This dataset provides a glacier inventory for the Svalbard Archipelago, derived from cloud-free Sentinel-2 satellite imagery acquired between August 28 and September 14, 2021. Glaciers larger than 0.01 km² were delineated using a semi-automated mapping approach followed by manual correction. Glaciers were subdivided based on their respective drainage basins derived from the ArcticDEM (Porter et al., 2023). The inventory includes geometric and topographic attributes for each glacier, which are: names of the glaciers, identification code of the glaciers based on 5 digits (Hagen et al., 1993), area, elevation, slope, aspect, and Global Land Ice Measurements from Space (GLIMS) x, y and ID.The dataset is part of a manuscript under consideration for publication.

This dataset provides a glacier inventory for the Svalbard Archipelago, derived from cloud-free Sentinel-2 satellite imagery acquired between August 28 and September 14, 2021. Glaciers larger than 0.01 km² were delineated using a semi-automated mapping approach followed by manual correction. Glaciers were subdivided based on their respective drainage basins derived from the ArcticDEM (Porter et al., 2023). The inventory includes geometric and topographic attributes for each glacier, which are: names of the glaciers, identification code of the glaciers based on 5 digits (Hagen et al., 1993), area, elevation, slope, aspect, and Global Land Ice Measurements from Space (GLIMS) x, y and ID.The dataset is part of a manuscript under consideration for publication.

This dataset provides a glacier inventory for the Svalbard Archipelago, derived from cloud-free Sentinel-2 satellite imagery acquired between August 28 and September 14, 2021. Glaciers larger than 0.01 km² were delineated using a semi-automated mapping approach followed by manual correction. Glaciers were subdivided based on their respective drainage basins derived from the ArcticDEM (Porter et al., 2023). The inventory includes geometric and topographic attributes for each glacier, which are: names of the glaciers, identification code of the glaciers based on 5 digits (Hagen et al., 1993), year of detection, analyst name, satellite source, DEM source, GLIMS IDs, longitude and latitude, area, elevation statistics, slope and aspect.The dataset is part of a manuscript under consideration for publication.

The on-going glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring or modeling purposes (e.g. determination of run-off, mass balance, or future glacier extent) and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. Fortunately, already the first S2 images acquired in August 2015 provided excellent mapping conditions for most of the glacierised regions in the Alps. We have used this opportunity to compile a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile a consistent update. However, cloud cover over many glaciers in Italy required including also S2 scenes from 2016. Whereas the automated mapping of clean glacier ice was straightforward using the band ratio method, the numerous debris-covered glaciers required intense manual editing. The uncertainty in the outlines was determined with multiple digitising of 14 glaciers by all participants. Topographic information for all glaciers was derived from the ALOS AW3D30 DEM. Overall, we derived a total glacier area of 1806 ±60 km² when considering 4394 glaciers >0.01 km². This is 14% (-1.2%/a) less than the 2100 km² derived from Landsat scenes acquired in 2003 and indicating an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. Due to the higher spatial resolution of S2 many small glaciers were additionally mapped in the new inventory or increased in size compared to 2003. An artificial reduction to the former extents would thus result in an even higher overall area loss. Still, the uncertainty assessment revealed locally considerable differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitised by different analysts.