The internal structure of glaciers evolves primarily due to their thermal state, which is influenced by ongoing climate change. Radio-echo sounding is a technique that indirectly identifies water-saturated temperate ice (W-STI) and water-free ice (W-FI) within glaciers. A novel automatic image processing method based on local binarization has been developed to improve the accuracy and efficiency of identifying these layers. Applied to the Arctic glacier Hansbreen from 2007 to 2021, this technique revealed that the glacier’s internal structure evolved from a two-layer system to a nearly homogeneous structure composed mainly of temperate ice (Kachniarz et al. 2025). The dataset contains raw GPR data from 2007 - 2021 taken in the Hansbreen ablation zone used to identify the glacier's internal structure. The profiles are divided into two sections: upper and lower areas. The upper area includes GPR profiles intended to replicate the 2003 GPR profile. The lower area consists of profiles shifted down the glacier, corresponding to the glacier’s movement since 2003. The profile lengths range from 0.7 km to 1.7 km, with the 2016 lower and 2021 upper areas, respectively. In the first season (2007), the GPR profiles were situated at altitudes between 188 and 216 meters above sea level, running transversely to the glacier’s movement. The glacier’s internal structure was examined using GPR system with unshielded 25 MHz, Rough Terrain Antenna (RTA) 30 MHz, and RTA 50 MHz antennas. Image processing Python script based on local binarization and processed image examples have been included in the dataset. See details in Kachniarz et al. 2025. The field data collection and/or processing received grant aid from: Svalbard Integrated Arctic Earth Observing System (SIOS) (SnowInOpt: SIOS Infrastructure Optimisation of the Cal/Val process for the snow research), European Commission Horizon Europe HORIZON-CL5-2024-D1-01-02 (LIQUIDICE: LinkIng and QUantifying the Impacts of climate change on inlanD ICE, snow cover, and permafrost on water resources and society in vulnerable regions) 101184962, the National Centre for Research and Development within the Polish-Norwegian Research Cooperation Programme (AWAKE2 project Pol Nor/198675/17/2013), Polish-Norwegian funding (AWAKE project PNRF-22-AI-1/07), Polish Ministry of Science and Higher Education (GLACIODYN No. IPY/269/2006), PolishNational Centre for Research and Development (SvalGlac project No. NCBiR/PolarCLIMATE-2009/2-2/2010), European Union 7th Framework Programme (ice2sea programme, grant no. 226375. Glaciological data were processed under assessment of the University of Silesia data repository within project Integrated Arctic Observing System (INTAROS, European Union’s Horizon 2020 Research and Innovation Programme—grant No. 727890). The work was supported by the Centre for Polar Studies (the Leading National Research Centre in Earth Sciences for 2014–2018) funding, No. 03/KNOW2/2014. Reference: Kachniarz K., Grabiec M., Wróbel K., Ignatiuk D. 2025: Glacier internal structure revealed by automatic image processing-powered classification of radar images. Applied Geomatics (in review)

Glaciers facies extents of Langjökull delivered from unsupervised classifications of fully-polarimetric SAR data (ALOS-2 PALSAR, RADARSAT-2) for 2018 year. Date of SAR images acquisitions: 12, 16 Mar 2018 (Fine Quad Pol RADARSAT-2), 17 Mar 2018 (High Sensitive Quad Pol ALOS-2 PALSAR). Method of classification: H-a Wishart Classification. Results validated with terrestrial measurements (shallow ice cores drilling, Ground Penetrating Radar measurements). Research done with cooperation with University of Iceland and supported by the European Space Agency, Third Party Miassions. Overwiew of results of RADATSAT-2 (16 Mar 2018; Fine Quad Pol) classification of south part of Langjökull. Black line - contour of Langjökull; other colours - different scattering properties of SAR microwaves. For more details please contact Barbara Barzycka (bbarzycka@us.edu.pl).

