Supraglacial Lakes on George VI Ice Shelf from a multi-decadal perspective

2021 ◽  
Author(s):  
Thomas Barnes ◽  
Amber Leeson ◽  
Mal McMillan ◽  
Vincent Verjans ◽  
Chris Kittel
Keyword(s):  
New Record ◽  
Climate Model ◽  
Southern Annular Mode ◽  
Climate Data ◽  
Ice Shelf ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Lake Formation ◽  
Record Value ◽  
Sentinel 2

<p><span>In 2020, 11.8% of northern George VI ice shelf was covered by supraglacial lakes, and it has been speculated that this was a record high lake density. Supraglacial lakes are associated with ice shelf instability, and were implicated in the collapse of Larsen B in 2002, where ~10% lake density was recorded. Here we use optical satellite imagery from Sentinel-2 and Landsat-1-8 in combination with recorded and modelled climate data from Fossil Bluff AWS, the MAR climate model, and the community firn model to study lakes on George VI ice shelf between 1973 and 2020. We find that the high density of lakes in 2020 was not unique, with similar events occurring five times in the study period, including a record value of 12.1% density in 1989. Furthermore, we find lake density to be controlled by a combination of high firn air content, high air temperature and a neutral southern annular mode, thus a strong melt year alone is insufficient for producing high lake densities. 2020 had record-high melt and temperature values, which suggests that this should also be a record year for lake coverage. A thicker than usual snow/firn pack in the winter prior to the 2020 melt season however, had a dampening effect on lake formation and thus lakes were less abundant than in 1989. As temperatures at this location are projected to increase in coming decades, but snowfall is expected to stay the same, future high melt years are very likely to lead to new record high lake coverage. Since supraglacial lakes are an indicator of ice shelf stability, this suggests that George VI may be rendered unstable within our lifetime.</span></p>

scholarly journals Changes in Supraglacial Lakes on George VI Ice Shelf, Antarctic Peninsula: 1973–2020

2021 ◽  
Author(s):  
Thomas James Barnes ◽  
Amber Alexandra Leeson ◽  
Malcolm McMillan ◽  
Vincent Verjans ◽  
Jeremy Carter ◽  
...  
Keyword(s):  
Historical Context ◽  
Limiting Factor ◽  
Climate Projections ◽  
Climate Data ◽  
Ice Shelf ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Lake Formation ◽  
Sentinel 2

Abstract. High densities of supraglacial lakes have been associated with ice shelf instability and collapse. 2020 was a record melt year on George VI ice shelf with ~12 % of its northernmost portion being covered by lakes. We use 208 Sentinel-2 and Landsat-1-8 satellite images from the past 47 years, together with climate data and firn modelling, to assess the long-term presence of lakes on George VI, thus placing 2020 within a historical context. We find that the ~12 % lake coverage observed in 2020 is not unprecedented and similar to previous high lake years; events of similar magnitude occurred at least five times previously. Secondly, we find lake coverage is controlled by a combination of melting, accumulation, firn air content and firn build-up strong melting alone does not entail high lake coverage. Instead, while melting contributes positively to lake formation, we find accumulation to act as a limiting factor on the formation of lakes in response to melt, introducing new frozen material to the surface, thus cooling and storing meltwater. We find accumulation’s ability to limit melt to be further enhanced by its build-up, increasing available firn air content, and thus meltwater storage capacity. Our findings are supported by comparative analysis, showing years such as 1989 to have 55 % less melt, but similar lake coverage to 2020. Finally, we find that climate projections suggest future temperature increases, but steady snowfall in this region. Thus, in future there will be a greater propensity for higher lake densities on North George VI ice shelf, and associated risk of instability.

scholarly journals Seasonal evolution of Antarctic supraglacial lakes in 2015–2021 and links to environmental controls

2021 ◽  
Author(s):  
Mariel Christina Dirscherl ◽  
Andreas J. Dietz ◽  
Claudia Kuenzer
Keyword(s):  
Antarctic Peninsula ◽  
Time Lags ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Shelf Stability ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Environmental Controls ◽  
Lake Formation ◽  
Increased Risk

