A high-resolution January surface meltwater dataset reveals interannual variability and recurrent meltwater zones on the Amery Ice Shelf from 2005 to 2026
2026-09-02
Surface meltwater on Antarctic ice shelves commonly occurs as supraglacial lakes, ponded-water patches and meltwater channels, and its storage and drainage can modify ice-shelf surface hydrology, increase surface loading and promote hydrofracture where water is routed into crevassed regions. Previous studies have mapped surface meltwater on the Amery Ice Shelf (AmIS) using satellite remote sensing and machine-learning approaches, but they have mainly focused on selected years, local drainage systems, individual melt events or algorithm evaluation. Due to the diagnostic value of January peak surface-meltwater extent for assessing AmIS surface hydrology and ice-shelf stability, a long-term, high-resolution and consistently processed dataset is needed for reliable interannual comparison. Here, we present a high-resolution January surface meltwater dataset for the AmIS from 2005 to 2026. The cross-sensor dataset was generated from Landsat 7 ETM+, Landsat 8 OLI and Sentinel-2 MSI imagery using a weakly supervised segmentation framework with source-adaptive components trained using NDWI-derived labels. The dataset includes annual January surface meltwater extent maps, annual surface meltwater area statistics and a multi-year surface meltwater occurrence-count product. All raster products are provided on a common 10 m product grid to support spatial comparison across image sources and years. For Landsat-derived products, this grid is used for spatial alignment, while the source Landsat multispectral bands retain their native 30 m spatial resolution. Technical validation showed high pixel-level agreement, with an overall accuracy of 0.9882, precision of 0.9507, recall of 0.9086, F1 score of 0.9291, intersection over union of 0.8677 and kappa coefficient of 0.9227. Compared with threshold-based methods, conventional machine-learning classifiers and multiple deep-learning models, our method achieved the highest F1 score, IoU and kappa. Component-wise ablation experiments and representative visual and boundary comparisons indicated improved delineation of narrow channels, small ponded-water features and continuous lake margins. The 2005–2026 dataset shows pronounced interannual variability in January surface meltwater extent, with relatively extensive surface meltwater coverage in 2005–2006, 2014, 2017, 2019 and 2025 and reduced coverage in 2007, 2011, 2021 and 2023. The occurrence-count product identifies recurrent January surface meltwater mainly across the western and central AmIS, with additional clusters along the southeastern ice-shelf margin. Recurrent surface meltwater is more concentrated near rock outcrops, varies across surface-elevation bands and shows spatial correspondence with MEaSUREs ice-velocity fields and ice-flow direction patterns. The dataset can be combined with runoff, surface mass balance, crevasse fields and MEaSUREs ice-velocity products to support studies of peak-season surface hydrology, recurrent surface meltwater pathways and stability-relevant surface meltwater settings on the AmIS.