Surface melt is a critical boundary condition for the hydrological system of the Antarctic Ice Sheet, impacting processes such as mass balance and ice shelf stability. However, its quantification remains challenging due to the scarcity of in-situ measurements and the spatial variability of melt processes. While remote sensing offers extensive coverage, estimating melt rates - beyond binary melt detection - is complicated by the nature of satellite measurements, which detect the presence of liquid water rather than the physical process of melting.
This study explores a novel method for estimating surface melt rates across Antarctica by calibrating passive microwave data from the Special Sensor Microwave Imager/Sounder (SSMIS) with in-situ observations of surface melt collected by Automatic Weather Stations (AWS).
Binary melt days were identified using SSMIS 19GHz brightness temperature thresholds carefully calibrated against AWS data from diverse Antarctic regions, including the Larsen C, Baudouin, and Ekström ice shelves. A quantitative relationship was established to link the number of melt days to the melt quantities measured at AWS sites, offering a first approximation of annual melt rates. The methodology emphasizes spatial coherence and compatibility across datasets and offers insights into regional variations in melt processes.
We suggest that this approach has the potential to improve the detection of melt days and provide estimates of melt rates from space rather than merely identifying melt occurrence. The study underscores the significance of AWS calibration, while also acknowledging the uncertainties in both the data and the methodology. This framework represents a step forward in understanding melt dynamics in Antarctica and contributes to the development of tools for long-term operational monitoring of surface melt, as well as offering an independent estimate of surface melt over the past 45 years since the onset of the satellite era.
How to cite: Di Biase, V., Kuipers Munneke, P., Wouters, B., van den Broeke, M., and van Tiggelen, M.: Estimating Antarctic surface melt rates using passive microwave data calibrated with weather station observations., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8060, https://doi.org/10.5194/egusphere-egu25-8060, 2025.
Fractures in Greenland’s ice slabs provide englacial drainage pathways that initially reduce surface runoff but may eventually form surface-to-bed connections that cause high-elevation hydrodynamic coupling. As a result, characterizing the large-scale spatial patterns of surface fracture in ice slabs is important for assessing the current and future mass balance of the Greenland Ice Sheet. Unfortunately, these crevasses are mostly too narrow to be directly observed with remote sensing systems that provide consistent pan-Greenland coverage. Here, we integrate the respective strengths of remote sensing and fracture mechanics with statistical methods to overcome this challenge. We use ice sheet surface velocities derived from satellite remote sensing to calculate the stresses at the ice sheet surface. We then use sparse but high-fidelity observations of ice slab fractures from WorldView imagery to train a logistic regression model to predict fracture likelihood based the von Mises stress. We use a model ensemble approach to both optimize our calculation of the surface stresses and to account for uncertainty in the stress state due to velocity measurement error and ice viscosity uncertainty. Our regionally cross-validated model achieves an F1 score of 0.83+/-0.05, demonstrating that the von Mises stress can robustly predict spatial patterns of crevassing in Greenland's ice slabs. Across the Greenland Ice Sheet, we predict that 41% of the ice slab area is fractured, with most crevasses fields forming in marine-terminating sectors. This result suggests that (1) englacial storage is likely a significant component of mass balance in many ice slab regions and (2) that the interplay between melt-modulated basal sliding and oceanic terminus forcing may be key to the future evolution of Greenland's marine-terminating outlet glaciers as englacial drainage expands to higher elevations.
How to cite: Culberg, R., Lai, C.-Y., and Mackie, E.: Von Mises Stress a Robust Predictor of Ice Slab Fracture in Greenland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6996, https://doi.org/10.5194/egusphere-egu25-6996, 2025.
Sermeq Kujalleq in Kangia (hereafter SKK, also known as Jakobshavn Isbræ), Greenland, is among the most extensively studied glaciers worldwide, mainly due to its recent retreat associated with fast flow and high ice discharge. However, substantial gaps remain in understanding its short-term ice dynamics, as glacier responses to abrupt changes occurring within hours to minutes require high-rate field measurements that are challenging to acquire. Here, we present high-resolution terrestrial radar observations revealing a stepwise acceleration of SKK ice stream immediately following a large calving event. This response was observed up to 10 km from the glacier terminus, representing one of the longest immediate calving responses reported in Greenland to date. Additionally, we detected large instantaneous deformations in the highly crevassed shear margins, further supporting the notion of a strong lateral and longitudinal coupling of the ice stream. Using a simplified theoretical framework, we present the loss of lateral drag due to calving as a key component in the genesis of such a widespread calving response.
How to cite: Wehrlé, A., Rousseau, H., Lüthi, M. P., Nap, A., Kneib-Walter, A., van Ginkel, J., Jouvet, G., and Walter, F.: Observing an immediate ice stream response to calving with terrestrial radar interferometry at Sermeq Kujalleq in Kangia, Greenland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11780, https://doi.org/10.5194/egusphere-egu25-11780, 2025.
