the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 May 2024
A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
Abstract. Supraglacial lakes on the ice sheets have been linked to ice shelf collapse in Antarctica and accelerated flow of grounded ice in Greenland. However, it is difficult to quantify the impact of supraglacial lakes on ice dynamics accurately enough to predict their contribution to future mass loss and sea level rise. This is largely because ice-sheet-wide assessments of meltwater volumes rely on models that are poorly constrained due to a lack of accurate depth measurements. Various recent case studies have demonstrated that accurate supraglacial lake depths can be obtained from ICESat-2’s ATL03 photon-level data product. ATL03 comprises hundreds of terabytes of unstructured point cloud data, which has made it challenging to use this bathymetric capability at scale. Here, we present two new algorithms – Flat Lake and Underlying Ice Detection (FLUID) and Surface Removal and Robust Fit (SuRFF) – which together provide a fully automated and scalable method for lake detection and depth determination from ATL03 data, and establish a framework for its large-scale implementation using distributed high-throughput computing. We report FLUID/SuRFF algorithm performance over two regions known to have significant surface melt – Central West Greenland and Amery Ice Shelf catchment in East Antarctica – during two melt seasons. FLUID/SuRFF reveals a total of 1249 lakes up to 25 m deep, with more water during higher melt years. In absence of ground truth data, manual annotation of test data suggests that our method reliably detects melt lakes whenever a bathymetric signal is discernible, and estimates water depths with a mean absolute error of 0.28 m. These results imply that our proposed framework has the potential to generate a comprehensive data product of accurate meltwater depths across both ice sheets.
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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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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1156', Jennifer Arthur, 11 Jun 2024
This manuscript uses ICESat-2’s ATL03 altimetry product to develop two new algorithms which together provide a scalable framework for supraglacial lake detection and depth determination from ATL03 data.
Surface melt is an important, yet poorly constrained, component of ice-sheet surface mass balance, leading to surface meltwater accumulating as lakes on ice-shelf surfaces and on grounded ice. In Antarctica, this has been linked to the process of meltwater-driven hydrofracture, which can trigger rapid ice-shelf collapse. Accurately measuring supraglacial lake meltwater depths from satellite data is important due to the challenges in obtaining in situ measurements and is needed for modelling meltwater interactions with ice sheet dynamics. However, few studies have developed automated lake depth estimation methods that are scalable beyond small data subsets, and previous studies rely on methods with poorly constrained parameters and a lack of in situ measurements.
The authors apply their algorithm framework to two regions that experience high surface melt (central west Greenland and the Amery Ice Shelf) and are able to reliably detect lakes where lake bathymetry is visible. The methodology appears robust, and the algorithm performs well even for more complex lakes (especially in Antarctica), including thin, elongated lakes and those with patchy ice cover. The authors found 1249 lakes with their algorithm during four melt seasons and conclude that lake depths agree well with manually-picked lakebeds in ICESat-2 along-track segments.
Overall, it is my view that this study is of broad interest to the cryospheric community as it builds upon previous work focused on supraglacial lake depths on both ice sheets, especially in the context of ice-shelf surface hydrology and dynamics, by paving the way for developing pan-ice sheet supraglacial lake depth and volume products. I think the surface hydrology and ice shelf communities would be very interested to see this algorithm applied at an ice-sheet scale in future.
In general, this is a very well-written manuscript with detailed methods, clear figures and most of my comments are relatively minor. Once the authors address these, I can therefore recommend that this manuscript is suitable for publication in The Cryosphere.
Please see the attached for specific comments.
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AC1: 'Reply on RC1', Philipp Arndt, 23 Jul 2024
Thank you so much Jenny for taking the time to read and review our manuscript, and for your positive, thoughtful and constructive comments. In particular, we believe that your comments were instrumental in better presenting this manuscript within the context of the broader research community’s efforts to characterize surface hydrological processes and quantify meltwater on the ice sheets.
Please see the attachment for our full responses and suggested changes to the manuscript.
- AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
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AC1: 'Reply on RC1', Philipp Arndt, 23 Jul 2024
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RC2: 'Comment on egusphere-2024-1156', Ian Brown, 17 Jun 2024
This is an important contribution to our ability to monitor the supra-glacial hydrology of the ice sheets. It is a very well executed investigation and a well written manuscript. I have some small concerns regarding validation and some suggestions for minor edits.
Abstract.
The first sentence sets the tone so it is a little odd to address ice shelf collapse when that is not the focus of the article. Consider refocussing the opening sentence.Line 38. "Recently" is relative. ICESAT-2 has been in orbit for 5 years and readers may access the article in a decade meaning "recently" is not appropriate. Delete the word.
Line 205-206. Can you describe in more detail the empirical observations: how many were used to establish the thresholds?
Line 227. Does FLUID work as well in the presence of wind roughened water surfaces: I assume it does though perhaps fewer photons penetrate the surface. Please comment (as you specifically address the impact of wind on optical estimates of water depth on line 105).
Line 251. c is presumably the speed of light in a vaccuum or freshwater (line 434)? Please clarify.
Line 321. Define "a few".
Line 343-345. It would be useful to estimate the real number of lakes mapped if possible. If it is not possible please discuss this in the appropriate section of the manuscript.
Figure 8., Line 483. Consider moving figure 8 to the front of the manuscript, for example, at the end of the Introduction where you cite the study areas.
Section 4.1.1 (line 529-). The number of lakes detected over West Greenland is very small compared with other studies. Especially considering you measure over a season. Also, it is odd that so few lakes are detected in 2020-21 over the Amery ice shelf. The number of false negatives is presumably very high (i.e. your detection rate is low). Presumably this is a function of the ground track spacing of ICESAT-2. I think it is important to discuss this and the impact it would have on operational implementation of your algorithm (or the limits to that).
Line 550-551. Did you consider identifying lakes that have emptied. Johansson et al., (2013; J. Hydrol. 476) show that many lakes are transient and will empty late in season. This would allow you to evaluate the accuracy of the estimate from filled-emptied conditions.
Line 657. I think you need to mention there is a bias towards over-estimation.
Line 660. I do not think this is demonstrated given the very low numbers of detections and the fact that you can't identify whether multiple measurement lines are from the same lake.
Citation: https://doi.org/10.5194/egusphere-2024-1156-RC2 -
AC2: 'Reply on RC2', Philipp Arndt, 23 Jul 2024
Thank you so much Ian for taking the time to read and review our manuscript, and for your positive, thoughtful and constructive comments. In particular, we believe that your suggestions regarding a clearer distinction between ICESat-2 lake segments and unique “real” supraglacial lakes have helped to make the manuscript more clear, especially to readers who are not already very familiar with ICESat-2 data.
Please see the attachment for our full responses and suggested changes to the manuscript. - AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC2: 'Reply on RC2', Philipp Arndt, 23 Jul 2024
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CC1: 'Comment on egusphere-2024-1156', Bert Wouters, 26 Jun 2024
I agree with the two other reviewers that this is a well-written and important contribution, presenting an elegant method to derive supraglacial lake bathymetry. Nevertheless, I would like to comment on the two statements below, in the Introduction and Summary sections:
L47-53: Previous ICESat-2 studies have been limited to applying depth estimation methods to a handful of manually picked lakes or data granules, with no clear pathway to large-scale computational implementation across the ATL03 data catalog, which comprises hundreds of terabytes of unstructured point cloud data (Neumann et al., 2023b). To address this challenge, we have created a fully automated and scalable algorithm for lake detection and depth determination from ICESat-2 data.
L646-651: ICESat-2 data had not previously been used at scale for this purpose because its photon-level product comprises hundreds of terabytes of unstructured point cloud data along spatially discrete ground tracks, which makes it difficult to integrate the data with spatially continuous data in existing workflows. To address this challenge, we have presented the fully automated, two-step FLUID/SuRRF algorithm for the detection and depth determination of supraglacial lakes on the ice sheets in ICESat-2 photon data, and proposed a computational framework that allows for its large-scale implementation across any desired ice sheet drainage basins and melt seasons.
