the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jan 2025
Review article: Using spaceborne lidar for snow depth retrievals: Recent findings and utility for global hydrologic applications
Abstract. Lidar is an effective tool to measure snow depth over key watersheds across the United States. Lidar-derived snow depth observations from airborne platforms have demonstrated centimeter-level accuracy at high spatial resolution. However, ground-based and airborne lidar surveys are costly and limited in space and time. In recent years, there has been an emerging interest in using spaceborne lidar to estimate snow depth. Preliminary results from spaceborne lidar altimeters such as the NASA Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ca provide routine snow depth retrievals over watersheds, though further research on accuracy, coverage, and operational potential is needed. In this review, we outline the current status of research using spaceborne lidar to derive snow depth. We focus on the currently operational ICESat-2 mission, with a summary of snow observations gathered from recent studies. We also outline best practices for spaceborne lidar snow depth retrieval, based on findings from recent studies. We conclude with a discussion of ongoing challenges for spaceborne lidar, with suggestions for future studies and requirements for future mission concepts.
Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 10 Apr 2025)
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RC1: 'Comment on egusphere-2024-3992', Anonymous Referee #1, 21 Feb 2025
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This review article provides a comprehensive overview of the current state of research in deriving snow depth using spaceborne lidar. The manuscript effectively summarizes basic lidar principles, outlines the measurement modalities, and compares the performance of different platforms through various studies. Additionally, the inclusion of a case study over the Arctic Coastal Plain of Alaska shows a practical applications with associated challenges. Overall, the paper is of high quality and appears solid.
Comments:
- The literature review is broad and offers a valuable overview of available approaches, and the explanation of basic principles serves as introduction for readers new to the technique. However, the level of detail and technical terminology is at times quite dense. I recommend adding a detailed schematic figure, similar to Figure 4, that visually represents key concepts such as along-track resolution, across-track resolution, and beam footprint for example. This would aid in comprehension and serve as a quick reference.
- The discussion regarding error sources is informative. An easily accessible recap table summarizing these uncertainty sources and their associated uncertainties would enhance the reader’s ability to grasp and compare the contributions of each factor.
- The case study adds significant value to the work; however, clarification is needed for lines 214–218. The assumption that no snowpack change occurred during the eight days between March 4 and March 12, based on sub-zero temperatures and the absence of melt and sublimation, is reasonable but should be reinforced. For instance, as shown by Spehlmann et al. (2023), sublimation rates in tundra environments can reach up to 0.15 mm/day. Assuming a snow density of 200 kg/m³ over 8 days, this would translate to a change of approximately 6 mm in snow depth, which is negligible relative to the expected measurement uncertainty. Strengthening this explanation will add robustness to the argument. Moreover I would us km/h instead of kt, for being coherent with SI.
- In line 447, the authors refer to previous studies on the interannual repeatability of snow patterns. For completeness, I suggest including Premier et al. (2021).
In summary, the work is already in a good and advanced state. I recommend acceptance after minor revisions.
References:
Spehlmann, K., Euskirchen, E., & Stuefer, S. (2023). Sublimation Measurements of Tundra and Taiga Snowpack in Alaska. The Cryosphere Discussions, 1–18.Premier, V., et al. (2021). A novel approach based on a hierarchical multiresolution analysis of optical time series to reconstruct the daily high-resolution snow cover area. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9223–9240.
Citation: https://doi.org/10.5194/egusphere-2024-3992-RC1 -
CC1: 'Comment on egusphere-2024-3992', Zuhal Akyurek, 12 Mar 2025
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This manuscript is presenting a review on using spaceborne lidar for snow depth retrievals. The authors gathered the recent studies on retrieving snow depth from spaceborne lidar data. They presented a case study over the tundra of Alaska to present accuracy estimates for several current methods. The manuscript is written well and presents the current status of research on using spaceborne lidar in retrieving snow depth to be used in operational hydrological studies. I recommend acceptance after minor revisions. There are some minor comments listed below:
- Line 209, Figure 4a must be Figure 5a
- Line 232, Figure 5 must be Figure 6
- Line 358, Figure 5 must be Figure 6
- In Figure 3 Hu et al. (2022a) must be Hu et al. (2022b) which is also given in Table 2.
- It would be good to include the size of the study areas in km2 in Table 2, it may give an idea in using these observations for hydrological modelling that is stated in the Conclusion part.
- In Figure 6, it would be good to present the common points from ATL06, ATL08 and ATL06-SR with a different colour to present the consistency of the products in retrieving the snow depth.
- There is so much spatial variation in ATL06 and ATL06-SR products. Are these noises or that spatial variation exists along the track. Especially the snow depth larger than 1.2 m between 70.03o and 70.05 o in Figure ATL06 is questionable. What is the reason to have this large snow depth. In scatterplot of ATL08 1.2 m is seen but it is not seen in snow depth figure of ATL08. 1.2 m snow depth is not presented in ATL06-SR snow depth and scatterplot figures.
- What is the reason to have a constant snow depth around 70.09 degree in ATL06 snow depth figure. It seems there is a gap of UAF snow depths in this area and an interpolation is applied in this region.
- It would be good to include ground elevation in snow depths of Figure 6. It would give us an idea how the ground elevation is changing along the track.
- “ Spaceborne lidar is currently unable to fulfill the revisit times necessary to achieve global SWE observations every 1-5 days.” I think this sentence is not correct. We can retrieve snow depth from spaceborne lidar but not snow density. Even data availability can be every 1-5 days, how can the snow depth retrieved from lidar can be used to obtain SWE?
Citation: https://doi.org/10.5194/egusphere-2024-3992-CC1
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