In this study, we assess the accuracy and precision of digital elevation models (DEM) retrieved from aerial photographs taken in 2011 and from Very High Resolution satellite images (WorldView-2 and Pléiades) from the period 2012–2017. Additionally, the accuracy of the freely available Strip product of ArcticDEM was verified. We use the DEMs to characterize geometry changes over Hansbreen and Hornbreen, two tidewater glaciers in southern Spitsbergen, Svalbard. The satellite-based DEMs from WorldView-2 and Pléiades stereo pairs were processed using the Rational Function Model (RFM) without and with one ground control point. The elevation quality of the DEMs over glacierized areas was validated with in situ data: static differential GPS survey of mass balance stakes and GPS kinematic data acquired during ground penetrating radar survey. Results demonstrate the usefulness of the analyzed sources of DEMs for estimation of the total geodetic mass balance of the Svalbard glaciers. DEM accuracy is sufficient to investigate glacier surface elevation changes above 1 m. Strips from the ArcticDEM are generally precise, but some of them showed gross errors and need to be handled with caution. The surface of Hansbreen and Hornbreen has been lowering in recent years. The average annual elevation changes for Hansbreen were more negative in the period 2015–2017 (−2.4 m a⁻¹) than in the period 2011–2015 (−1.7 m a⁻¹). The average annual elevation changes over the studied area of Hornbreen for the period 2012–2017 amounted to −1.6 m a⁻¹. The geodetic mass balance for Hansbreen was more negative than the climatic mass balance estimated using the mass budget method, probably due to underestimation of the ice discharge. From 2011 to 2017, Hansbreen lost on average over 1% of its volume each year. Such a high rate of relative loss illustrates how fast these glaciers are responding to climate change.

In recent years, the Svalbard area, especially its southern section, has been characterised by an exceptionally thin snow cover, which has a significant impact of the annual mass balance of glaciers. The objective of this study was to determine melting processes of the snow cover deposited on 11 glaciers that terminate into Hornsund Fjord during the melting period of 2014. The study included analyses of snow pits and snow cores, meteorological data collected from automatic weather stations and Polish Polar Station Hornsund, and supervised classification of six Landsat 8 images for assessing the progress of snow cover melting. The calculated Snow-Covered Area (SCA) varied from 98% at the beginning of the melting season to 43% at the end of August. The melting vertical gradient on Hansbreen was −0.34 m 100 m⁻¹, leading to surface melting of −1.4 cm water equivalent (w.e.) day⁻¹ in the ablation zone (c. 200 m a.s.l. (above sea level)) and −0.7 cm w.e. day⁻¹ in the accumulation zone (c. 400 m a.s.l.). Furthermore, the study identified several observed features such as low snow depth in the accumulation zone of the Hornsund glaciers, a large proportion of the snow layers (12–27%) produced by rain-on-snow events, and a frequent occurrence of summer thermal inversions (80% annually), indicating that the area is experiencing intensive climate changes.

Natural mineral particulate matter deposited from aerosols and trapped in glaciers—herein defined as “cryodust”—may be an excellent indicator of atmospheric circulation, if terrestrial sources of dust can be identified. In this study, we analyzed the composition of cryodust in shallow ice cores taken from five glaciers in Southern Spitsbergen (Svalbard Archipelago, Northern Norway). The chemical composition, magnetic properties and radiogenic ages of individual grains were measured, where possible, to provide indicators of source areas. To identify mineral and rock fragments, solid particulates were examined by Scanning Electron Microscope fitted with a backscattered electron and Energy Dispersive Spectroscopic detectors. An Electron MicroProbe was employed for the U-Th-Pb chemical dating of monazite grains. Magnetic measurements comprised analyses of magnetic susceptibility (κ) vs. temperature (T) variations and determination of magnetic hysteresis parameters. Monazite ages span 445–423 Ma, consistent with mineral growth during the Caledonian orogeny. Caledonian rocks are exposed in the Nordaustlandet area of North-Eastern Svalbard, and this is the most probable source for monazite grains. Magnetic analyses show a predominance of ferrous (Fe^II) over ferric (Fe^III) phases, consistent with a lack of input from subtropical sources. The results from both methods are consistent with local sources of dust from exposures in the Svalbard archipelago.

Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data.