Changes in margins derived from satellite imagery are quantitative indicators of the environmental processes and drivers acting on the Earth's surface, for example retreating ice margins or coastal changes with rising sea level. However, the large-scale rapid visualisation and analysis of the satellite record is often impractical due to factors such as computer processing power, software availability, internet connection speed and/or user expertise in remote sensing. Here are presented three new, freely accessible tools that together can be used to process, visualise and review data from the full Landsat 4–8 and Sentinel 1–2 satellite records in seconds, enabling efficient mapping (through manual digitisation) and automated quantification of margin changes. These tools are highly accessible for users from a range of remote-sensing expertise (from academics to high school students), with minimal computational, licensing and knowledge-based barriers to access. The Google Earth Engine Digitisation Tool (GEEDiT) allows users to define a point anywhere on the planet and filter data from each satellite for user-defined time frames, maximum acceptable cloud cover extent, and options of predefined or custom image band combinations via a simple graphical user interface (GUI). GEEDiT allows georeferenced vectors to be easily and rapidly mapped from each image, with image metadata and user notes automatically appended to each vector, which can then be exported for subsequent analysis. The GEEDiT Reviewer tool allows users to quality control their own/others' data and also filter existing datasets based on the spatial/temporal requirements for their particular research question. The Margin change Quantification Tool (MaQiT) is complementary to GEEDiT and GEEDiT Reviewer, allowing the rapid quantification of these margin changes by utilising two well-established methods that have previously been used to measure glacier margin change and two new methods via a similarly simple GUI. A case study of the lake-terminating glacier Breiðamerkurjökull, Iceland, is used to demonstrate the complementary functionality of GEEDiT, GEEDiT Reviewer and MaQiT, though it should be noted that MaQiT is also suitable for the (re-)analysis of existing datasets not generated by GEEDiT. MaQiT has been developed with the original aim of quantifying tidewater glacier terminus change, though the methods included within the tool have potential for wide applications in multiple areas of Earth surface science (e.g. coastal and vegetation extent change). It is hoped that these tools will allow a wide range of researchers and students across the geosciences to efficiently map, analyse and access volumes of data that would have previously proven prohibitive.

Several different methodologies have previously been employed in the tracking of glacier terminus change, though a systematic comparison of these has not been undertaken. The frequent application of single methods to multiple glaciers over large geographical areas such as Greenland, raises the question of whether individual methodologies are robust. In this study we evaluate three existing methodologies that have been widely used to track terminus change (the centre-line, bow and box methods) against a full range of idealized glaciological scenarios and six examples of real glaciers. We also evaluate two new methodologies that aim to reduce measurement error compared with the existing methodologies. The first is a modification to the box method that can account for termini retreating through fjords that change orientation (termed the curvilinear box method), while the second determines the average terminus position relative to the glacier centre line using an inverse distance weighting extrapolation (termed the extrapolated centre-line method). No single method tested achieved complete accuracy for all scenarios, though the extrapolated centre-line method was able to successfully account for variable fjord orientation, width and terminus geometry with the least error.

We assess the runoff and surface mass balance (SMB) of the Greenland ice sheet in the Nuuk region (southwest) using output of two regional climate models (RCMs) evaluated by observations. The region encompasses six glaciers that drain into Godthåbsfjord. RCM data (1960–2012) are resampled to a high spatial resolution to include the narrow (relative to the native grid spacing) glacier trunks in the ice mask. Comparing RCM gridded results with automatic weather station (AWS) point measurements reveals that locally models can underestimate ablation and overestimate accumulation by up to tens of per cent. However, comparison with lake discharge indicates that modelled regional runoff totals are more accurate. Model results show that melt and runoff in the Nuuk region have doubled over the past two decades. Regional SMB attained negative values in recent high-melt years. Taking into account frontal ablation of the marine-terminating glaciers, the region lost 10–20 km³ w.e. a^–1 in 2010–12. If 2010 melting prevails during the remainder of this century, a low-end estimate of sea-level rise of 5 mm is expected by 2100 from this relatively small section (2.6%) of the ice sheet alone.

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet.

Gaining knowledge of tidewater glacier (TWG) margin evolution, solid ice flux and their responses to climate over large spatio-temporal scales provides valuable context for the projection of future Greenland ice sheet (GrIS) change. Although studies of sector-wide responses of TWGs exist, studies at an ice-sheet-wide scale have only just become feasible. Here, we present a dataset of 224 annual TWG margins for 1984–2017 (n= 3801), showing that averaged over regional scales, normalised terminus change is linear. Regionally linear retreat trends were identified across most sectors of the GrIS starting in the mid-1990s, although in contrast to previous studies, the northeastern sector is shown to have experienced sustained retreat since the mid-1980s. Through cointegration analyses, individual glaciers are shown to have differing sensitivities to potential climate drivers, though on a sector-wide scale the northwest and southeast are shown to be especially sensitive to annual sea surface temperature and June–July–August air temperature, respectively. Although 92% of the analysed glaciers experience retreat across the GrIS, observed increases in absolute flux for the entire ice sheet can be explained by changes in just 11 of these TWGs.

Surface meltwater is widespread around the Antarctic Ice Sheet margin and has the potential to influence ice shelf stability, ice flow and ice–albedo feedbacks. Our understanding of the seasonal and multi-year evolution of Antarctic surface meltwater is limited. Attempts to generate robust meltwater cover time series have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we add a novel method for calculating visibility metrics to an existing meltwater detection method within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing time series of surface meltwater area to be automatically generated over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 to 3898 km² per melt season. By incorporating image visibility assessments, however, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values. We show that modelled melt predictions from a regional climate model provide a good indication of lake cover in the Amery region and that annual lake coverage is typically highest in years with a negative austral summer SAM index. Our results demonstrate that our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.