Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Maximum Extent

The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017.  To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.

This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&reserved=0) . Landsat-8 and Sentinel-2 imagery are freely available at  (https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&reserved=0) and (https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&reserved=0), respectively.

The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.

本数据集针对2017年1月的西南极洲与南极半岛区域，采用带阈值的双归一化差异水体指数（Dual-NDWI, Normalised Difference Water Index）方法提取了该区域内冰面湖泊与河道的最大分布范围。2017年1月整月期间，Sentinel-2（S2）与Landsat-8（L8）卫星传感器共采集了超过2000单景独立影像。为获取多云的南极半岛区域的最大影像覆盖范围，该区域的影像采集时间窗口被延长至2017年2月10日。

本数据集包含2017年1月西南极冰盖与南极半岛区域内冰面水文活动的最大分布范围，以及共计10478处精细冰面水文要素（其中10223处冰面湖与255处冰面河道），总累积面积达119.4平方千米。除最终成品成果外，与最终成果图叠加的双传感器冰面水文要素（S2传感器对应23389个多边形矢量，L8传感器对应17571个多边形矢量）也被纳入补充数据集。冰面湖与河道的多边形矢量可获取为数字地理信息系统（GIS, Geographic Information System）格式、形状文件（.shp）、GeoJSON文件以及谷歌地球格式（.kmz）。用于生成双传感器（S2与L8）冰面湖与河道数据集的代码采用Python实现，可在Zenodo平台获取，访问链接为：https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&reserved=0。Landsat-8与Sentinel-2影像可分别从美国地质调查局地球探索者平台（https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&reserved=0）与哥白尼科学数据中心（https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&reserved=0）免费获取。

本数据集产品可为变暖气候下的冰面水文要素动态监测提供科学基准，进而提升我们对未来冰架崩解与坍塌的预测能力。本次成果可为后续冰面水文监测提供基准基线，尤其适用于监督机器学习算法的训练。冰面湖与河道数据集可作为训练数据，用于基于像元或基于对象的机器学习自动制图方法，以提取大规模冰面水文要素。此外，本数据集还可为合成孔径雷达（SAR）及其他传感器与平台相关的冰面湖分布先验研究提供参考。