Abstract. Supraglacial meltwater accumulation on ice shelves may have important implications for future sea-level-rise. Despite recent progress in the understanding of Antarctic surface hydrology, potential influences on ice shelf stability as well as links to environmental drivers remain poorly constrained. In this study, we employ state-of-the-art machine learning on Sentinel-1 Synthetic Aperture Radar (SAR) and optical Sentinel-2 satellite imagery to provide new insight into the inter-annual and intra-annual evolution of surface hydrological features across six major Antarctic Peninsula and East Antarctic ice shelves. For the first time, we produce a record of supraglacial lake extent dynamics for the period 2015–2021 at unprecedented 10 m spatial resolution and bi-weekly temporal scale. Through synergetic use of optical and SAR data, we obtain a more complete mapping record enabling the delineation of also buried lakes. Our results for Antarctic Peninsula ice shelves reveal below average meltwater ponding during most of melting seasons 2015–2018 and above average meltwater ponding throughout summer 2019–2020 and early 2020–2021. Meltwater ponding on investigated East Antarctic ice shelves was far more variable with above average lake extents during most of melting seasons 2016–2019 and below average lake extents during 2020–2021. This study is the first to investigate relationships with climate drivers both, spatially and temporally including time lag analysis. The results indicate that supraglacial lake formation in 2015–2021 is coupled to the complex interplay of varying air temperature, solar radiation, snowmelt, wind and precipitation, each at different time lags and directions and with strong local to regional discrepancies, as revealed through pixel-based correlation analysis. Southern Hemisphere atmospheric modes as well as the local glaciological setting including melt-albedo feedbacks and the firn air content were revealed to strongly influence the spatio-temporal evolution of supraglacial lakes as well as below or above average meltwater ponding despite variations in the strength of forcing. Recent increases of Antarctic Peninsula surface ponding point towards a further reduction of the firn air content implying an increased risk for ponding and hydrofracture. In addition, lateral meltwater transport was observed over both Antarctic regions with similar implications for future ice shelf stability.

Inter-annual variability in supraglacial lakes around East Antarctica

2021 ◽  
Author(s):  
Jennifer Arthur ◽  
Chris Stokes ◽  
Stewart Jamieson ◽  
Rachel Carr ◽  
Amber Leeson
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Landsat 8 ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Flow Acceleration ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Global Sea Level

<p>Surface meltwater ponding can weaken and trigger the rapid disintegration of Antarctic ice shelves which buttress the ice sheet, causing ice flow acceleration and global sea-level rise. While supraglacial lakes (SGLs) are relatively well documented during some years and selected ice shelves in Antarctica, we have little understanding of how Antarctic-wide SGL coverage varies between melt seasons. Here, we present a record of SGL evolution around the peak of the melt season on the East Antarctic Ice Sheet (EAIS) over seven consecutive years. Our findings are based on a threshold-based algorithm applied to 2175 Landsat 8 images during the month of January from 2014 to 2020. We find that EAIS-wide SGL volume fluctuates inter-annually by up to ~80%. Moreover, patterns within regions and on neighbouring ice shelves are not necessarily synchronous. Over the whole EAIS, total SGL volume was greatest in January 2017, dominated by the Amery and Roi Baudouin ice shelves, and lowest in January 2016. Excluding these two ice shelves, SGL volume peaked in January 2020. Preliminary results suggest EAIS-wide total SGL volume and extent are weakly correlated with firn model simulations of firn air content, surface melt and minimum ice lens depth predicted by the regional climate model MAR. On certain ice shelves, years with peak SGL volume correspond with minimum firn air content. This work provides important constraints for numerical ice-shelf and ice-sheet model predictions of future Antarctic surface meltwater distributions and the potential impact on ice-sheet stability and flow.  </p>

scholarly journals Seasonal evolution of Antarctic supraglacial lakes in 2015–2021 and links to environmental controls

2021 ◽  
Vol 15 (11) ◽  
pp. 5205-5226
Author(s):  
Mariel C. Dirscherl ◽  
Andreas J. Dietz ◽  
Claudia Kuenzer
Keyword(s):  
Antarctic Peninsula ◽  
Large Scale ◽  
Environmental Drivers ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Shelf Stability ◽  
Air Content ◽  
Supraglacial Lakes ◽  
Environmental Controls ◽  
Lake Formation

