the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An 8-day Antarctic supraglacial lake dataset from MODIS
Abstract. Supraglacial lakes (SGLs) are widely distributed across Antarctica and play an important role in modulating surface energy balance through albedo feedback, promoting ice-shelf disintegration via hydrofracture, and influencing ice dynamics. Existing SGL monitoring studies mainly rely on narrow-swath satellite data, such as Landsat and Sentinel-2, resulting in discontinuous observations with relatively long revisit intervals and limiting the ability to capture the rapid evolution of supraglacial hydrological processes. Here, an 8-day Antarctic SGL fraction dataset spanning 2000–2023 is presented. The dataset is generated by integrating high-temporal-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery with high-spatial-resolution Sentinel-2 data within a machine-learning framework. The dataset reveals that Antarctic SGLs are highly dynamic and short-lived, with approximately 83 % of lakes exhibiting mean persistence rates below 5 %. The multi-year mean maximum SGL area is estimated at 4,103 ± 1,479 km2. Clear spatial heterogeneity in peak timing is further revealed, with SGL extent peaking approximately one week earlier in West Antarctica than in East Antarctica and about two weeks earlier than on the Antarctic Peninsula. Spatially, approximately 65 % of SGLs are located within 10 km of grounding lines and are closely associated with blue-ice and exposed rock areas. These long-term, high-temporal-resolution observations provide a valuable basis for investigating the spatiotemporal variability of Antarctic SGLs and their associated impacts. The dataset is publicly available at https://doi.org/10.5281/zenodo.19936100.
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RC1: 'Comment on essd-2026-353', Anonymous Referee #1, 08 Jul 2026
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Supraglacial lakes are widely distributed across the Antarctic Ice Sheet during summer and have significant impacts on ice sheet hydrology and ice shelf stability. Existing snapshot mapping of supraglacial lakes based on high-resolution satellite imagery is insufficient for capturing their rapid changes. This paper utilizes MODIS and Sentinel-2 satellite remote sensing imagery and employs machine learning methods to construct an 8-day Antarctic supraglacial lake dataset. This work is very timely and has potentially important implications for better understanding the Antarctic Ice Sheet. The paper is well-structured. I recommend publication after minor revisions and provide the following comments for the authors' consideration.General comments1. The supraglacial lakes extracted from Sentinel-2 satellite remote sensing imagery are used as training data, which greatly influences the accuracy of the machine learning algorithm. Currently, the description of the Sentinel-2 supraglacial lake extraction workflow is relatively brief, only stating that a supervised classification algorithm was used and results with classification accuracy greater than 95% were selected. I suggest further explaining the production of the training data for this supervised classification algorithm, as well as addressing the potential misleading of overall accuracy metrics caused by the small targets (supraglacial lakes) versus the large background area of snow and ice. This would help demonstrate the reliability of the Sentinel-2 supraglacial lake training data.2. In the accuracy assessment section, the paper mentions both classification and regression accuracies, which is somewhat confusing. Classification yields potential water pixels, while regression calculates the SGL fraction only within those water pixels. Therefore, the final accuracy assessment should rely on the SGL fraction results rather than the water versus non-water classification results. If so, I suggest using "performance of SGL fraction retrieval" instead of "performance of SGL classification and fraction retrieval" in the results assessment (Section 4.1), as the latter may cause confusion. Additionally, in Table 2 of the accuracy assessment, metrics such as bias, MAE, and RRMSE could be added. In Figure 5, maps of Sentinel-2-derived SGL and SGL fraction, and difference maps between Sentinel-2-derived SGL and SGL fraction and MODIS-derived SGL and SGL fraction, could be included to better reflect the product accuracy.Minor commentsline 27, "impermeable snow and ice surfaces", should it be "impermeable ice surfaces"?line 31, add a few recently published papers on the impact of meltwater on ice sheet dynamics and stability.lines 38 and 43, add spatial resolution numbers here, e.g., XX-XX m.line 66, specify the resampling method.Figure 1, add acquisition dates to the Sentinel-2 and MODIS imagery.line 78, why use Sentinel-2 Level-1C products instead of Level-2A products?line 92, why use the CryoSat-2 DEM instead of the Reference Elevation Model of Antarctica (REMA)?lines 100-104, these lines overlap in content with lines 106-110 and should be removed.line 123, "known lake locations", is this referenced from another paper?line 128, why say "max pooling" rather than "max aggregation"?line 143, the design of NDWIice is actually intended to better distinguish open water from easily confusable features such as low-albedo slush. This is because the NDWIice index shifts the two bands of NDWI (green and NIR) toward shorter wavelengths, replacing them with blue and red.lines 148-149, the band combinations for these two water indices are written incorrectly; NDWI uses green and NIR, while NDWIice uses blue and red. Please verify.line 151, these thresholds are somewhat lower than those in previous supraglacial lake extraction studies, what might be the reason?line 171, five-fold cross-validation, two references are cited here, did the authors follow their methods?line 179, "supraglacial meltwater accumulation" can be changed to "supraglacial lake formation".Figure 4, it is suggested to add metrics such as n, bias, MAE, and RRMSE.Figure 6, the area value ranges in the three columns are inconsistent, making direct comparison difficult. It is suggested to unify the range to 0–600 km². Does this figure's pronounced interannual variability in SGL area potentially suffer from cloud cover effects (even though the 8-day composite may largely mitigate this)? The SGL fraction in the figure is difficult to discern; consider focusing the map on regions with SGL distribution.Figures 7 & 8, nice figures! From these two figures, some extracted features appear as very wide channels and are classified as SGL. If such wide channels are limited in distribution, including them in the SGL discussion is fine; however, if they are widespread, is it useful to discuss them separately in future?Figure 9, again, nice figure! My question is similar to that for Figure 6: do the annual results here suffer from cloud cover and data quality differences? Even though the 8-day composite may largely mitigate this, the 8-day composite MODIS data product still has certain issues. Should this be considered when analyzing multi-year SGL area changes?Figure 10, this shows the 23-year long-term mean. I suggest adding standard deviation in Figure 10b to illustrate the degree of interannual variability.ReplyCitation: https://doi.org/
10.5194/essd-2026-353-RC1
Data sets
An 8-day Antarctic supraglacial lake dataset from MODIS Shuo Wei, Lei Zheng, Qi Liang, Teng Li, and Xiao Cheng https://doi.org/10.5281/zenodo.19936100
Model code and software
An 8-day Antarctic supraglacial lake dataset from MODIS Shuo Wei, Lei Zheng, Qi Liang, Teng Li, and Xiao Cheng https://doi.org/10.5281/zenodo.19936100
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