Massive processing using correlation algorithms on optical and SAR image pairs are now largely used to measure glacier surface velocity worldwide. This variable is crucial as it controls glacier mass redistribution and geometry changes. Post-processed products of these raw image-pair velocities are available at an annual scale in open-access. However, at shorter time scales, velocity time-series are still highly uncertain and available at heterogeneous temporal resolutions. This hinders our ability to understand physical processes related to glacier dynamics, such as basal sliding or surges, and the integration of these observations in numerical models. Therefore, post-processing pipelines are needed to extract sub-annual velocity time-series from the large-scale datasets available in open-access or on demand.
Here, we introduce an open source and operational Python package called TICOI (Temporal Inversion using Combination of Observations and Interpolation). TICOI is an out-of-core algorithm. It accesses cloud datasets without fully loading them into local memory, and parallelize the processing by chunks, using the dask library. TICOI fuses multi-temporal and multi-sensor image-pair velocities produced by different processing chains, using the temporal closure principle. Several strategies are implemented to improve TICOI robustness to Gaussian noise, temporal decorrelation, and abrupt non-linear changes. Here, we provide extensive examples of TICOI application on the ITS\_LIVE cloud dataset and in-house velocity products. We discuss the performance of our pipeline using GNSS data collected on three glaciers with different dynamics in Yukon and western Greenland. We show that TICOI is able to retrieve monthly velocities even when only annual image-pair velocity observations are available, implying a paradigm shift. Finally, we illustrate the spatio-temporal variations of velocity retrieved by TICOI in several montain range: the Mont Blanc Massif in the Alps, the Qilian Mountains in High Mountain Asia, and the St Elias Mountains in Yukon, Canada.
This package opens the door to the regularization of various datasets, enabling the production of standardized sub-annual velocity time-series.
How to cite: Charrier, L., Dehecq, A., Guo, L., Brun, F., Millan, R., Lioret, N., Rabatel, A., Copland, L., Maier, N., Dow, C., and Halas, P.: Monthly variations of glacier velocity extracted from large scale datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12187, https://doi.org/10.5194/egusphere-egu25-12187, 2025.
Accurate estimation of glacier elevation change is crucial for long-term geodetic mass balance and assessing glacier response to climate change. This study introduces an automated pipeline for generating Digital Elevation Models (DEMs) from satellite stereo imagery to quantify glacier elevation changes.
We focus on SPOT-5 High-Resolution Stereoscopic (HRS) imagery, recently freely accessible through the SPOT World Heritage program by CNES. SPOT-5 is an underutilized archive with global coverage from 2002 to 2015 and stereoscopic capabilities, making it valuable for reconstructing glacier elevation changes and complementing stereo imageries from more recent satellites. However, its inherent challenges, such as limited radiometric resolution, rectangular pixel geometry, and absence of camera's Rational Polynomial Coefficient model, require specific attention. We apply our workflow to Hofsjökull, Iceland’s third-largest ice cap, because of extensive SPOT-5 temporal coverage, further complemented by ArcticDEM, SPOT-6 and Pleiades for recent years.
Our workflow addresses key steps in DEM generation such as stereo pair selection, bundle adjustment, stereo correlation, noise filtering, point cloud gridding, void filling, and co-registration. Each of these steps significantly affects DEM quality and glacier elevation change estimates. Therefore, we compare and evaluate various approaches to identify optimal solutions for automation. We benchmark open-source photogrammetry tools, including Ames Stereo Pipeline and MicMac, and geospatial libraries like xDEM, GeoUtils, and OPALS, integrating them for interoperability.
We tested different stereo-matching algorithms and found that the More Global Matching algorithm performs best for SPOT-5 data under diverse illumination and viewing conditions. For DEM gridding and void filling, we use a Robust Moving Planes fitting method in OPALS. Co-registration is performed using the globally available Copernicus DEM (GLO-30) as reference, using appropriate masks to exclude glaciers, forests, water bodies and steep areas. The least-squares template matching algorithm implemented in OPALS enhances alignment accuracy by estimating full affine transformations, while sub-pixel refinement is achieved with the Nuth and Kääb method. Finally, we derive elevation-band-based trends from spaceborne DEM time series to extrapolate elevation changes over decadal intervals. This enables us to calculate area-weighted mean elevation change estimates for each glacier and the entire ice cap over defined periods.
This study contributes to the Glacier Mass Balance Intercomparison Exercise (GlaMBIE) by advancing scalable, open-access methodologies for glacier elevation change assessments. Additionally, our systematic comparison and integration of algorithms and techniques for each stage ensures optimized performance, making the pipeline reproducible across regions, temporal scales, and satellite platforms.
How to cite: Ioli, F., Webster, C., Kugler, L., and Piermattei, L.: Automated pipeline for DEM generation from SPOT-5 stereo imagery for glacier elevation change assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15978, https://doi.org/10.5194/egusphere-egu25-15978, 2025.