Abstract. Supraglacial meltwater accumulation on ice shelves may have important implications for future sea level rise. Despite recent progress in the understanding of Antarctic surface hydrology, potential influences on ice shelf stability as well as links to environmental drivers remain poorly constrained. In this study, we employ state-of-the-art machine learning on Sentinel-1 synthetic aperture radar (SAR) and optical Sentinel-2 satellite imagery to provide new insight into the inter-annual and intra-annual evolution of surface hydrological features across six major Antarctic Peninsula and East Antarctic ice shelves. For the first time, we produce a high-resolution record of supraglacial lake extent dynamics for the period 2015–2021 at unprecedented 10 m spatial resolution and bi-weekly temporal scale. Through synergetic use of optical and SAR data, we obtain a more complete mapping record also enabling the delineation of buried lakes. Our results for Antarctic Peninsula ice shelves reveal below-average meltwater ponding during most of melting seasons 2015–2018 and above-average meltwater ponding throughout summer 2019–2020 and early 2020–2021 considering years 2015–2021 as a reference period. Meltwater ponding on investigated East Antarctic ice shelves was far more variable, with above-average lake extents during most 2016–2019 melting seasons and below-average lake extents during 2020–2021, considering the reference interval 2016–2021. This study is the first to investigate relationships with climate drivers both spatially and temporally including time lag analysis. The results indicate that supraglacial lake formation in 2015–2021 is coupled to the complex interplay of local, regional and large-scale environmental drivers with similar driving factors over both ice sheet regions. In particular, varying air temperature, solar radiation and wind conditions influenced supraglacial lake formation over all six ice shelves despite strong local to regional discrepancies, as revealed through pixel-based correlation analysis. Furthermore, regional climatic conditions were shown to be influenced by Southern Hemisphere atmospheric modes showing large-scale impacts on the spatio-temporal evolution of supraglacial lakes as well as on above- or below-average meltwater ponding with respect to the period 2015–2021. Finally, the local glaciological setting, including melt–albedo feedbacks and the firn air content, was revealed to strongly influence supraglacial lake distribution. Recent increases in Antarctic Peninsula surface ponding point towards a further reduction in the firn air content, implying an increased risk for ponding and hydrofracture. In addition, lateral meltwater transport was observed over both Antarctic regions with similar implications for future ice shelf stability.

Automated mapping of Antarctic supraglacial lakes and streams using machine learning

2020 ◽  
Author(s):  
Mariel Dirscherl ◽  
Andreas Dietz ◽  
Celia Baumhoer ◽  
Christof Kneisel ◽  
Claudia Kuenzer
Keyword(s):  
Machine Learning ◽  
Mass Balance ◽  
Antarctic Peninsula ◽  
Ice Sheet ◽  
Ice Shelf ◽  
Ice Dynamics ◽  
Antarctic Ice Sheet ◽  
Supraglacial Lakes ◽  
The Antarctic ◽  
Sentinel 2

<p>Antarctica stores ~91 % of the global ice mass making it the biggest potential contributor to global sea-level-rise. With increased surface air temperatures during austral summer as well as in consequence of global climate change, the ice sheet is subject to surface melting resulting in the formation of supraglacial lakes in local surface depressions. Supraglacial meltwater features may impact Antarctic ice dynamics and mass balance through three main processes. First of all, it may cause enhanced ice thinning thus a potentially negative Antarctic Surface Mass Balance (SMB). Second, the temporary injection of meltwater to the glacier bed may cause transient ice speed accelerations and increased ice discharge. The last mechanism involves a process called hydrofracturing i.e. meltwater-induced ice shelf collapse caused by the downward propagation of surface meltwater into crevasses or fractures, as observed along large coastal sections of the northern Antarctic Peninsula. Despite the known impact of supraglacial meltwater features on ice dynamics and mass balance, the Antarctic surface hydrological network remains largely understudied with an automated method for supraglacial lake and stream detection still missing. Spaceborne remote sensing and data of the Sentinel missions in particular provide an excellent basis for the monitoring of the Antarctic surface hydrological network at unprecedented spatial and temporal coverage.</p><p>In this study, we employ state-of-the-art machine learning for automated supraglacial lake and stream mapping on basis of optical Sentinel-2 satellite data. With more detail, we use a total of 72 Sentinel-2 acquisitions distributed across the Antarctic Ice Sheet together with topographic information to train and test the selected machine learning algorithm. In general, our machine learning workflow is designed to discriminate between surface water, ice/snow, rock and shadow being further supported by several automated post-processing steps. In order to ensure the algorithm’s transferability in space and time, the acquisitions used for training the machine learning model are chosen to cover the full circle of the 2019 melt season and the data selected for testing the algorithm span the 2017 and 2018 melt seasons. Supraglacial lake predictions are presented for several regions of interest on the East and West Antarctic Ice Sheet as well as along the Antarctic Peninsula and are validated against randomly sampled points in the underlying Sentinel-2 RGB images. To highlight the performance of our model, we specifically focus on the example of the Amery Ice Shelf in East Antarctica, where we applied our algorithm on Sentinel-2 data in order to present the temporal evolution of maximum lake extent during three consecutive melt seasons (2017, 2018 and 2019).</p>