Glaciers facies (ice, superimposed ice, firn) extents of Hansbreen, Storbreen and Flatbreen delivered from unsupervised classifications of single, dual and fully-polarimetric SAR data (ALOS-2 PALSAR, RADARSAT-2, Sentinel-1, ERS-2 SAR) between 2008 and 2018. Methods of classification: unsupervised ISO classification, H-a Wishart Classification. Results validated with terrestrial measurements (shallow ice cores drilling, Ground Penetrating Radar measurements). Research supported by the European Space Agency, Third Party Missions grant and Svalbard Science Forum, Arctic Field Grant 2018. For more details, please e-mail to bbarzycka@us.edu.pl.

Time-lapse cameras In order to determine the state of coverage of the area, e.g. period of snow cover on a tundra, the extent of the glacier front, etc., it is necessary to perform photographic imaging at a specific time interval. This will allow for precise diagnosis of snow conditions. The camera is installed at the main entrance to the NCU Polar Station. The lens is pointed in a westerly direction, towards Prins Karls Forland CRIOS – Cryosphere Integrated Observation Network on Svalbard Project financed from the EEA Financial Mechanism 2014-2021 operated by the National Science Centre in Poland Agreement no. UMO-2022/43/7/ST10/00001 to a predefined project no. 2022/43/7/ST10/00001 Project period: 08.09.2022 - 30.04.2024 (2029)

Dataset contains results of GPR survey performed with 800 MHz antennas for snow depth calculation. Fieldwork has been done during peak of accumulation, along repeated profiles on several glaciers in the region of Hornsund, Svalbard: Amundsenisen (2013) Werenskioldbreen (2013, 2014, 2015) Nannbreen (2013) Ariebreen (2014) Flatbreen (2018) Storbreen (2018) Acknowledgements: Research Council of Norway, Arctic Field Grant 2013: Spatial distribution of snow cover and drainage systems on the glaciers on Wedel Jarlsberg Land (RiS ID: 6158); the National Science Centre PRELUDIUM 4: Role of meltwater from snow cover for supplying drainage systems of the Spitsbergen glaciers (2012/07/N/ST10/03784); References: LASKA M.,GRABIEC M.,IGNATIUK D.,BUDZIK T.,2017. Snow deposition patterns on southern Spitsbergen glaciers, Svalbard, in relation to recent meteorological conditions and local topography. Geografiska Annaler, Series A: Physical Geography, 99(3): 262–287. doi:10.1080/04353676.2017.1327321

Thermal structure of selected S Spitsbergen glaciers was derived from ground based radio-echo sounding (RES). The division between cold and temperate ice layers is based on indirect interpretation of GPR (ground penetrating radar) image. Cold ice layer is virtually “transparent” for radio waves, while temperate ice layer is characterised by numerous diffractions on water inclusions. The database contains results from 479.7 km of RES profiles acquired in 2007-2014 on 12 glaciers in Wedel Jarlsberg Land and Torell Land (S Spitsbergen) including: Amundsenisen, Austre Torellbreen, Vestre Torellbreen, Hansbreen, Storbreen, Hornbreen, Hambergbreen, Recherchebreen, Scottbreen, Renardbreen, Werenskioldbreen and Ariebreen. Basic characteristics of investigated glaciers and its thermal structure is provided in table 1 (supplementary information). The surveys used GPR antennas in range 25-200 MHz, selected according to expected ice depth. Thanks to that on 87% of the profiles ice/bed interface has been identified. The radar system was pulled behind the snowmobile moving with velocity c. 20 km h-1. Applying trace interval 0.2-1.0 s, trace-to-trace distance was in range 1-5m. Trace positions were acquired by GNSS receivers working in navigation or differential mode with respective accuracy 3.0 m and 0.1m. RES data were processed applying standard filtering procedure (DC-offset, time-zero adjustment, 2-D filter, amplitude correction and bandpass filtering). Time-to-depth conversion used average radio wave velocity (RWV) for glacier ice 16.4 cm ns-1, 16.7 and 16.1 for cold and temperate ice respectively, based on CMP survey. More precise description of data collection, processing and quality is provided by Grabiec (2017). In S Spitsbergen polythermal glaciers are predominant. 57.8% of surveyed profiles consist of both: temperate and cold ice layers; 22.7% profiles is entirely temperate while 6.6% contains cold ice only (remaining profiles have undefined thermal structure). Studied glaciers represent broad spectrum of polythermal structure with cold-to-temperate ice ratio from 99:1% (Ariebreen) to 2:98% (accumulation zone of Vestre Torellbreen). The data were collected and processed under following projects: • IPY/269/2006 GLACIODYN The dynamic response of Arctic glaciers to global warming • UE FP7-ENV-2008-1 ice2sea Estimating the future contribution of continental ice to sea-level rise • PNRF-22-AI-1/07 AWAKE Arctic Climate and Environment of the Nordic Seas and the Svalbard – Greenland Area • NCBiR/PolarCLIMATE-2009/2-1/2010 SvalGlac Sensitivity of Svalbard glaciers to climate change • Pol-Nor/198675/17/2013 AWAKE-2 Arctic climate system study of ocean, sea ice and glaciers interactions in Svalbard area • 03/KNOW2/2014 KNOW Leading National Research Centre Reference: Grabiec M. 2017: Stan i współczesne zmiany systemów lodowcowych południowego Spitsbergenu w świetle badań metodami radarowymi. Wydawnictwo Uniwersytetu Śląskiego, 328 s.