Glaciers are dynamic systems of snow and ice that accumulate and melt, producing a wide range of sounds associated with morphodynamic and hydrological processes, such as their deformation and movement, melting and refreezing cycles, and ice collapse. The USIE (Un Suono In Estinzione) project leverages sound analysis to investigate Alpine glacier evolution and to raise awareness of climate change impacts. Through a multidisciplinary approach, the project aims to advance scientific research in alpine glacier hydrology while creating artistic performances and installations, both relying on the same dataset of recorded sounds. Such approach can be classified as STEAM, as it integrates Scientific research, Technology, Engineering, Art, and Mathematics, incorporating into the classical STEM aspect, typical of classical hydrological and cryospheric sciences, the perceptual and emotional dimensions typical of sound arts. Between 2021 and 2023, over 14’000 hours of acoustic data were collected on the Adamello Glacier using five bioacoustic recorders, suitable for long-term outdoor deployment, placed in strategic locations, such as crevasses and meltwater streams. For the same years, a spatially distributed energy and mass balance model (the PDSLIM model) has been used to compute surface melting and runoff at the sound recorders locations. In order to capture the daily variability of surface melting, the model requires hourly temporal resolution meteorological input data. Here, we show how the acoustic monitoring can be used for the validation of the PDSLIM surface melting model. We show how the sound pressure level daily variability reveals insights about timing of snow and ice melting cycles and the hydrological response of the glacier, highlighting seasonal and daily patterns. Through this innovative approach, we investigate the potential of acoustics as a complementary tool for advancing cryospheric and hydrological science while emotionally communicating the critical conditions of alpine glaciers.
How to cite: Ranzi, R., Astolfi, A., Baroni, C., Carton, A., Casarotto, C., Colosio, P., Degani, A., Ferrari, C., Lendvai, A., Smiraglia, C., Tedesco, M., and Maggioni, S.: Validating energy and mass balance model simulations of the Adamello glacier using sound records from the artistic-scientific project Un Suono In Estinzione (USIE), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19073, https://doi.org/10.5194/egusphere-egu25-19073, 2025.
Glacier lakes have been expanding globally in quantity and size due to accelerated glacier melt. In Norway, a growth in lakes has been seen in recent decades. The expansion and formation of glacier lakes pose significant risks, including the increased frequency of Glacier Lake Outburst Floods (GLOFs), which can impact downstream communities. Given the difficulty in accessing mountainous regions, the application of remote sensing is fundamental to monitoring glacier lakes for understanding the impacts of climate change and assessing the risks associated with GLOFs. However, there is no universally accepted definition of a glacier lake, which complicates regional comparisons. Standard mapping techniques for glacier lakes, such as thresholding the Normalized Difference Water Index (NDWI) from remote sensing data, face challenges due to cloud cover, terrain shadows, and ice cover variability. This has led to the use of manual or semi-automatic methods, often requiring labour-intensive post-processing to improve accuracy. Recent advancements in machine learning offer promising alternatives, enabling more efficient and accurate mapping by integrating multiple input data sources. However, existing methods still rely on digital elevation models (DEMs), which may not accurately reflect recent glacier retreat and the formation of new lakes. This study aims to address these limitations by developing an automated, reproducible workflow to update the glacier lake inventory of Norway using Sentinel-1 and Sentinel-2 imagery from 2023/24. We employ a random forest classifier trained on a 10th percentile Sentinel-2 summer composite without relying on DEMs. To mitigate misclassification, particularly due to mountain shadows, we propose a novel post-processing step that uses differences between ascending and descending Sentinel-1 images. Our fully automated workflow, implemented in Google Earth Engine and Python, is expected to improve the efficiency and reproducibility of glacier lake mapping. A comparison of the results with Norway's most recent glacier lake inventory from 2018/19 shows further glacier retreat with associated lake expansion and formation of new lakes. The method performs best in flat and low-lying glacier environments, whereas some manual editing is still needed in steep, high-alpine regions due to shadowing and year-round lake ice cover.
How to cite: Lappe, R. and Andreassen, L. M.: Updating Norway’s glacial lake inventory - an automated workflow using Sentinel-1 & 2 data and machine learning , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17460, https://doi.org/10.5194/egusphere-egu25-17460, 2025.
Rock glaciers are critical landforms in periglacial environments. They play a significant role in regional hydrology and provide valuable insights into climate and geomorphological processes. Mapping rock glacier extent is an important step for quantifying their hydrologic and geomorphic role in the landscape, but this process is labor intensive. To automate the process of mapping rock glaciers in the western U.S. (total area ~ 30000 km2), we present a methodological framework that relies on a combination of Google Earth Engine and TensorFlow cloud computing. Using existing rock glacier inventories, we trained a Compact Residual U-Net Convolutional Neural Network (CNN) that uses 14 input bands, including Sentinel-2 optical imagery, USGS elevation models, Sentinel-1 backscatter SAR imagery, and Landsat 8 thermal imagery. The model was trained across 5 US states (Utah, Colorado, Wyoming, Idaho, Montana) which have different rock types and climates. With 2597 rock glacier outlines from the Portland State University Active Rock Glacier Inventory used for training, the model achieved a moderate Intersection over Union (IoU) of 0.495 when tested on a new dataset. Precision and recall values were 0.735 and 0.602, respectively. The model successfully mapped 206 out of 290 rock glaciers as well as 41 false positives and 84 false negatives in the eastern Uinta Mountains across an area of 3037 km2. The model struggled to map slower-moving rock glaciers, which are more geomorphologically subtle. Our research advances the application of machine learning in rock glacier mapping, offering a high-dimensional method for mapping rock glaciers, which will ultimately enhance our understanding of these important landforms in a changing climate.