scholarly journals Distribution and seasonal evolution of supraglacial lakes on Shackleton Ice Shelf, East Antarctica

2020 ◽  
Author(s):  
Jennifer F. Arthur ◽  
Chris R. Stokes ◽  
Stewart S. R. Jamieson ◽  
J. Rachel Carr ◽  
Amber A. Leeson
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
East Antarctica ◽  
Katabatic Wind ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Shelf Stability ◽  
Seasonal Evolution ◽  
Supraglacial Lakes ◽  
Area And Volume

Abstract. Supraglacial lakes (SGLs) enhance surface melting and can flex and fracture ice shelves when they grow and subsequently drain, potentially leading to ice shelf disintegration. However, the seasonal evolution of SGLs and their influence on ice shelf stability in East Antarctica remains poorly understood, despite some potentially vulnerable ice shelves having high densities of SGLs. Using optical satellite imagery, air temperature data from climate reanalysis products and surface melt predicted by a regional climate model, we present the first long-term record (2000–2020) of seasonal SGL evolution on Shackleton Ice Shelf, which is Antarctica’s northernmost remaining ice shelf and buttresses Denman Glacier, a major outlet of the East Antarctic Ice Sheet. In a typical melt season, we find hundreds of SGLs with a mean area of 0.02 km2, a mean depth of 0.96 m, and a mean total meltwater volume of 7.45 x 106 m3. At their most extensive, SGLs cover a cumulative area of 50.7 km2 and are clustered near to the grounding line, where densities approach 0.27 km2 per km2. Here, SGL development is linked to an albedo-lowering feedback associated with katabatic winds, together with the presence of blue ice and exposed rock. Although below average seasonal (December-January-February, DJF) temperatures are associated with below average peaks in total SGL area and volume, warmer seasonal temperatures do not necessarily result in higher SGL areas and volumes. Rather, peaks in total SGL area and volume show a much closer correspondence with short-lived high magnitude snowmelt events. We therefore suggest seasonal lake evolution on this ice shelf is instead more sensitive to snowmelt intensity associated with katabatic wind-driven melting. Our analysis provides important constraints on the boundary conditions of supraglacial hydrology models and numerical simulations of ice shelf stability.

Evolution of supraglacial lakes on Shackleton Ice Shelf, East Antarctica

2020 ◽  
Author(s):  
Jennifer Arthur ◽  
Chris Stokes ◽  
Stewart Jamieson ◽  
Rachel Carr ◽  
Amber Leeson
Keyword(s):  
Climate Model ◽  
High Surface ◽  
Surface Melting ◽  
East Antarctica ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Shelf Stability ◽  
Supraglacial Lakes ◽  
Surface Melt ◽  
The Impact

