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.

中文翻译：

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使用机器学习方法自动绘制南极冰川湖

冰川上湖泊通过冰架破裂和随后的冰川加速，会对冰盖质量平衡和全球海平面上升产生重大影响。在南极洲，冰川湖的分布和时间发展及其对增加冰量损失的潜在贡献仍是未知之数，需要对南极地表水文网络进行详细的制图。在这项研究中，我们采用了对Sentinel-2和辅助TanDEM-X地形数据进行训练的机器学习算法，用于自动绘制南极冰河湖泊。为了确保我们方法的时空可移植性，随机森林在14个训练区域上进行了训练，并应用于分布在整个南极大陆的八个空间独立的测试区域。此外，我们将工作流程用于在Amery冰架上的大规模应用，在该冰架上，我们计算了2017年至2020年冰架全覆盖时的年际冰川湖动力学。为了验证我们的冰川湖探测算法，我们在分类结果上随机创建了点样本，并将其与Sentinel-2影像进行了比较。使用混淆矩阵评估点比较，以计算选定的精度指标。我们的分析揭示了在所有三个南极地区广泛存在的冰川湖。我们首次确定了南极西部的雅培，赫尔和科斯格罗夫冰架上的冰川作用融水特征，以及整个2017-2020年的阿默里冰架。在阿默里冰架上²）。测试区域的准确性评估显示平均Kappa系数为0.86，其中最大Kappa值超过乔治六世冰架的0.98。未来的发展将涉及生成南极环周冰川湖测绘产品，以及利用Sentinel-1 SAR数据将其用于进一步的方法学开发，以表征极地夜间和与气象条件无关的年内冰川度融水动态。总而言之，随机森林分类器的实现实现了第一个自动映射方法的开发，该方法适用于分布在所有三个南极地区的Sentinel-2数据。