How to cite: Takoudes, A. C., Handwerger, A. L., and Munroe, J. S.: Automated Mapping of Rock Glaciers Using Image Segmentation with Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12804, https://doi.org/10.5194/egusphere-egu25-12804, 2025.
The rapid advancement and commercialization of low-cost (0.5k to 20k EUR) lidar have transformed how researchers monitor and analyze dynamic environmental processes. Initially developed for industries like automotive navigation and robotics, these compact and cost-effective sensors have gained significant traction in geoscience. Many of these sensors operate in the wavelength range of 900 to 1000 nm, where ice is highly reflective, making them particularly suited for cryospheric observations. In addition to their affordability, these systems are robust, capable of high scan rates of up to 20 Hz, and have a range of up to 450 meters. The high scan rates enable the collection of detailed datasets but can result in substantial data volumes that require efficient processing. Many sensors also include integrated IMUs, adding another layer of functionality.
This work focuses on the static use of lidar systems, where they are mounted on fixed structures for continuous or periodic monitoring. Low-cost lidars are typically sold without essential components such as power supplies, data loggers, or data transmission capabilities. Additionally, they often output data in proprietary formats, making data analytics and processing cumbersome. Furthermore, the comparable low range makes it often necessary to use multiple sensors which increases complexity even more. These barriers make their deployment challenging for many researchers or research groups that lack the resources or expertise to build custom solutions.
To address this gap, we have developed a comprehensive system combining hardware, software, and analytical tools to lower the barrier to entry. Our data logger is built on the Robot Operating System (ROS 2), enabling seamless integration of multiple sensors, even from different manufacturers, if they provide a ROS 2 driver. Users can configure scanning intervals and durations to suit their needs, such as a 5-second scan every 15 minutes combined with continuous monitoring. The collected scan data is uploaded to our server via a REST API, where further processing is automated. Our REST API handles tasks such as quality checks, conversion to standard point cloud formats like LAS or CSV, point cloud differencing, and volume calculations. Furthermore, our system integrates seamlessly with pointcloudset, our open-source Python package designed for advanced 4D point cloud analytics. This package enables detailed analysis of extensive point cloud datasets recorded over time.
We present the current version of our API, available at api.avalanchemonitoring.com/schema/swagger, alongside the first deployment of a system with two lidar from Livox in December 2024 in Lech am Arlberg, Austria, at an elevation of 2270 m asl. Preliminary insights from the collected data highlight the potential of our system to enable widespread use of (semi-)permanently installed lidars in cryospheric research and beyond. By providing an accessible and integrated solution, we aim to empower researchers to leverage the full capabilities of low-cost lidar systems without the burden of technical challenges.
How to cite: Goelles, T., Wallner, S., Schlager, B., Prokop, A., Gaisberger, C., Schratter, M., and Muckenhuber, S.: Low-Cost lidar as an Easy-to-Use REST API: Permanently Installed Systems for Cryospheric Research and Beyond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14949, https://doi.org/10.5194/egusphere-egu25-14949, 2025.
In our previous articles (Hu et al., 2022; Lu et al., 2022, Hu et al., 2023), we introduced a theoretical snow depth and snow density measurements concept using lidar measurements. The key findings of these studies are: snow depth and snow density play key roles in the probability distribution of diffused photon scattering inside snow. When absorption can be ignored, the averaged photon pathlength of laser light or sunlight traveling inside snow is proportional to snow depth. Snow density are also affect spectral absorptions and the higher order statistics of the diffused photon pathlength distribution.
Spectral reflectance of sunlight R(k) is the Laplace transform of the diffuse photon pathlength distribution, P(L). R(k)=∫ p(L) e-kL dL. Here k is the absorption coefficient of snow at given wavelength. Thus there are information of snow depth and snow density in the spectral measurements of sunlight, of which k may change between 0.02 per meter to 100 per meter. For example, Snow depth is proportional to the first moment of the pathlength distribution, , which is simply, -R' (k)=-dR⁄dk=∫ L p(L) e-kL dL. Thus, snow depth is proportional to the first order derivative of the spectral reflectance.
Thus, snow depth and snow density can be derived from spectral reflectance of sunlight through inverse transform. Using machine learning that uses lidar measurements of snow depth and snow density to train the collocated spectral solar reflectance measurements, we can effectively perform atmospheric correction and -R’(k) at the same time This short paper describes the theory behind the measurements. We will also demonstrate the measurement concept with collocated PACE and ICESat-2 observations.
How to cite: Hu, Y., Lu, X., Sun-mack, S., Chen, Y., and Di Girolamo, P.: A Roadmap to Global High Spatial/Temporal Resolution Snow Depth Survey Through Synergistic Space Lidar and Optical Spectral Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4549, https://doi.org/10.5194/egusphere-egu25-4549, 2025.