The positions of the glacier termini in Hornsund are derived with very high frequency in the period 1991–2018. Over 230 multispectral and Synthetic Aperture Radar (SAR) data were used: LANDSAT 5, LANDSAT 7, LANDSAT 8, Terra ASTER, Alos AVNIR, SPOT 5, ERS-1, ERS-2, ENVISAT, Alos PALSAR, TerraSAR-X, TanDEM-X, and Sentinel-1. SAR data were used to detect any variability in the glacier front during the polar night. The satellite data were digitized manually to obtain the ice cliff position. Multispectral images were orthorectified and geocoded in PCI Geomatica and ArcGIS software. SAR data were usually provided at the SLC level, so that both radiometric and geometric corrections could be applied using the same methods, and with the same digital elevation model (2008 DEM SPOT developed by the IPY-SPIRIT Project; Korona et al., 2009). The SAR data were processed in BEAM (http://www.brockmann-consult.de/cms/web/beam). Sentinel data downloaded from the Sentinel’s Data Hub were already processed. Data not published.

Inventory of tidewater and landbased glaciers delineated from archival satellite images (ASTER, Landsat) for Southern Spitsbergen. Tidewater glaciers are delineated based on satellite data from 2010. Landbased glacier are extracted from different satellite data (2005-2010). Shapefiles have basic information in the attribute table (name, ID, area, type of glaciers). Detailed description of the source data and accuracy data for glaciers from hydrological basin of Hornsund can be found in: Błaszczyk M., Jania J.A., Kolondra L., 2013: Fluctuations of tidewater glaciers in Hornsund Fiord (Southern Svalbard) since the beginning of the 20th century. Polish Polar Research, 34( 4): 327-352.http://journals.pan.pl/dlibra/publication/114504/edition/99557/content

1. Dataset of UAV vertical aerial photos of front and ice melange in the north part of Tunabreen, obtained in spring 2017. Area covered: ~1600x~800m. Used UAV: Phantom 3 Advanced, flight height: 100m. Data obtained during fieldwork supported by Svalbard Science Forum, Arctic Field Grant 2017 with cooperation with University of Maine (USA) and the University Centre in Svalbard (Norway).

Digital elevation model (DEM) combined from DEM derived from aerial images captured in 2020 over Hornsund, Svalbard by Dornier aircraft and from terrestrial laser scanner campaign in 2021. Both DEMs contain data gaps; therefore, they were combined to create a continuous product with the best possible accuracy and resolution. The dataset is the supplement to the paper: Błaszczyk, M.; Laska, M.; Sivertsen, A.; Jawak, S.D. Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard. Remote Sens. 2022, 14, 601. https://doi.org/10.3390/rs14030601