We explore and compare the capabilities and limitations of different optical sensors (Sentinel-2/ESA, Landsat 7/8/USGS, Ven $\mathsf{\mu }$ s/CNES-ISA, Pléiades/AirbusD&S and Planet Labs images) for mapping the surface speeds of mountain glaciers on a regional scale. We present here our automated workflow designed to download data from institutional or commercial servers, prepare images, launch the feature tracking algorithm, calibrate glacier surface speeds, and our post-processing treatment to obtain filtered and time-averaged velocity maps. We applied our methodology to three regions: (1) the European alps; (2) the Peruvian Cordillera Blanca; and (3) the Southern Alps of New Zealand for years 2017 and 2018 and quantified ice velocity for every possible repeat cycle from few days up to 400 days. For these regions, we demonstrate the ability of our processing chain to derive precise time-averaged ice flow maps. The statistical analysis of the results provided by each individual repeat cycles shows that velocity mapping from Sentinel-2 is about twice more precise than that from Landsat 7/8. If Sentinel-2 captures more details than Landsat, some of the smallest glaciers (<250 m wide) remain challenging. Given the estimated precision for Sentinel-2, we also conclude that velocity fluctuations of the order of 10 m/yr can only be captured with repeat cycles longer than 60 days. Comparing Sentinel-2 with Pléiades, Planet and Ven $\mathsf{\mu }$ s imagery, we finally highlight the advantages of high-resolution sensors to map glacier surface speed with finer details in space and time.

During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relatively large intrinsic errors associated with the measurements. Here, we process and combine together measurements of ice velocity of Russell Gletscher in Greenland from three satellites—Sentinel-1, Sentinel-2, and Landsat-8, creating a multi-year velocity database with high temporal and spatial resolution. We then investigate post-processing methodologies with the aim of generating corrected, ordered, and simplified time series. We tested rolling mean and median, cubic spline regression, and linear non-parametric local regression (LOWESS) smoothing algorithms to reduce data noise, evaluated the results against ground-based GPS in one location, and compared the results between two locations with different characteristics. We found that LOWESS provides the best solution for noisy measurements that are unevenly distributed in time. Using this methodology with these sensors, we can robustly derive time series with temporal resolution of 2–3 weeks and improve the accuracy on the ice velocity to about 10 m/yr, or a factor of three compared to the initial measurements. The presented methodology could be applied to the entire Greenland ice sheet with an aim of reconstructing comprehensive sub-seasonal ice flow dynamics and mass balance.

The Petermann ice shelf is one of the largest in Greenland, buttressing 4 % of the total ice sheet discharge, and is considered dynamically stable. In this study, we use differential synthetic aperture radar interferometry to reconstruct the grounding line migration between 1992 and 2021. Over the last 30 years, we find that the grounding line of Petermann retreated 4 km in the western and eastern sectors and 7 km in the central part. The majority of the retreat in the central sector took place between 2017 and 2021, where the glacier receded more than 5 km along a retrograde bed grounded 500 m below sea level. While the central sector stabilized on a sill, the eastern flank is sitting on top of a down-sloping bed, which might enhance the glacier retreat in the coming years. This grounding line retreat followed a speedup of the glacier by 15 % in the period 2015–2018. Along with the glacier acceleration, two large fractures formed along flow in 2015, splitting the ice shelf in three sections, with a partially decoupled flow regime. While these series of events followed the warming of the ocean waters by 0.3 ^∘C in Nares Strait, the use of a simple grounding line model suggests that enhanced submarine melting may have been responsible for the recent grounding line migration of Petermann Glacier.

Due to increasing surface melting on the Greenland ice sheet, better constraints on seasonally evolving basal water pressure and sliding speed are required by models. Here we assess the potential of using inverse methods on a dense time series of surface speeds to recover the seasonal evolution of the basal conditions in a well-documented region in southwest Greenland. Using data compiled from multiple satellite missions, we document seasonally evolving surface velocities with a temporal resolution of 2 weeks between 2015 and 2019. We then apply the inverse control method using the ice flow model Elmer/Ice to infer the basal sliding and friction corresponding to each of the 24 surface velocity data sets. Near the margin where the uncertainty in the velocity and bed topography are small, we obtain clear seasonal variations that can be mostly interpreted in terms of an effective-pressure-based hard-bed friction law. We find for valley bottoms or “troughs” in the bed topography that the changes in modelled basal conditions directly respond to local modelled water pressure variations, while the link is more complex for subglacial “ridges” which are often non-locally forced. At the catchment scale, in-phase variations in the water pressure, surface velocities, and surface runoff variations are found. Our results show that time series inversions of observed surface velocities can be used to understand the evolution of basal conditions over different timescales and could therefore serve as an intermediate validation for subglacial hydrology models to achieve better coupling with ice flow models.

We consider the relationship between leaf area index (LAI) and normalized difference vegetation index (NDVI) for green-leaf vegetation from a subarctic study site, specifically to test whether relationships optimized for lower-latitude vegetation can be assumed to hold at higher latitudes. We focus attention particularly on dwarf-shrub vegetation, which has received little previous investigation. We have collected hyperspectral measurements of the optical properties (reflectance and absorptance) of single leaves from dwarf shrub and tree species common to northern European Russia, and have developed a simple physical model of the properties of assemblages (‘leaf stacks’) of these leaves. The model is shown to provide a satisfactory explanation of the effect of varying the number of leaves in a stack on its NDVI, and can be easily adapted to make simple measurements using relatively inexpensive equipment. Our results show that the LAI-NDVI relationship for a vegetation canopy will saturate (approach within 10% of its limiting value) when the LAI reaches a value of around 2 to 3. Values this low are not uncommon in subarctic vegetation. It is also shown that dwarf shrub vegetation may show lower NDVI than trees for the same LAI.