TanDEM-X digital elevation model (DEM) is a global DEM released by the German Aerospace Center (DLR) at outstanding resolution of 12 m. However, the procedure for its creation involves the combination of several DEMs from acquisitions spread between 2011 and 2014, which casts doubt on its value for precise glaciological change detection studies. In this work we present TanDEM-X DEM as a high-quality product ready for use in glaciological studies. We compare it to Aerial Laser Scanning (ALS)-based dataset from April 2013 (1 m), used as the ground-truth reference, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) V003 DEM and SRTM v3 DEM (both 30 m), serving as representations of past glacier states. We use a method of sub-pixel coregistration of DEMs by Nuth and Kääb (2011) to determine the geometric accuracy of the products. In addition, we propose a slope-aspect heatmap-based workflow to remove the errors resulting from radar shadowing over steep terrain. Elevation difference maps obtained by subtraction of DEMs are analyzed to obtain accuracy assessments and glacier mass balance reconstructions. The vertical accuracy (± standard deviation) of TanDEM-X DEM over non-glacierized area is very good at 0.02 ± 3.48 m. Nevertheless, steep areas introduce large errors and their filtering is required for reliable results. The 30 m version of TanDEM-X DEM performs worse than the finer product, but its accuracy, −0.08 ± 7.57 m, is better than that of SRTM and ASTER. The ASTER DEM contains errors, possibly resulting from imperfect DEM creation from stereopairs over uniform ice surface. Universidad Glacier has been losing mass at a rate of −0.44 ± 0.08 m of water equivalent per year between 2000 and 2013. This value is in general agreement with previously reported mass balance estimated with the glaciological method for 2012–2014.

In the field of iceberg and glacier calving studies, it is important to collect comprehensive datasets of populations of icebergs. Particularly, calving of lake-terminating glaciers has been understudied. The aim of this work is to present an object-based method of iceberg detection and to create an inventory of icebergs located in a proglacial lagoon of San Quintín glacier, Northern Patagonia Icefield, Chile. This dataset is created using high-resolution WorldView-2 imagery and a derived DEM. We use it to briefly discuss the iceberg size distribution and area–volume scaling. Segmentation of the multispectral imagery produced a map of objects, which were classified with use of Random Forest supervised classification algorithm. An intermediate classification product was corrected with a ruleset exploiting contextual information and a watershed algorithm that was used to divide multiple touching icebergs into separate objects. Common theoretical heavy-tail statistical distributions were tested as descriptors of iceberg area and volume distributions. Power law models were proposed for the area–volume relationship. The proposed method performed well over the open lake detecting correctly icebergs in all size bands except 5–15 m² where many icebergs were missed. A section of the lagoon with ice melange was not reliably mapped due to uniformity of the area in the imagery and DEM. The precision of the DEM limited the scaling effort to icebergs taller than 1.7 m and larger than 99 m², despite the inventory containing icebergs as small as 4 m². The work demonstrates viability of object-based analysis for lacustrine iceberg detection and shows that the statistical properties of iceberg population at San Quintín glacier match those of populations produced by tidewater glaciers.

Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of 26 scientific institutions from 9 countries studying the environment and climate in and around Svalbard. The key aims of SIOS are: (1) to develop an efficient observing system, (2) to share technology, experience, and data, (3) to close knowledge gaps, and (4) to decrease the environmental footprint of science. SIOS encourages the usage of airborne remote sensing platforms for research activities in Svalbard to complement in situ measurements and reduce the environmental footprint of research. SIOS member institution Norwegian Research Centre (NORCE) has installed and tested a suite of optical imaging sensors on the Lufttransport Dornier aircraft stationed in Longyearbyen as part of the SIOS-InfraNor project. Two optical sensors are installed onboard the Dornier aircraft (1) the PhaseOne IXU-180 RGB camera and (2) the HySpex VNIR-1800 hyperspectral sensor. The aircraft with these cameras is configured to acquire aerial RGB imagery and hyperspectral remote sensing data in addition to its regular transport operation in Svalbard. To date, SIOS has supported around 50 hours of flight time to acquire airborne data using Dornier aircraft in Svalbard for more than 20 scientific projects. Airborne imaging sensors include a variety of applications within glaciology, biology, hydrology, and other fields of Earth system science to understand the state of the environment of Svalbard. Mapping glacier crevasses, generating DEMs for glaciological applications, mapping and characterising earth (e.g., minerals, vegetation), ice (e.g., sea ice, icebergs, glaciers and snow cover) and ocean surface features (e.g. colour, chlorophyll) are examples of implementation. Aerial photos are also useful for monitoring the seasonal changes in snow, sea ice cover, and ocean colour. In 2021, SIOS conducted capacity building activities to train the next generation of polar scientists to use airborne imaging sensor data for their projects as part of the SIOS hyperspectral remote sensing training course (HSRS). This study presents a few selected applications from this course to demonstrate the potential of airborne imaging sensors in Svalbard. These include mapping water bodies (e.g. fjords, rivers), estimation of snow grain size, land cover classification, deriving chlorophyll, and mapping terrestrial vegetation. Preliminary results from these studies will be used to develop operational scientific applications and complement measurements from in-situ observations acquired by SIOS infrastructure in Svalbard. Eventually, these datasets will be valuable resources for calibration and validation activities for upcoming satellite hyperspectral missions, for example, the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).

In recent years, Chile has experienced an extraordinary drought that has had significant impacts on both the livelihoods of people and the environment, including the Andean glaciers. This study focuses on analyzing the surface processes of Universidad Glacier, a benchmark glacier for the Dry Andes. Multiple remote sensing datasets are used alongside a novel spectral index designed for mapping of rock material located on the glacier's surface. Our findings highlight the precarious state of the glacier, which serves as a crucial water source for the region. The glacier exhibits locally varied debris accumulation and margin retreat. The most significant impacts are observed on the tongue and secondary accumulation cirques, with the latter at risk of disappearing. Debris cover on the tongue expands, reaching higher elevations, and is accompanied by glacier retreat, especially at higher altitudes. The equilibrium line is rapidly shifting upglacier, although mid-season snow cover tends to reach the 2013 equilibrium line even by 2020. Changes in stream density on the glacier's tongue indicate an increased water supply in this area, likely due to enhanced melting of glacial ice. These observed processes align well with meteorological data obtained from models. The behavior of dust and debris is influenced by precipitation amount, while the rate of retreat is linked to air temperature.