a difference between random error and a systematic shift. For your empirical relation this is not really relevant but I think the absolute Ts is important for the EB model.
L338: Earlier it was not called a sensitivity experiment, and here it’s unclear half way through the sentence. There’s a difference between a sensitivity analysis and model calibration.
L338: Consider subsections between empirical and EB results, it’s not clear going straight into k that this is a build for EB results.
L440: Point to where in FIg 13 we learn the “very sensitive”
L440: What does “spread” mean? The RMSE?
L451: Nice result. Maybe also speak to repeatability / calibration here if there isn’t a more in depth section in the discussion.
Discussion
L454: I’m not recalling any explicit mention of the cost?
L459: Trading for an easily acquired time-series.
L469: I wouldn’t say “defacto infeasible”, just a problem needing an innovative solution.
L475: Thermal equilibrium with what? Do you mean a single temperature throughout? Your method itself depends on a thermal gradient within the debris layer.
L530: Why unclear? Within constrained confidence limits, the measurement is not much different from any other that is generally collected without redundancy.
L539-548: This paragraph seems to be repeating established ideas and results, consider cutting.
L548: This study is limited to debris thicknesses that are known to be detectable with thermal imagery. I think the key questions for a promising debris thickness mapping technique are centered around thicker debris, so this statement seems too strong to me given the scope of the study. As Ts climbs the upper limb of the Eq in Fig 5, small variations in Ts produce bigger errors in debris thickness. This study, at least the empirical portion, is conveniently distant from the approaching asymptotic behavior.
L555: Why, because it has a slightly more similar shape than the wider emp? Per my earlier comment, I don’t think these plots are directly comparable.
L591: To be fair, Kanderfirn is a tiny glacier with almost no debris cover. That the global dataset caught it at all seems like a very positive review.
L595-597: Now talking about sub-debris melt seems a little out of place from the bulk discussion of the paper.
L597: I think your study domain would cover 6 ASTER thermal pixels. It’s not the most encouraging advice to say this study would have to be repeated maybe 10 times to cover a more traditionally extensive debris cover and evaluate satellite data.
Conclusions
L603: I don’t really see any “[paving] the way for glacier-wide high-resolution debris thickness mapping” in this study. The following sentence provides some ideas to overcome the 10 minute drone flight limit, but these are not explored here. The discussion is dismissive of using reanalysis data (Gok et al., 2022) but doesn’t put forth an alternative scaling method other than deploying more met stations on glaciers.
L607: ODM
L613: I’m not sure I agree this is the most important next step, these thermal data seem quite good and fairly well constrained. I think effort now needs to be focused on thicker debris, repeatability, and wider coverage.
L615: Can you state some statistical measures of performance for the two methods here and in the abstract? It would be useful for the reader to see right away how different the end results are of the two methods.
References
Rounce, David R., et al. "Distributed global debris thickness estimates reveal debris significantly impacts glacier mass balance." Geophysical Research Letters 48.8 (2021): e2020GL091311.
Rounce, David R., et al. "Global glacier change in the 21st century: Every increase in temperature matters." Science 379.6627 (2023): 78-83.
Citation: https://doi.org/10.5194/tc-2023-41-RC1 -
RC2: 'Comment on tc-2023-41', Anonymous Referee #2, 10 Jun 2023
reply
The main strength of the paper is to present an open-source pipeline for the processing of the TIR imagery, which has been an ongoing concern when using the black box, proprietary software to extract temperatures from TIR images. The paper is straightforward and easy to read, and it is well-placed within the existing literature that relates debris-thickness and surface temperature. I enjoyed reading it.
I think the manuscript would benefit from a more candid assessment of the performance of their temperature maps, which give results for the snow/ice surface temperatures that seem to have a strong spatially consistent bias, and the applications of both the empirical model and the energy-balance model given the limitations of the input data. Applying these methods is not a straightforward process, which is discussed qualitatively in the paper, but only in general terms. In my opinion, a more quantitative assessment of the model sensitivities would be beneficial.
Another component that is not much discussed in the paper, but I think should be added, is suggestions on how to upscale this method to a larger domain, considering the limited area tested here, and the possible complications in areas with thicker debris, as the maximum thickness here is 15 cm.
L9: typo: orthophoto
L9: I suggest you mention that you calibrate the energy-balance approach “with an empirical or calibrated inverse surface energy balance”.
L84: Could you give the elevation (and elevation range) of the study area, as well as the same characteristics like slope, aspect (which influence the energy-balance application)
L117: A strength here is that the flights were so short that it is unlikely that there was a significant change in surface temperature during that time, but it could still have happened. I would like to see somewhere (likely discussion) some mention of possible biases caused by changes in surface temperature during the UAV surveys, especially when it comes to aiming to do longer flights to cover larger areas. This could be made worst in partly overcast weather if cloud movement is occurring rapidly, casting changing shadows, or if flights occur late afternoon or morning. Could you mention if the temperature varied between the flight time (did it warm up or cool down, or was air temperature stable?)