Whereas it is true that other methods have not been used at such a large scale as in the manuscript, the Watta algorithm (Datta and Wouters, 2021) is fully automated (i.e. it detects potential lake locations based on a flatness criterion and then estimates the bathymetry, similar to the framework presented in this manuscript) and it is designed to be run in parallel, allowing large-scale application at any location or time period. The reason Watta hasn’t been applied at large scale is a lack of computational infrastructure.
It would be nice to acknowledge that the automated and scalable nature of the method, while advantageous, is not a unique selling point. This doesn’t take away that there is plenty of novelty in the manuscript to merit publication. Emphasizing these specific innovations would strengthen the manuscript, in my view.
Citation: https://doi.org/10.5194/egusphere-2024-1156-CC1 -
AC4: 'Reply on CC1', Philipp Arndt, 23 Jul 2024
Thank you very much Bert for your positive and constructive comment, and for bringing this particular issue to our attention. We would like to express our appreciation for the scientific contributions that you made in Datta and Wouters (2021) and acknowledge that the results you published had a large impact on motivating our own study and informing our opinion that it is crucial to retrieve and publicly share as many ICESat-2 water depth estimates as possible, to improve our ability to continuously monitor supraglacial meltwater volumes across the ice sheets. We explain this in the later paragraphs of our full response.
Please see the attachment for our full response and suggested changes to the manuscript.
- AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC4: 'Reply on CC1', Philipp Arndt, 23 Jul 2024
-
RC3: 'Comment on egusphere-2024-1156', Sammie Buzzard, 08 Jul 2024
Review of Arndt and Fricker (2024) -A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
This paper presents two algorithms that together allow the retrieval of supraglacial lake depths using ICESat-2. This method is scalable beyond the case studies presented and therefore a useful contribution to the community that I would strongly recommend for publication.
Understanding surface melt, and lake depths is key for predicting ice shelf stability, and datasets of lake depths are limited. In situ data is scarce and remote sensing methods are necessary for providing validation and calibration for models as well as understanding the development of these lakes so this work will clearly be of use to the community.
While there are methods existing to determine lake depths remotely e.g. using Landsat-8, this to my knowledge is one of only a small number using ICESat-2, the advantage of this particular method appears to be scalability (although see my comments below on this).
The methodology and results presented are in my opinion sufficient for publication, and my recommendations for changes to the manuscript are mostly minor.
General comments:
It would be good to clarify the limitations of the algorithm e.g. max/min lake widths/ lengths/ depths detected clearly early on in the paper (and state how this compares to other methods).
This method appears to only be scalable if you live in the US based on the information in the paper. Some comments on this would be useful e.g. can your methodology transfer to a reader’s local (i.e. institutional) supercomputer or does it need to be a National level facility? How possible would it be to do this?
Detailed comments:
Line 44: It would be good to have a more detailed comparison with the Datta and Wouters and Leeuwen methodologies to explain what the differences are here. Given the method presented here is promoted as scalable would it be possible to do a direct comparison to their results?
Line 57: Most of the Greenland ablation zone rather than most of Greenland?
Line 60: There are enough notable examples of melt away from the grounding zone (over the ice shelves of Larsen B, George VI) or on grounded ice (e.g. Corr et al. found more than a quarter or meltwater features on grounded ice https://essd.copernicus.org/articles/14/209/2022/) that maybe ‘mostly’ could be changed?
Line 73: Acceleration isn’t always the case. Some of these references are fairly old, Davison et al. have a nice review (https://doi.org/10.3389/feart.2019.00010), although there may be updated literature.
Line 75: I’m not sure this is strictly true it’s been observed, more than it’s been suggested as a possible mechanism.
Line 97: If we’re providing estimates we can also model this (e.g. https://tc.copernicus.org/articles/12/3565/2018/tc-12-3565-2018.html for individual lakes) but remote sensing is quicker/ more scalable but it’s not technically true we have to rely on it.
