Deriving glacier outlines from satellite data has become increasingly popular in the past decade. In particular when glacier outlines are used as a base for change assessment, it is important to know how accurate they are. Calculating the accuracy correctly is challenging, as appropriate reference data (e.g. from higher-resolution sensors) are seldom available. Moreover, after the required manual correction of the raw outlines (e.g. for debris cover), such a comparison would only reveal the accuracy of the analyst rather than of the algorithm applied. Here we compare outlines for clean and debris-covered glaciers, as derived from single and multiple digitizing by different or the same analysts on very high- (1 m) and medium-resolution (30 m) remote-sensing data, against each other and to glacier outlines derived from automated classification of Landsat Thematic Mapper data. Results show a high variability in the interpretation of debris-covered glacier parts, largely independent of the spatial resolution (area differences were up to 30%), and an overall good agreement for clean ice with sufficient contrast to the surrounding terrain (differences ∼5%). The differences of the automatically derived outlines from a reference value are as small as the standard deviation of the manual digitizations from several analysts. Based on these results, we conclude that automated mapping of clean ice is preferable to manual digitization and recommend using the latter method only for required corrections of incorrectly mapped glacier parts (e.g. debris cover, shadow).

Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented. The publication of digital objects empowers scientists to manage their research products as valuable scientific assets in an open and transparent way that enables broader access by other scientists, students, decision makers, and the public. Improving documentation and dissemination of research will accelerate the pace of scientific discovery by improving the ability of others to build upon published work.

Liquid water stored on the surface of ice sheets and glaciers impacts surface mass balance, ice dynamics, and heat transport. Multispectral remote sensing can be used to detect supraglacial lakes and estimate their depth and area. In this study, we use in situ spectral and bathymetric data to assess lake depth retrieval using the recently launched Landsat 8 Operational Land Imager (OLI). We also extend our analysis to other multispectral sensors to evaluate their performance with similar methods. Digital elevation models derived from WorldView stereo imagery (pre-lake filling and post-drainage) are used to validate spectrally derived depths, combined with a lake edge determination from imagery. The optimal supraglacial lake depth retrieval is a physically based single-band model applied to two OLI bands independently (red and panchromatic) that are then averaged together. When OLI- and WorldView-derived depths are differenced, they yield a mean and standard deviation of 0.0 ± 1.6 m. This method is then applied to OLI data for the Sermeq Kujalleq (Jakobshavn Isbræ) region of Greenland to study the spatial and intra-seasonal variability of supraglacial lakes during summer 2014. We also give coefficients for estimating supraglacial lake depth using a similar method with other multispectral sensors.

Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R² value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf. The methods presented here pave the way to upscaling throughout the Landsat 8 and Sentinel-2 observational record across Antarctica to produce a first-ever continental dataset of supraglacial lake volumes. Such a dataset will improve our understanding of the influence of surface hydrology on ice shelf stability, and thus, future projections of Antarctica’s contribution to sea level rise.

We identify areas near the East Antarctic ice divide where <−90 °C surface snow temperatures are observed in wintertime satellite thermal‐band data under clear‐sky conditions. The lowest temperatures are found in small (<200 km²) topographic basins of ~2 m depth above 3,800 m elevation. Approximately 100 sites have observed minimum surface temperatures of ~−98 °C during the winters of 2004–2016. Comparisons of surface snow temperatures with near‐surface air temperatures at nearby weather stations indicate that ~−98 °C surfaces imply ~−94 ± 4 °C 2‐m air temperatures. Landsat 8 thermal band data and elevation data show gradients near the topographic depressions of ~6 °C km⁻¹ horizontally and ~4 °C m⁻¹ vertically. Ultralow temperature occurrences correlate with strong polar vortex circulation. We discuss a conceptual model of radiative surface cooling that produces an extreme inversion layer. Further cooling occurs as near‐surface cold air pools in shallow high‐elevation topographic basins, moderated by clear‐air downwelling radiation and heat from subsurface snow.

Strain rates are fundamental measures of ice flow and are used in a wide variety of glaciological applications including investigations of bed properties, calculations of basal mass balance on ice shelves, and constraints on ice rheological models. However, despite their extensive application, strain rates are calculated using a variety of methods and length scales and the details are often not specified. In this study, we compare the results of nominal and logarithmic strain-rate calculations based on a satellite-derived velocity field of the Antarctic ice sheet generated from Landsat 8 satellite data. Our comparison highlights the differences between the two common approaches in the glaciological literature. We evaluate the errors introduced by each approach and their impacts on the results. We also demonstrate the importance of choosing and specifying a length scale over which strain-rate calculations are made, which can strongly influence other derived quantities such as basal mass balance on ice shelves. Finally, we present strain-rate data products calculated using an approximate viscous length-scale with satellite observations of ice velocity for the Antarctic continent.