Hyperspectral imaging is a valuable analytical technique with significant benefits for environmental monitoring. However, the application of these technologies remains limited, largely by the cost and bulk associated with available instrumentation. This results in a lack of high-resolution data from more challenging and extreme environmental settings, limiting our knowledge and understanding of the effects of climate change in these regions. Here we challenge these limitations through the application of a low-cost, smartphone-based hyperspectral imaging instrument to measurement and monitoring activities at the Greenland Ice Sheet. Datasets are captured across a variety of supraglacial and proglacial locations covering visible and near infrared wavelengths. Our results are comparable to the existing literature, despite being captured with instrumentation costing over an order of magnitude less than currently available commercial technologies. Practicalities for field deployment are also explored, demonstrating our approach to be a valuable addition to the research field with the potential to improve the availability of datasets from across the cryosphere, unlocking a wealth of data collection opportunities that were hitherto infeasible.
How to cite: Stuart, M., Davies, M., Fisk, C., Allen, E., Sole, A., Ing, R., Hobbs, M., and Willmott, J.: Low-cost smartphone hyperspectral imaging for environmental monitoring in the cryosphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3215, https://doi.org/10.5194/egusphere-egu25-3215, 2025.
Surface snowmelt on the Antarctic Ice Sheet (AIS) plays a crucial role in Earth’s climate system, influencing surface hydrology, ice shelf stability, surface mass and energy balance. Liquid water emerging in surface snowpacks during melt seasons increases the emissivity of microwaves, enabling the detection of snowmelt information by identifying the sudden increase in passive microwave brightness temperature (Tb). The detecting of polar ice sheet surface snowmelt state at superior timeliness and finer scale is rapidly progressing through the augmenting real-time capability and resolution of passive microwave radiometry. However, existing algorithms often rely on complete melt seasons of observation Tb data, which limits their applicability for real-time detection and typically suffer from low spatial resolution. Here, we develop a real-time detection algorithm and a corresponding system for surface snowmelt detection on the Antarctic Ice Sheet, utilizing Tb data at multiple spatial resolutions.
The three remotely sensed variables we used include diurnal amplitude variation at 37 GHz vertical polarization (i.e., DAV37), the difference between the Tb at 37 GHz vertical polarization and the winter reference (i.e., ΔTB37V), and the normalized polarization ratio at 37 GHz (NPR37). In-situ observations from the AIS automatic weather stations (AWSs) are provided by the Institute for Marine and Atmospheric research of Utrecht (IMAU). The energy available for surface snowmelt was calculated using the surface energy balance (SEB) model, which has demonstrated reliability and robustness in providing consistent snowmelt flux estimates. In this study, we primarily used three remotely sensed variables and in-situ snowmelt flux as inputs for the parameterization of the Fisher Discriminant Analysis (FDA) equation Di=ω0+ω1xi,1+ω2xi,2. In this equation, D represents the discriminant score, and if D is above 0°C, a specific pixel is classified as a melting state, whereas if D is below 0°C, the pixel is classified as frozen. The validation of snowmelt results was conducted using snowmelt flux data from AWSs, yielding a promising overall accuracy of 96% and an F1-score of 0.74.
The algorithm is suitable for real-time snowmelt detection, and the corresponding detection system enables high timeliness (within 24 hours) in acquiring surface snowmelt state, melting area and melting days on the Antarctic Ice Sheet. The FY-3 MWRI provides real-time Tb data stably, whereas it is limited by its low spatial resolution (25 km). The missing time series satellite observations from the SSMIS sensor are substantial, leading to random errors. However, the enhanced-resolution SSMIS dataset can provide higher spatial resolution Tb measurements (3.125 km). Here we perform the linear regression and time-line interpolation method to establish a relationship between Tb data from various spatial resolutions, and further combine measurements from the FY-3 MWRI and SSMIS sensors to enhance the spatial resolution of the system.
How to cite: Zhang, Z., Zheng, L., Cheng, X., and Jiang, L.: Real-time Detection of Daily Surface Snowmelt Based on FY-3 MWRI and Enhanced-resolution SSMIS Data Over the Antarctic Ice Sheet, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8150, https://doi.org/10.5194/egusphere-egu25-8150, 2025.
The Antarctic Digital Database (ADD) is a comprehensive compilation of the best available topographic data for Antarctica, serving as an essential tool for researchers navigating and understanding the continent’s ever-changing landscape. Key layers, such as the Antarctic coastline, are regularly updated through visual control and manual editing. In contrast, other data layers, like Rock Outcrop, are updated only upon request, typically during the creation of maps for specific regions.
Antarctic topographic mapping primarily relies on remote sensing data rather than ground surveys, which is unlike most populated areas of the world. Monitoring changes in polar regions is crucial for understanding global climate change. Therefore, the increasing use of pre-trained foundation models based on remote sensing data is expected to be beneficial for Antarctic mapping.
This study aimed to utilize models pre-trained on large satellite datasets to update the Rock Outcrop layers using a small amount of training data. The current Rock Outcrop layer, generated in 2016 based on Landsat 8 imagery, has become outdated.
For this work, a Sentinel-2 mosaic of averaged, mostly cloud-free images from the 2023/2024 Antarctic summer season was generated using Google Earth Engine for the British Antarctic Territory (BAT). The mosaic was exported as a tiled raster image, consisting of 4,990 chips. Rock outcrop labels were created for 20 tiles, evenly spread across the BAT.