Line 152: Is it a good assumption that lakes are non-turbid? Certainly for sea ice ponds we can assume they are turbulent (of course they are shallower) but I wonder if e.g. it’s a very windy day when the measurements are taken how much this impacts the flatness.
Line 207: How were these numbers determined (empirical observation is a little broad e.g. how many lakes were examined?)
Line 259 onwards: What if the lake is e.g. 0.92m deep, would the bathymetric signal still get picked up? I think this is what you are saying in line 273 but you could clarify how you might discern the two, or if it is likely to be possible.
Line 314: Can you determine why there is no signal from the lake bed here? Is it in the ice covered lake areas?
Line 319: Not sure if the word ‘each’ here is a typo?
Line 321: See my general comment about lake sizes, what does a ‘few’ mean here, please be more specific. Is this related to the 10 in equation 3? (Or if not where does that come from?)
Line 350: I’m not sure what you mean here by ‘removing any lakes that fully overlap with another lake’. Does that not just make them the same lake (and is some information about that lake then lost)?
Line 405: So is this a limitation on minimum lake depths that can be detected?
Line 435: What is the situation where this could happen? Does this suggest a lack of confidence in surface retrievals or is the algorithm picking up something else (an ice lens?).
Section 4.1.2: Could you compare with the Moussavi et al dataset? That goes up to 2020 according to the data description: https://www.usap-dc.org/view/dataset/601401
Section 4.2.2: How many lakes are there actually in track 81? Is this something that could be determined manually to get an idea of false positives/ negatives from SuRFF?
Line 630: ‘this’ satellite rather than ‘a’ satellite. Datasets exist for other satellites.
Line 740: What were these equations based on? I understand you used trial and error but they are complex equations that must have had a starting point.
Citation: https://doi.org/10.5194/egusphere-2024-1156-RC3 -
AC3: 'Reply on RC3', Philipp Arndt, 23 Jul 2024
Thank you so much Sammie for taking the time to read and review our manuscript, and for your positive, thoughtful and constructive comments. In particular, we believe that your comments suggesting more detailed comparisons to similar studies as well as better explanations of the limitations of our method have helped to significantly improve the manuscript.
Please see the attachment for our full responses and suggested changes to the manuscript.
- AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC3: 'Reply on RC3', Philipp Arndt, 23 Jul 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1156', Jennifer Arthur, 11 Jun 2024
This manuscript uses ICESat-2’s ATL03 altimetry product to develop two new algorithms which together provide a scalable framework for supraglacial lake detection and depth determination from ATL03 data.
Surface melt is an important, yet poorly constrained, component of ice-sheet surface mass balance, leading to surface meltwater accumulating as lakes on ice-shelf surfaces and on grounded ice. In Antarctica, this has been linked to the process of meltwater-driven hydrofracture, which can trigger rapid ice-shelf collapse. Accurately measuring supraglacial lake meltwater depths from satellite data is important due to the challenges in obtaining in situ measurements and is needed for modelling meltwater interactions with ice sheet dynamics. However, few studies have developed automated lake depth estimation methods that are scalable beyond small data subsets, and previous studies rely on methods with poorly constrained parameters and a lack of in situ measurements.
The authors apply their algorithm framework to two regions that experience high surface melt (central west Greenland and the Amery Ice Shelf) and are able to reliably detect lakes where lake bathymetry is visible. The methodology appears robust, and the algorithm performs well even for more complex lakes (especially in Antarctica), including thin, elongated lakes and those with patchy ice cover. The authors found 1249 lakes with their algorithm during four melt seasons and conclude that lake depths agree well with manually-picked lakebeds in ICESat-2 along-track segments.
Overall, it is my view that this study is of broad interest to the cryospheric community as it builds upon previous work focused on supraglacial lake depths on both ice sheets, especially in the context of ice-shelf surface hydrology and dynamics, by paving the way for developing pan-ice sheet supraglacial lake depth and volume products. I think the surface hydrology and ice shelf communities would be very interested to see this algorithm applied at an ice-sheet scale in future.
