Mapping of glacier extents from automated classification of optical satellite images has become a major application of the freely available images from Landsat. A widely applied method is based on segmented ratio images from a red and shortwave infrared band. With the now available data from Sentinel-2 (S2) and Landsat 8 (L8) there is high potential to further extend the existing time series (starting with Landsat 4/5 in 1982) and to considerably improve over previous capabilities, thanks to increased spatial resolution and dynamic range, a wider swath width and more frequent coverage. Here, we test and compare a variety of previously used methods to map glacier extents from S2 and L8, and investigate the mapping of snow facies with S2 using top of atmosphere reflectance. Our results confirm that the band ratio method works well with S2 and L8. The 15 m panchromatic band of L8 can be used instead of the red band, resulting in glacier extents similar to S2 (0.7% larger for 155 glaciers). On the other hand, extents derived from the 30 m bands are 4%–5% larger, indicating a more generous interpretation of mixed pixels. Mapping of snow cover with S2 provided accurate results, but the required topographic correction would benefit from a better orthorectification with a more precise DEM than currently used.

The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling 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. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of 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 an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km² when considering 4395 glaciers >0.01 km². This is 14 % (−1.2 % a⁻¹) less than the 2100 km² derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s.l., with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at https://doi.org/10.1594/PANGAEA.909133 (Paul et al., 2019).

Mapping snow cover (SC) on glaciers at the end of the ablation period provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is a metric for the mass balance. Satellite determination of glacier snow cover, derived over large regions, can reveal its spatial variability and temporal trends. Accordingly, snow mapping on glaciers has been widely applied using several satellite sensors. However, as glacier ice originates from compressed snow, both have very similar spectral properties and standard methods to map snow struggle to distinguish snow on glaciers. Hence, most studies applied manual delineation of snow extent on glaciers. Here we present an automated tool, named ‘ASMAG’ (automated snow mapping on glaciers), to map SC on glaciers and derive the related snow line altitude (SLA) for individual glaciers using multi-temporal Landsat satellite imagery and a digital elevation model (DEM). The method has been developed using the example of the Ötztal Alps, where an evaluation of the method is possible using field-based observations of the annual equilibrium line altitude (ELA) and the accumulation area ratio (AAR) measured for three glaciers for more than 30 years. The tool automatically selects a threshold to map snow on glaciers and robustly calculates the SLA based on the frequency distribution of elevation bins with more than 50% SC. The accuracy of the SC mapping was about 90% and the SLA was determined successfully in 80% of all cases with a mean uncertainty of ±19 m. When cloud-free scenes close to the date of the highest snowline are available, a good to very good agreement of SC ratios (SCR)/SLA with field data of AAR/ELA are obtained, otherwise values are systematically higher/lower as useful images were often acquired too early in the summer season. However, glacier specific differences are still well captured. Snow mapping on glaciers is impeded by clouds and their shadows or when fresh snow is covering the glaciers, so that more frequent image acquisitions (as now provided by Sentinel-2) would improve results.

Monitoring of plant succession in glacier forelands has so far been restricted to field sampling. In this study, in situ vegetation sampling along a chronosequence between Little Ice Age (LIA) maximum extent and the recent glacier terminus at Jamtalferner in the Austrian Alps is compared to time series of the Normalized Difference Vegetation Index (NDVI) calculated from 13 Landsat scenes (1985–2016). The glacier terminus positions at 16 dates between the LIA maximum and 2015 were analysed from historical maps, orthophotos and LiDAR images. We sampled plots of different ages since deglaciation, from very recent to approx. 150 years: after 100 years, roughly 80% of the ground is covered by plants and ground cover does not increase significantly thereafter. The number of species increases from 10–20 species on young sites to 40–50 species after 100 years. The NDVI increases with the time of exposure from a mean of 0.11 for 1985–1991 to 0.20 in 2009 and 0.27 in 2016. As the increase in ground cover is clearly reproduced by the NDVI (R² ground cover/NDVI 0.84) – even for sparsely vegetated areas –, we see a great potential of satellite-borne NDVI to perform regional characterizations of glacier forelands for hydrological, ecological and hazard management-related applications.

Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.

A new high-resolution glacier inventory captures the rapid decay of the glaciers in the Austrian Silvretta for the years 2017 and 2018. Identifying the glacier outlines offers a wide range of possible interpretations of glaciers that have evolved into small and now totally debris-covered cryogenic structures. In previous inventories, a high proportion of active bare ice allowed a clear delineation of the glacier margins even by optical imagery. In contrast, in the current state of the glacier only the patterns and amounts of volume change allow us to estimate the area of the buried glacier remnants. We mapped the glacier outlines manually based on lidar elevation models and patterns of volume change at 1 to 0.5 m spatial resolution. The vertical accuracy of the digital elevation models (DEMs) generated from six to eight lidar points per square metre is of the order of centimetres. Between 2004/2006 and 2017/2018, the 46 glaciers of the Austrian Silvretta lost −29 ± 4 % of their area and now cover 13.1 ± 0.4 km². This is only 32 ± 2 % of their Little Ice Age (LIA) extent of 40.9 ± 4.1 km². The area change rate increased from 0.6 %/yr (1969–2002) to −2.4 %/yr (2004/2006–2017/2018). The Sentinel-2-based glacier inventory of 2018 deviates by just 1 % of the area. The annual geodetic mass balance referring to the area at the beginning of the period showed a loss increasing from −0.2 ± 0.1 m w.e./yr (1969–2002) to −0.8 ± 0.1 m w.e./yr (2004/2006–2017/2018) with an interim peak in 2002–2004/2006 of −1.5 ± 0.7 m w.e./yr. To keep track of the buried ice and its fate and to distinguish increasing debris cover from ice loss, we recommend inventory repeat frequencies of 3 to 5 years and surface elevation data with a spatial resolution of 1 m.