The ResNet18 model, pre-trained on the SSL4EO-S12 dataset with Sentinel-2 RGB MOCO weights, was trained, resulting in an F1 score of 0.91. To achieve proper cartographic representation, the generated predictions underwent a generalization process. The results will be published as part of the next ADD update.
The updated layer shows a 28% increase in the area of exposed rock compared to the 2016 layer. This significant change could be attributed to both the data and methods used, as well as actual changes in snow coverage in the study area. Further updates, which now require minimal effort to implement, may help explain the observed dynamics.
How to cite: Skachkova, A.: Improving Antarctic Topography: Utilizing Pre-Trained Models for Rock Outcrop Updates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9539, https://doi.org/10.5194/egusphere-egu25-9539, 2025.
Understanding glacier dynamics is fundamental to predicting their response to a changing climate and mitigating associated risks. Glaciers, which hold approximately 70% of the world's freshwater, are losing mass at an unprecedented rate due to anthropogenic activities. Accurate predictions of glacier behavior under warming scenarios are crucial for short-term hazard mitigation and long-term water resource planning.
The project "AI4Glaciers: AI-Enabled Prediction of Glacial Calving based on 4D Real-Time Multi-Sensor Monitoring (AI4G)" aims to monitor, in near real-time, a section of the Perito Moreno Glacier's front using a multi-sensor photogrammetric system (RGB and thermal cameras) in 4D (3D + time). By correlating calving events with climatic conditions using artificial intelligence, the project seeks to understand the drivers of accelerated glacial calving processes and, consequently, glacier retreat.
In this contribution, we present the camera setup installed in January 2025 at the Perito Moreno Glacier as part of the AI4G project. The system comprises eight synchronized DSLR cameras capturing image pairs every 30 minutes during daylight hours. These images are transmitted twice daily to a central server via 4G connectivity, enabling near real-time analysis. Additionally, three thermal cameras (600x400 pixels), capturing data continuously 24 hours a day, were installed to generate photogrammetric reconstructions using temperature data.
With approximately 15,000 images collected monthly, photogrammetric models are generated using Structure-from-Motion Multi-View Stereo (SfM-MVS) techniques. These models are compared using change-detection algorithms to identify relative changes at the glacier front, including ice loss due to calving and pre-calving deformations.
How to cite: Blanch Gorriz, X., Duran Vergara, L. C., and Eltner, A.: AI4Glaciers: Introducing a multi-sensor photogrammetric system for calving monitoring at the Perito Moreno glacier, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9870, https://doi.org/10.5194/egusphere-egu25-9870, 2025.
Seasonal snow is the largest single component of the cryosphere, covering about 50% of the Northern Hemisphere’s land surface during mid-winter. Snow plays an important component of Earth’s hydrological and climate systems and provides a significant feedback effect in a warming climate owing to its high albedo. Snow also a major, if not dominant, freshwater source in many regions and an important contribution to the global water cycle.
Within Copernicus Climate Change Service (C3S) Finnish Meteorological Institute will offer consistent long-term observations of snow from satellite. C3S provides reliable, open, and free access to a wide variety of Climate Data Records (CDRs) consistently derived from satellite observations that can be used to monitor climate change.
Snow water equivalent (SWE) is an important variable indicating the amount of accumulated snow on land surfaces. FMI’s SWE processor combines satellite-based passive microwave radiometer data with ground based synoptic snow depth observations using Bayesian data assimilation, incorporating a microwave snow emission model. Building on developments in ESA GlobSnow and Snow CCI projects, the new Copernicus services facilitate the transition from research to operations by ensuring reliable access to the climate data records and all information needed to use them effectively.
FMI’s contribution to C3S services focus on the operational production and provision of consistent and long-term data records on Essential Climate Variable (ECV) of snow water equivalent based on observation dating back to 1979. The new C3S SWE dataset is extended in time and will offer a fully documented, quality-controlled, free and open product, easily accessible via the Copernicus Climate Data Store (CDS).
We are presenting the activities of C3S cryosphere and hydrology and how the new remote-sensing products of Snow Water Equivalent provide operational snow information to these domains.
How to cite: Kosmale, M., Luojus, K., Moisander, M., Venäläinen, P., Takala, M., and Ikonen, J.: Long-term satellite remote sensing SWE observations for new operational C3S cryosphere and hydrology services, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13003, https://doi.org/10.5194/egusphere-egu25-13003, 2025.
The formation and expansion of glacial lakes, driven by ongoing climate warming, is a potentially hazardous phenomenon with significant impacts on glacier melt and regional hydrology. In regions like Central Asia, decades of glacier retreat and thinning have fostered the development and growth of various types of glacial lakes, including ice-marginal, moraine-dammed, and supraglacial lakes. These water bodies store meltwater and accelerate glacier mass loss and terminus retreat through heat exchange and intensified melt, ice calving, and ice-marginal destabilization. Once formed, glacial lakes can trigger positive feedback mechanisms that decouple their evolution from direct climate forcing, resulting in rapid glacier downwasting and retreat.