In general, this is a very well-written manuscript with detailed methods, clear figures and most of my comments are relatively minor. Once the authors address these, I can therefore recommend that this manuscript is suitable for publication in The Cryosphere.
Please see the attached for specific comments.
-
AC1: 'Reply on RC1', Philipp Arndt, 23 Jul 2024
Thank you so much Jenny for taking the time to read and review our manuscript, and for your positive, thoughtful and constructive comments. In particular, we believe that your comments were instrumental in better presenting this manuscript within the context of the broader research community’s efforts to characterize surface hydrological processes and quantify meltwater on the ice sheets.
Please see the attachment for our full responses and suggested changes to the manuscript.
- AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC1: 'Reply on RC1', Philipp Arndt, 23 Jul 2024
-
RC2: 'Comment on egusphere-2024-1156', Ian Brown, 17 Jun 2024
This is an important contribution to our ability to monitor the supra-glacial hydrology of the ice sheets. It is a very well executed investigation and a well written manuscript. I have some small concerns regarding validation and some suggestions for minor edits.
Abstract.
The first sentence sets the tone so it is a little odd to address ice shelf collapse when that is not the focus of the article. Consider refocussing the opening sentence.Line 38. "Recently" is relative. ICESAT-2 has been in orbit for 5 years and readers may access the article in a decade meaning "recently" is not appropriate. Delete the word.
Line 205-206. Can you describe in more detail the empirical observations: how many were used to establish the thresholds?
Line 227. Does FLUID work as well in the presence of wind roughened water surfaces: I assume it does though perhaps fewer photons penetrate the surface. Please comment (as you specifically address the impact of wind on optical estimates of water depth on line 105).
Line 251. c is presumably the speed of light in a vaccuum or freshwater (line 434)? Please clarify.
Line 321. Define "a few".
Line 343-345. It would be useful to estimate the real number of lakes mapped if possible. If it is not possible please discuss this in the appropriate section of the manuscript.
Figure 8., Line 483. Consider moving figure 8 to the front of the manuscript, for example, at the end of the Introduction where you cite the study areas.
Section 4.1.1 (line 529-). The number of lakes detected over West Greenland is very small compared with other studies. Especially considering you measure over a season. Also, it is odd that so few lakes are detected in 2020-21 over the Amery ice shelf. The number of false negatives is presumably very high (i.e. your detection rate is low). Presumably this is a function of the ground track spacing of ICESAT-2. I think it is important to discuss this and the impact it would have on operational implementation of your algorithm (or the limits to that).
Line 550-551. Did you consider identifying lakes that have emptied. Johansson et al., (2013; J. Hydrol. 476) show that many lakes are transient and will empty late in season. This would allow you to evaluate the accuracy of the estimate from filled-emptied conditions.
Line 657. I think you need to mention there is a bias towards over-estimation.
Line 660. I do not think this is demonstrated given the very low numbers of detections and the fact that you can't identify whether multiple measurement lines are from the same lake.
Citation: https://doi.org/10.5194/egusphere-2024-1156-RC2 -
AC2: 'Reply on RC2', Philipp Arndt, 23 Jul 2024
Thank you so much Ian for taking the time to read and review our manuscript, and for your positive, thoughtful and constructive comments. In particular, we believe that your suggestions regarding a clearer distinction between ICESat-2 lake segments and unique “real” supraglacial lakes have helped to make the manuscript more clear, especially to readers who are not already very familiar with ICESat-2 data.
Please see the attachment for our full responses and suggested changes to the manuscript. - AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC2: 'Reply on RC2', Philipp Arndt, 23 Jul 2024
-
CC1: 'Comment on egusphere-2024-1156', Bert Wouters, 26 Jun 2024
I agree with the two other reviewers that this is a well-written and important contribution, presenting an elegant method to derive supraglacial lake bathymetry. Nevertheless, I would like to comment on the two statements below, in the Introduction and Summary sections:
L47-53: Previous ICESat-2 studies have been limited to applying depth estimation methods to a handful of manually picked lakes or data granules, with no clear pathway to large-scale computational implementation across the ATL03 data catalog, which comprises hundreds of terabytes of unstructured point cloud data (Neumann et al., 2023b). To address this challenge, we have created a fully automated and scalable algorithm for lake detection and depth determination from ICESat-2 data.
