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Abstract. In the 2019/2020 austral summer, the surface melt duration andextent on the northern George VI Ice Shelf (GVIIS) was exceptional comparedto the 31 previous summers of distinctly lower melt. This finding is basedon analysis of near-continuous 41-year satellite microwave radiometer andscatterometer data, which are sensitive to meltwater on the ice shelfsurface and in the near-surface snow. Using optical satellite imagery fromLandsat 8 (2013 to 2020) and Sentinel-2 (2017 to 2020), record volumes ofsurface meltwater ponding were also observed on the northern GVIIS in2019/2020, with 23 % of the surface area covered by 0.62 km3 of ponded meltwater on 19 January. These exceptional melt andsurface ponding conditions in 2019/2020 were driven by sustained airtemperatures ≥0 ∘C for anomalously long periods (55 to 90 h)from late November onwards, which limited meltwater refreezing.The sustained warm periods were likely driven by warm, low-speed (≤7.5 m s−1) northwesterly and northeasterly winds and not by foehn windconditions, which were only present for 9 h total in the 2019/2020 meltseason. Increased surface ponding on ice shelves may threaten theirstability through increased potential for hydrofracture initiation; a riskthat may increase due to firn air content depletion in response tonear-surface melting. 

Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R2 value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf. The methods presented here pave the way to upscaling throughout the Landsat 8 and Sentinel-2 observational record across Antarctica to produce a first-ever continental dataset of supraglacial lake volumes. Such a dataset will improve our understanding of the influence of surface hydrology on ice shelf stability, and thus, future projections of Antarctica’s contribution to sea level rise. 

Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product. 

Abstract Surface melting and lakes are common to Antarctic ice shelves, and their existence and drainages have been invoked as a precursor for ice shelf collapse. Here, we present satellite observations over 2014–2020 of repeated, rapid drainages of a supraglacial lake at the grounding zone of Amery Ice Shelf, East Antarctica. Post‐drainage imagery in 2018 reveals lake bottom features characteristic of rapid, vertical lake drainage. Observed lake volumes indicate drainages are not associated with a threshold meltwater volume. Instead, drainages typically coincide with periods of high daily tidal amplitude, suggesting hydrofracture is assisted by tidally forced ice flexure inherent to the ice shelf grounding zone. Combined with observations of widespread grounding zone lake drainages on Amery, these findings indicate ice shelf meltwater accumulation may be inhibited by grounding zone drainage events, thus representing a potential stabilizing mechanism despite enhanced melting common to these regions.