the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Nov 2024
Quantifying degradation of the Imja Lake moraine dam with fused InSAR and SAR feature tracking time series
Abstract. Glacial lake outburst flood (GLOF) hazards are often tied to the structural properties of the moraines that dam glacial lakes. Traditional investigations of moraine dam structure and degradation involve costly and logistically challenging in-situ geophysical and repeat topographic surveys, which can only be performed for a small number of sites. We developed a scalable satellite remote sensing approach using interferometric synthetic aperture radar (InSAR), InSAR coherence, and SAR feature tracking to precisely measure moraine dam surface displacement and map the extent of buried ice. We combined time series from ascending and descending Sentinel-1 orbits to investigate vertical and horizontal surface displacement from 2017–2024 with ~12-day temporal sampling.
We applied our approach to quantify degradation of the Imja Lake moraine dam in the Everest Region of Nepal. We find that a 0.3 km2 area of the moraine dam has cumulatively subsided ~90 cm over the 7-year study period. Seasonal change in InSAR coherence provides evidence for buried ice throughout the moraine dam. We observe consistent downward and eastward displacement throughout the colder months, which we attribute to ice flow. The magnitude of downward vertical surface velocity increases in the warmer months, likely due to melting of buried ice. Our observations provide new insights into the timing and magnitude of the processes that control moraine dam development and evolution, with broader implications for regional GLOF hazard assessment and mitigation.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2024-3196', Anonymous Referee #1, 20 Dec 2024
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RC2: 'Comment on egusphere-2024-3196', Anonymous Referee #2, 30 Dec 2024
Dear Editor, Dear Authors,
Thank you for the opportunity to review your manuscript. I appreciate the effort and dedication that has gone into developing your study and the processing strategies employed. However, after a thorough evaluation, I regret to inform you that I must recommend rejecting this paper in its current form. While the work demonstrates potential, it would benefit significantly from the involvement of an InSAR/SAR expert with specific expertise in time series analysis development. Such expertise could provide critical support to strengthen the analysis and ensure the robustness of the methodologies employed. Additionally, the reliance on open-access code, while commendable for accessibility, may introduce fundamental flaws in the processing approach if not carefully validated. Below, I outline the key reasons for this recommendation, along with specific feedback.
Lack of Acknowledgment of Prior Work: Your study builds upon well-established methods, such as those introduced by Casu et al. (2011) and Jolivet and Simons (2018), yet these foundational works are neither appropriately acknowledged nor referenced. This omission undermines the context of your research and does not allow the reader to contextualize your research in literature.
Introduction of Non-Validated Processing Approaches: The manuscript introduces non-standard processing techniques, such as the use of pixel offsets to constrain an InSAR time series, without sufficient validation or precedent in the literature. Introducing novel methodologies is commendable; however, without proper validation, these methods cannot be reliably assessed or accepted by the broader scientific community.
Integration of Processing and Physical Interpretation: Attempting to combine an alternative processing strategy with the interpretation of a physical phenomenon adds complexity to the manuscript. The two aspects require independent development, validation, and presentation to ensure clarity and scientific soundness.
Given these concerns, I suggest splitting the manuscript into two separate papers:
- A technical paper presenting and validating the alternative processing approach, potentially submitted to a journal specializing in remote sensing or geophysical methods (e.g., IEEE Transactions on Geoscience and Remote Sensing).
- A separate study presenting the physical results and interpretation, submitted to a journal like The Cryosphere.
Major Concerns and Specific Feedback:
- Atmospheric Noise Filtering (Lines 202-228):
The methodology for removing atmospheric noise and assessing measurement accuracy is not specified. This step is critical for validating the results. - Kernel and Window Sizes (Lines 228-231):
The chosen dimensions and skip sizes are not sufficiently justified. It would be helpful to provide a sensitivity analysis or rationale based on the study's objectives. - Displacement Accuracy (Lines 235):
The theoretical accuracy of feature tracking measurements should account for terrain slope and ground range, not just slant range, as this directly impacts the reliability of your results. - Feature Tracking and InSAR Constraining (Lines 250-260):
Using pixel offsets to constrain a high-accuracy phase time series is methodologically questionable. Errors inherent to pixel offsets should be filtered using metrics like SNR rather than aggregating them into a median velocity product. - Displacement Thresholding (Line 256):
Applying a displacement threshold when the expected values are already known introduces bias into the analysis. This approach is contradictory to the principle of deriving unbiased results. - Methodology Validation (Lines 261-265):
Using the normalized median absolute deviation (NMAD) for variability assessment is unconventional for SAR analysis. A focused validation or comparative analysis with established methods is required. - Continuous Network Requirement (Lines 268):
Removing interferograms with low coherence to maintain a continuous network is unnecessary. The NSBAS approach (Jolivet and Simons, 2018) allows for network separations, negating the need for such constraints. - Pseudo 3D Analysis (Figure 4):
Ignoring the north component in your analysis leads to incomplete results, especially in complex topography. Conducting a full 3D inversion using all four available directions would yield more accurate and meaningful results. - Topographical Signal Contamination (Figure 5, Panel B):
The downward signal observed in July-October suggests north-south displacement contamination. A proper 3D analysis would mitigate this issue and provide clearer insights.
In summary, while your study addresses an important topic, significant revisions and validations are required before it can be considered for publication. I hope these comments provide constructive guidance for improving your work.
Best regards
Jolivet, R., & Simons, M. (2018). A multipixel time series analysis method accounting for ground motion, atmospheric noise, and orbital errors. Geophysical Research Letters, 45(4), 1814-1824.
Casu, F., Manconi, A., Pepe, A., & Lanari, R. (2011). Deformation time-series generation in areas characterized by large displacement dynamics: The SAR amplitude pixel-offset SBAS technique. IEEE Transactions on Geoscience and Remote Sensing, 49(7), 2752-2763.
Citation: https://doi.org/10.5194/egusphere-2024-3196-RC2 -
EC1: 'Comment on egusphere-2024-3196', Ian Delaney, 08 Jan 2025
This manuscript has received some mixed reviews. I want to allow the authors to respond, particularly if they can address Reviewer 2's methodological comments and Reviewer 1's points on DEM differencing and presentation.
Citation: https://doi.org/10.5194/egusphere-2024-3196-EC1
Model code and software
Fufiters (fused feature tracking-InSAR time series) Scott Henderson and George Brencher https://github.com/relativeorbit/fufiters
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