Table 1: Could you change the units from ha to m2 or km2 (as in the text, L119, or at least give the conversion between ha and m2?)
L146: Could you point to the figure comparing measured surface temp to UAV-corrected temperature?
L156: few – can you give an actual number?
Table 2, and elsewhere: It would be interesting to read a bit more about the uncertainties linked with using such estimates from other locations to derive the energy balance of this highly specific study site. There is a mismatch of complexity here, where you use estimates for the input to the energy-balance model, compared to the high-resolution data you use to derive the debris thickness. I think more information and discussion of the application of the energy-balance model would be interesting in the results or discussion section because it is not a trivial thing to obtain these results.
L174: Could you have aimed for lower overlap and achieved a longer flight to cover a larger area? Could this be a suggestion for other studies? (similar to L192)
L209-211: I disagree with this point. The main novel aspect of this paper is presenting an open processing for UAV-based debris thickness but then you don’t use the open access too and instead used Pix4D. I think that you should have used only WebODM for the UAV visual UAV instead of using the pix4d if you were only going to use one version. Also, I suggest moving this sentence to the end of the paragraph to avoid talking about thermal, then visual, then thermal again and avoid confusion.
L218: according? Do you mean corresponding? Would radiation be radiative?
L254: This is similar to the approach discussed in Baker et al (2019)?
Baker, EA, Lautz LK, McKenzie JM and Aubry-Wake C (2019) Improving the accuracy of time-lapse thermal infrared imaging for hydrologic applications. Journal of Hydrology 571,60 – 70. doi:10.1016/j.jhydrol.2019.01.053 https://doi.org/10.1016/j.jhydrol.2019.01.053
Table 4. Any suggestion on how/why they are so variable over such a small area, and for a measurement that occurred all at the same time? What kind of bias occurred if you take the average value for reflected temperature when it is obviously very variable? Was there a spatial pattern to reflect temperature, and could you create a distributed field of reflected apparent temperature?
Figure 5: Could you have a different symbol for the training and validation? It’s not the easiest to differentiate them at the moment with the shades of grey.
L355: You have measured debris thickness and surface temperature from the empirical approach (and near-surface from the in-situ small sensors). Could you calculate keff instead of calibrating it? What kind of values would you get if you tried to derive them from the measurement instead?
L297: To increase the validity of your approach, you could remove these pixels that you know are not valued by creating a different mask that removed the location of the rocks an
L361: These scattered boulders – did you remove them from your analysis (removed from your temperature maps) to calculate the debris thickness field? You should probably not use your empirical equation beyond the bounds of the measurements that were used to create your empirical fit, as these modelled thicknesses above 13 cm are extrapolated and not well constrained at all. It looks like you don’t have much-modelled thickness above 13cm for the empirical approach, so it might not be a big issue in this case, but something to be careful about.
Fig 7: I think debris temperature should be before debris thickness in the results, as it is an analysis step that comes before – the measured and mapped temperature influences the modelled thickness, not the other way around. Also, instead, of having outliers in the results that are artifacts of the methods, I think these outliers should be removed from the image by designing a mask that does not include them. This figure presents the debris temperature, so it would be appropriate to remove the GCP from the results.
Fig 8. The DSM inset could be called (e) for clarity. In the legend, you set the crevasse and ice cliff as the same feature. Is it the same feature that you refer to, or a different part of the subset image? Can you add what m stands for in the legend/caption?
L376: How large is it in m x m?
L380: Could you add like Stream 1 and Stream 2 on the fig 8m, like you tag the moraine location?
Section 4.4.: It would in good to see if these numbers for the difference in surface temperature are similar to those in other studies that find a bias between TIR and in-situ debris temperature in the discussion section. Is it even a useful way to assess if TIR imagery is correct as they are measuring different things?
L395: The 162 image number is already mentioned in L194.
L400: A bit contradictory to mention that it is interesting but then put it in the supplementary.
Fig 10-11: I suggest you mask the section of the mages that should not be considered with an emissivity of 0.97. I suggest you segment the cover type (debris, ice) and only show the temperature for the section of the image where the result is valid (where it uses the proper emissivity). only sow the debris are
L403: Similar to the processing of the orthophoto, I think it is misleading to have this paper about an open-access pipeline but then use the result from the proprietary software in the results. I think it would be much more interesting to showcase the result with the open source, and then we could also see the processing artifacts that are mentioned above.
L405: I suggest removing this. This is an artifact of the processing that should not be considered a result.
L408 : given the actual number instead of “about 11”
Fig 11, L422-425: I find this result quite concerning. Some patterns make sense: the warmer margin for snow/ice temperature near the debris (a,b,c), but others, such as the snow patch of h-g going from ~+1 to -4 (and likely more if it wasn’t masked?), and the strong gradient in temperature between the edge and middle of the image around (c), going from -4 to +4, to -4 over ~150m. To me, these look like edge effects in the processing and suggest that only the middle third of your image is valid. If there is a reason to think that these distributed temperatures are valid, it should be explained. If these are edge effects, then the image should be segmented to only keep the middle section and remove these weird gradients. You mention these artifacts in L422, but then the next sentence states that they perform well enough, and I do not agree with that statement.