Despite advances in remote sensing, accurately capturing the full size and spatial distribution of these lakes remains challenging. Current inventories, largely based on moderate-resolution imagery (Landsat, Sentinel-2), often overlook smaller lakes. These smaller lakes are a critical yet underappreciated component of the cryosphere and can expand rapidly, posing risks of Glacier Lake Outburst Floods (GLOFs).
In this study, we present a comprehensive, high-resolution glacial lake inventory for Central Asia, derived from Planet’s PlanetScope imagery. With a spatial resolution of approximately 3 m/pixel—an order of magnitude finer than Landsat and Sentinel-2 —PlanetScope data enables the delineation of lakes as small as tens of meters in size, overcoming the spatial limitations of previous satellite-based inventories. The study focuses on Central Asia, covering regions including Dzhungarsky Alatau, Tien Shan, Pamir-Alay, and Pamir.
Our approach consists of four main steps: (i) Water Pixel Identification: Water pixels are detected from PlanetScope images using the Normalized Difference Water Index (NDWI) and Coloured Dissolved Organic Matter (CDOM), with thresholds determined by Otsu’s algorithm. (ii) Lake Clustering: Detected water pixels are grouped into clusters, with glacial lakes defined as clusters exceeding 22 connected pixels (~200 m²). (iii) Boundary Refinement: Lake boundaries are further refined using an NDWI- and CDOM--based Otsu method. (iv) Lake Inventory Compilation: A detailed inventory is produced, including geographical coordinates, elevation, area, and NDWI statistics for each identified lake. The algorithm is implemented within the Google Earth Engine (GEE) environment, enhancing computational efficiency and minimizing the need for local data storage.
The new Central Asia Glacial Lake Inventory (CAGLI) comprises approximately 14,000 water bodies, each with an area exceeding 200 m² with a combined area of 470 km² as of August-September 2023. This dataset allows for a detailed characterization of lake size distribution in the region, efficient monitoring of lake formation and growth, and analysis of the impact of surface ponds on glacier evolution. It also enhances the delineation of debris-covered glaciers, where water pond formation is more likely than on non-glacierized terrain. Importantly, the new algorithm provides practitioners in Central Asia and other glacierized mountain regions with a highly efficient tool for monitoring lake changes, supporting early warning systems and risk reduction strategies.
How to cite: Li, Q., Shahgedanova, M., Roy, S., and Jiang, Y.: High-Resolution Mapping of Glacial Lakes in Central Asia Using PlanetScope Imagery and Google Earth Engine: A New Algorithm and Comprehensive Inventory, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13738, https://doi.org/10.5194/egusphere-egu25-13738, 2025.
Recently formed crevasses can now be observed more than 100 km upstream from the outlet of Sermeq Kujalleq (Jakobshavn Isbræ, JI), challenging previous assumptions that the ice sheet is crevasse-free at 2000 m elevation. The recent appearance of these open surface crevasses is a strong indicator of change migrating inwards on the ice sheet. Investigating the formation and evolution of these large transverse crevasses is important, as their presence may signal shifting firn mechanics which may amplify the impacts of climate change on ice sheet stability.
We are working on a case study of one particular crevasse field located at site T131, which is ~130 km upstream of the JI grounding zone. Open surface crevasses appeared around 2001 in optical satellite imagery (Landsat-7), but radar imagery (ERS-1) confirm the prior existence under snow and firn cover as early as 1991. We have lowered a tethered LiDAR robot into an open crevasse to map its geometry beyond the line of sight accessible to humans. Direct LiDAR measurements indicate a crevasse depth exceeding 37.6 m, with width-based extrapolations placing the full depth between 44 and 58 m. A firn and ice density profile from nearby Crawford Point (50 km away) reveals the firn-ice transition at ~124 m depth, confirming that these crevasses are forming within the firn.
Offsets between airborne radar and laser altimetry suggest the presence of ice slabs formed by refreezing meltwater in the near surface firn downstream of our crevasse field. Repeat firn density profiles from nearby site T4 (47 km away) show an increase in firn density from ~600 to ~700 kg/m³ above 20m depth between 1967 and 2019. Repeat firn density profiles from higher elevations above the crevasse field show no such recent increase in near-surface density. A transition toward more brittle firn conditions, associated with the appearance of refrozen ice layers within the near surface, may be responsible for the recent opening of this crevasse field.
The crevasse field examined here is not unique; similar fields are appearing across the region, suggesting a regional transition toward more fracture-prone firn.
How to cite: Løkkegaard, A., Colgan, W., Khan, S. A., Fahrner, D. R., Polzin, M., Hughes, J., Lippert, E. Y., and Pickell, D.: Brittle Landscapes: A Case Study of Crevasse Development in Firn, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16344, https://doi.org/10.5194/egusphere-egu25-16344, 2025.
Monitoring glaciers is essential for understanding their response to climate change, managing freshwater resources, and mitigating geohazards such as icefalls and glacial lake outburst floods (GLOFs). Traditional glacier monitoring techniques often face challenges connected to limited spatial and temporal resolution and logistical constraints in hazardous terrain. These challenges are especially pronounced for steep and fast-moving glaciers with large surface changes and high velocities. In such settings high temporal and spatial resolution data are essential for capturing rapid surface changes and understanding glacier dynamics.