<p>Supraglacial lakes (SGLs) enhance surface melting and their development and subsequent drainage can flex and fracture ice shelves, leading to their disintegration. However, the seasonal evolution of SGLs and their potential influence on ice shelf stability in East Antarctica remains poorly understood, despite a number of potentially vulnerable ice shelves. Using optical satellite imagery, climate reanalysis data and surface melt predicted by a regional climate model, we provide the first multi-year analysis (1974-2019) of seasonal SGL evolution on Shackleton Ice Shelf in Queen Mary Land, which is Antarctica’s northernmost remaining ice shelf. We mapped >43,000 lakes on the ice shelf and >5,000 lakes on grounded ice over the 45-year analysis period, some of which developed up to 12 km inland from the grounding line. Lakes clustered around the ice shelf grounding zone are strongly linked to the presence of blue ice and exposed rock, associated with an albedo-lowering melt-enhancing feedback. Lakes either drain supraglacially, refreeze at the end of the melt season, or shrink in-situ. Furthermore, we observe some relatively rapid (≤ 7 days) lake drainage events and infer that some lakes may be draining by hydrofracture. Our observations suggest that enhanced surface meltwater could increase the vulnerability of East Antarctic ice shelves already preconditioned for hydrofracture, namely those experiencing high surface melt rates, firn air depletion, and extensional stress regimes with minimum topographic confinement. Our results could be used to constrain simulations of current melt conditions on the ice shelf and to investigate the impact of increased surface melting on future ice shelf stability.</p>

scholarly journals Distribution and seasonal evolution of supraglacial lakes on Shackleton Ice Shelf, East Antarctica

2020 ◽  
Vol 14 (11) ◽  
pp. 4103-4120 ◽  
Author(s):  
Jennifer F. Arthur ◽  
Chris R. Stokes ◽  
Stewart S. R. Jamieson ◽  
J. Rachel Carr ◽  
Amber A. Leeson
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
East Antarctica ◽  
Katabatic Wind ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Shelf Stability ◽  
Seasonal Evolution ◽  
Supraglacial Lakes ◽  
Area And Volume

Abstract. Supraglacial lakes (SGLs) enhance surface melting and can flex and fracture ice shelves when they grow and subsequently drain, potentially leading to ice shelf disintegration. However, the seasonal evolution of SGLs and their influence on ice shelf stability in East Antarctica remains poorly understood, despite some potentially vulnerable ice shelves having high densities of SGLs. Using optical satellite imagery, air temperature data from climate reanalysis products and surface melt predicted by a regional climate model, we present the first long-term record (2000–2020) of seasonal SGL evolution on Shackleton Ice Shelf, which is Antarctica's northernmost remaining ice shelf and buttresses Denman Glacier, a major outlet of the East Antarctic Ice Sheet. In a typical melt season, we find hundreds of SGLs with a mean area of 0.02 km2, a mean depth of 0.96 m and a mean total meltwater volume of 7.45×106 m3. At their most extensive, SGLs cover a cumulative area of 50.7 km2 and are clustered near to the grounding line, where densities approach 0.27 km2 km−2. Here, SGL development is linked to an albedo-lowering feedback associated with katabatic winds, together with the presence of blue ice and exposed rock. Although below-average seasonal (December–January–February, DJF) temperatures are associated with below-average peaks in total SGL area and volume, warmer seasonal temperatures do not necessarily result in higher SGL areas and volumes. Rather, peaks in total SGL area and volume show a much closer correspondence with short-lived high-magnitude snowmelt events. We therefore suggest seasonal lake evolution on this ice shelf is instead more sensitive to snowmelt intensity associated with katabatic-wind-driven melting. Our analysis provides important constraints on the boundary conditions of supraglacial hydrology models and numerical simulations of ice shelf stability.

scholarly journals Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes

2020 ◽  
Vol 12 (1) ◽  
pp. 134 ◽  
Author(s):  
Mahsa Moussavi ◽  
Allen Pope ◽  
Anna Halberstadt ◽  
Luke Trusel ◽  
Leanne Cioffi ◽  
...  
Keyword(s):  
Landsat 8 ◽  
Ice Shelf ◽  
Surface Hydrology ◽  
Ice Shelves ◽  
Supraglacial Lakes ◽  
Dual Sensor ◽  
Extensive Surface ◽  
Image Pairs ◽  
The Antarctic ◽  
Sentinel 2

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.

scholarly journals Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach

2020 ◽  
Vol 12 (7) ◽  
pp. 1203 ◽  
Author(s):  
Mariel Dirscherl ◽  
Andreas J. Dietz ◽  
Christof Kneisel ◽  
Claudia Kuenzer
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Large Scale ◽  
Confusion Matrix ◽  
Potential Contribution ◽  
Ice Shelf ◽  
Automated Mapping ◽  
Supraglacial Lakes ◽  
Amery Ice Shelf ◽  
Sentinel 2

Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km2). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.

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