L646-651: ICESat-2 data had not previously been used at scale for this purpose because its photon-level product comprises hundreds of terabytes of unstructured point cloud data along spatially discrete ground tracks, which makes it difficult to integrate the data with spatially continuous data in existing workflows. To address this challenge, we have presented the fully automated, two-step FLUID/SuRRF algorithm for the detection and depth determination of supraglacial lakes on the ice sheets in ICESat-2 photon data, and proposed a computational framework that allows for its large-scale implementation across any desired ice sheet drainage basins and melt seasons.
Whereas it is true that other methods have not been used at such a large scale as in the manuscript, the Watta algorithm (Datta and Wouters, 2021) is fully automated (i.e. it detects potential lake locations based on a flatness criterion and then estimates the bathymetry, similar to the framework presented in this manuscript) and it is designed to be run in parallel, allowing large-scale application at any location or time period. The reason Watta hasn’t been applied at large scale is a lack of computational infrastructure.
It would be nice to acknowledge that the automated and scalable nature of the method, while advantageous, is not a unique selling point. This doesn’t take away that there is plenty of novelty in the manuscript to merit publication. Emphasizing these specific innovations would strengthen the manuscript, in my view.
Citation: https://doi.org/10.5194/egusphere-2024-1156-CC1 -
AC4: 'Reply on CC1', Philipp Arndt, 23 Jul 2024
Thank you very much Bert for your positive and constructive comment, and for bringing this particular issue to our attention. We would like to express our appreciation for the scientific contributions that you made in Datta and Wouters (2021) and acknowledge that the results you published had a large impact on motivating our own study and informing our opinion that it is crucial to retrieve and publicly share as many ICESat-2 water depth estimates as possible, to improve our ability to continuously monitor supraglacial meltwater volumes across the ice sheets. We explain this in the later paragraphs of our full response.
Please see the attachment for our full response and suggested changes to the manuscript.
- AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC4: 'Reply on CC1', Philipp Arndt, 23 Jul 2024
-
RC3: 'Comment on egusphere-2024-1156', Sammie Buzzard, 08 Jul 2024
Review of Arndt and Fricker (2024) -A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
This paper presents two algorithms that together allow the retrieval of supraglacial lake depths using ICESat-2. This method is scalable beyond the case studies presented and therefore a useful contribution to the community that I would strongly recommend for publication.
Understanding surface melt, and lake depths is key for predicting ice shelf stability, and datasets of lake depths are limited. In situ data is scarce and remote sensing methods are necessary for providing validation and calibration for models as well as understanding the development of these lakes so this work will clearly be of use to the community.
While there are methods existing to determine lake depths remotely e.g. using Landsat-8, this to my knowledge is one of only a small number using ICESat-2, the advantage of this particular method appears to be scalability (although see my comments below on this).
The methodology and results presented are in my opinion sufficient for publication, and my recommendations for changes to the manuscript are mostly minor.
General comments:
It would be good to clarify the limitations of the algorithm e.g. max/min lake widths/ lengths/ depths detected clearly early on in the paper (and state how this compares to other methods).
This method appears to only be scalable if you live in the US based on the information in the paper. Some comments on this would be useful e.g. can your methodology transfer to a reader’s local (i.e. institutional) supercomputer or does it need to be a National level facility? How possible would it be to do this?
Detailed comments:
Line 44: It would be good to have a more detailed comparison with the Datta and Wouters and Leeuwen methodologies to explain what the differences are here. Given the method presented here is promoted as scalable would it be possible to do a direct comparison to their results?
Line 57: Most of the Greenland ablation zone rather than most of Greenland?
Line 60: There are enough notable examples of melt away from the grounding zone (over the ice shelves of Larsen B, George VI) or on grounded ice (e.g. Corr et al. found more than a quarter or meltwater features on grounded ice https://essd.copernicus.org/articles/14/209/2022/) that maybe ‘mostly’ could be changed?