Could the relatively good average (0.4C, 0.3C) be linked to the fact that the errors are centred on 0 and so it gives a good average, when in fact it is quite spread out? Could you add information on how you define the +/- for the uncertainty of your numbers? Could you show the distribution of the ice and snow temperature in Figure 7, in addition to the map of the whole area (maybe even show both the snow/ice segmented distribution and the debris mask-only distribution?)
My understanding and experience is also that thermal infrared cameras can be quite accurate from pixel to pixel within one image, but can be quite off in terms of absolute temperature. It makes me more cautious about these spatial patterns in the temperature of the ice surface. I understand that the camera accuracy says +5 to -5, but that covers a much too large range and really limits the possibility to investigate TIR use for glacier melt, where a much smaller temperature range has large consequences for melt.
Fig 11: Also, maybe put a dotted box around the area that is used for the quantitative assessment – is that the area near where the temperature artifact is in (g)?
L429: couple millimetres -> use the actual number?
L431: relatively thick -> How thick?
L435: Could they be linked to wetness level, which would influence the conductivity and the surface temperature, instead of thickness?
L440: Have you tested how other parameters are sensitive in the model? Can you justify calibrating this one instead of a selection of other parameters?
L440: I find it interesting that you use a fairly complex inverse energy-balance approach, but then calibrate it with one parameter to fit the data. The other component of that model is also highly uncertain – meteorology, albedo, etc, are all very specific to the study area, and potentially even variable throughout your study area. It would be interesting to hear more about how suitable it is to use spatially homogenous input to the model when you are looking at a variable terrain. You mention this very briefly in L445, but maybe you could elaborate slightly more in the discussion.
L470: But, if you have a fixed wing that can flight longer and further, you are likely to be able to find a patch or snow or smooth meadow where you can land adjacent to the glacier…. Another limitation to uav is that they are realy bulking to hike in to remote sites.
Figure 15: I don’t think this figure is needed as you have the RMSE values on fig 13.
L476: Aubry-Wake et al., 2022 might be a helpful reference about the different factors that influence TIR acquisition for debris thickness measurements because the conclusions are very different – midday is not a good time to have a strong relationship between surface temperature and debris thickness, but you focus on very thin debris overall, so a different dataset!
Aubry-Wake, C., Lamontagne-Hallé, P., Baraër, M., McKenzie, J., & Pomeroy, J. (2023). Using ground-based thermal imagery to estimate debris thickness over glacial ice: Fieldwork considerations to improve the effectiveness. Journal of Glaciology, 69(274), 353-369. doi:10.1017/jog.2022.67
L486: A nuance that I think needs to be clarified here is that precise surface temperatures (that are consistent together) are needed for empirical thickness calculation, but for empirical calculation, the measurements do not need to be accurate. They could be biased, but as long as they are consistent, it works. However, for energy-balance approaches, you need both accurate and precise measurements of surface temperature.
L518: But those snow/ice temperature maps still show a large vignetting effect, so potentially these atmospheric corrections and reflected radiation are not enough to obtain precise and consistent temperature maps. These vignette effects have not been mentioned in ground-based measurements, so they might originate from the processing of the UAV images.
L530: This might be a good moment to point out that using small in-situ temperature sensors tucked in the debris, as you did, has a fairly limited use to assess the performance of UAV-based temperature.
L558: But even applying a site-specific empirical model can lead to erroneous debris thickness if the model is based on flawed data, like a bias in the sampling of the debris thickness, or a surface temperature that does not correlate well with debris thickness due to time of day, as discussed by Herreid (2022) and Aubry-Wake et al. (2023).
L559: But you do not account for spatial variation right? I appreciate that you discuss how it would be nicer to have in-situ measurements of meteorology, I would like a bit more quantitative analysis of this. For example, what kind of error or uncertainty are you erasing by calibrating keff? Is the model as sensitive to air temperature as it is keff? Applying an energy balance model to a small area like this, with high spatial heterogeneity, is hard to get right even with data coming from the site, so it would be good to have a bit more information on the sensitivity of the debris thickness to the model application.
Citation: https://doi.org/10.5194/tc-2023-41-RC2
Jérôme Messmer and Alexander R. Groos
Data sets
A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography (v1.0.0) [Data set] Jérôme Messmer and Alexander R. Grooos https://doi.org/10.5281/zenodo.7692542
Jérôme Messmer and Alexander R. Groos
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 276 | 117 | 11 | 404 | 7 | 7 |
- HTML: 276
- PDF: 117
- XML: 11
- Total: 404
- BibTeX: 7
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1