Line 73: Acceleration isn’t always the case. Some of these references are fairly old, Davison et al. have a nice review (https://doi.org/10.3389/feart.2019.00010), although there may be updated literature.
Line 75: I’m not sure this is strictly true it’s been observed, more than it’s been suggested as a possible mechanism.
Line 97: If we’re providing estimates we can also model this (e.g. https://tc.copernicus.org/articles/12/3565/2018/tc-12-3565-2018.html for individual lakes) but remote sensing is quicker/ more scalable but it’s not technically true we have to rely on it.
Line 152: Is it a good assumption that lakes are non-turbid? Certainly for sea ice ponds we can assume they are turbulent (of course they are shallower) but I wonder if e.g. it’s a very windy day when the measurements are taken how much this impacts the flatness.
Line 207: How were these numbers determined (empirical observation is a little broad e.g. how many lakes were examined?)
Line 259 onwards: What if the lake is e.g. 0.92m deep, would the bathymetric signal still get picked up? I think this is what you are saying in line 273 but you could clarify how you might discern the two, or if it is likely to be possible.
Line 314: Can you determine why there is no signal from the lake bed here? Is it in the ice covered lake areas?
Line 319: Not sure if the word ‘each’ here is a typo?
Line 321: See my general comment about lake sizes, what does a ‘few’ mean here, please be more specific. Is this related to the 10 in equation 3? (Or if not where does that come from?)
Line 350: I’m not sure what you mean here by ‘removing any lakes that fully overlap with another lake’. Does that not just make them the same lake (and is some information about that lake then lost)?
Line 405: So is this a limitation on minimum lake depths that can be detected?
Line 435: What is the situation where this could happen? Does this suggest a lack of confidence in surface retrievals or is the algorithm picking up something else (an ice lens?).
Section 4.1.2: Could you compare with the Moussavi et al dataset? That goes up to 2020 according to the data description: https://www.usap-dc.org/view/dataset/601401
Section 4.2.2: How many lakes are there actually in track 81? Is this something that could be determined manually to get an idea of false positives/ negatives from SuRFF?
Line 630: ‘this’ satellite rather than ‘a’ satellite. Datasets exist for other satellites.
Line 740: What were these equations based on? I understand you used trial and error but they are complex equations that must have had a starting point.
Citation: https://doi.org/10.5194/egusphere-2024-1156-RC3 -
AC3: 'Reply on RC3', Philipp Arndt, 23 Jul 2024
Thank you so much Sammie for taking the time to read and review our manuscript, and for your positive, thoughtful and constructive comments. In particular, we believe that your comments suggesting more detailed comparisons to similar studies as well as better explanations of the limitations of our method have helped to significantly improve the manuscript.
Please see the attachment for our full responses and suggested changes to the manuscript.
- AC5: 'Final author comments', Philipp Arndt, 23 Jul 2024
-
AC3: 'Reply on RC3', Philipp Arndt, 23 Jul 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets Philipp Sebastian Arndt and Helen Amanda Fricker https://zenodo.org/doi/10.5281/zenodo.10901737
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets Philipp Sebastian Arndt and Helen Amanda Fricker https://zenodo.org/doi/10.5281/zenodo.10901737
Model code and software
Source Code for: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets Philipp Sebastian Arndt and Helen Amanda Fricker https://zenodo.org/doi/10.5281/zenodo.10905941
Source Code for: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets Philipp Sebastian Arndt and Helen Amanda Fricker https://zenodo.org/doi/10.5281/zenodo.10905941
Interactive computing environment
Data and Code for Figure Reproduction: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets Philipp Sebastian Arndt and Helen Amanda Fricker https://zenodo.org/doi/10.5281/zenodo.10901826
Data and Code for Figure Reproduction: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets Philipp Sebastian Arndt and Helen Amanda Fricker https://zenodo.org/doi/10.5281/zenodo.10901826
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Helen Amanda Fricker
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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