We introduce the potential of autonomous unmanned aerial vehicles (UAVs) operating from stationary drone docks as a novel, flexible, and cost-effective solution for glacier monitoring. We tested a DJI Dock 2 at Flatbreen and Bøyabreen, two outlet glaciers of the Jostedalsbreen ice cap in Western Norway. We captured high-resolution aerial imagery for photogrammetric mapping, conducted at customizable intervals (hourly, daily, weekly). These datasets enabled the generation of multitemporal point clouds, digital terrain models and orthophotos. To derive surface velocities and detect changes over time we used the 3D point cloud analysis algorithm M3C2 and 2D feature-tracking methods.
Preliminary findings revealed that autonomous UAVs can monitor surface changes and velocity patterns effectively with a high temporal and spatial resolution. Surface velocities for both glaciers ranged from 0.4 to 1.5 m per day, with higher rates observed in steeper sections of the glacier. The data offers unique insights on short-term processes, including acceleration phases, crevassing, the collapse of subglacial cavities and several significant icefall events. The results demonstrate a level of detection of 2-4 cm, which allows for the identification of subtle changes at cm-scale. Integrating autonomous UAVs into existing glacier monitoring frameworks represents a significant advancement in data collection by improving spatial and temporal resolution and time efficient workflows through automation in data collection and post processing.
This study highlights the feasibility and effectiveness of autonomous UAVs for near-continuous glacier and geohazard monitoring, particularly valuable in inaccessible or dangerous environments. We demonstrate the potential of autonomous UAVs to track both long-term glacier dynamics and short-term changes. This capability enhances process understanding and provides a robust foundation for developing UAV based early warning systems for glacial hazards. While challenges remain, particularly in difficult weather conditions, low visibility, and regulatory compliance, this innovative approach demonstrates substantial potential for monitoring, supporting effective risk management in regions vulnerable to glacial hazards.
How to cite: Maschler, A., Snook, P., Scheiber, T., Schild, L., and Langes, S.: Autonomous UAVs for Monitoring Glacier Dynamics and Hazards: A Case Study from Jostedalsbreen, Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18201, https://doi.org/10.5194/egusphere-egu25-18201, 2025.
The ice shelf surface temperature (IST) is a critical environmental and climatic parameter. Current wide-swath IST products, such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR), typically have a spatial resolution of around 1000 m. In contrast, the Medium Resolution Spectral Imager (MERSI) provides a thermal infrared channel with a wide swath of 2900 km and a higher spatial resolution of 250 m. In this study, we developed a practical single-channel algorithm to retrieve ISTs from MERSI series data, addressing several key challenges: (1) the wide range of incidence angles; (2) the unstable, snow-covered ice surface; and (3) variations in atmospheric water vapor content. To enhance retrieval accuracy, we simulated directional emissivity to mitigate the limitations of assuming a constant emissivity. Simulations were conducted for various ice surface types, with the sun crust type identified as the most suitable for IST retrieval. Additionally, real-time water vapor content was estimated using a band ratio method based on MERSI near-infrared data. The proposed algorithm demonstrates improved accuracy and reliability compared to the original approach. The retrieved IST values are higher than those from IceBridge measurements, with a mean bias of 1.06 K and an RMSE of 1.76 K. Validation against AWS measurements further confirms the algorithm's performance, yielding an RMSE of about 2.5 K for both MERSI-I and MERSI-II data.
How to cite: li, Y. and Liu, T.: The Antarctic ice shelf surface temperature retrieval from Chinese MERSI series data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1803, https://doi.org/10.5194/egusphere-egu25-1803, 2025.
Glaciers in Svalbard are undergoing rapid changes due to Arctic Amplification that demand frequent and accurate monitoring. We present a deep learning model, Intensity-Coherence-Evolution-mapper (ICEmapper) designed to extract annual glacier outlines from Sentinel-1 time series data regardless of cloud cover. The model combines SAR backscatter intensity and interferometric coherence. In extensive validation tests against manually digitised optical imagery, ICEmapper demonstrates human-expert accuracy, with intersection of union score higher than 0.95, total area discrepancies below 0.5%, median distance deviations under 15 m, and 95th percentile deviations within 250 m. Additionally, we report calibrated uncertainties of our classification results at the pixel level, allowing for detailed analysis of significant changes as well as total area uncertainty estimation. This performance allowed us to construct a continuous inventory of glacier outlines in Svalbard from 2016 to 2023.
The results of area change analysis suggest a substantial escalation in the rate of glacier area loss to about 180 km2 a−1 in the last decade, nearly doubling from the previously reported ~80 km2 a−1 (1980–2010; Nuth et al., 2013). This increase is predominantly driven by enhanced calving at tidewater glaciers, although climatic signal also shows a significant correlation with the area loss of land-terminating glaciers. Surging glaciers, particularly in the Nathorstbreen system and Austfonna, exhibited unique behaviours that can temporarily increase the total glacier area. In 2016, the Nathorstbreen system gained 107.76 km2, while Austfonna, Basin-3 expanded by 86.54 km2 as compared to the Randolph Glacier Inventory, jointly offsetting net losses by approximately two years. In the last decade, however, surging glaciers lost area more rapidly (−0.57 km2 a−1 on average) than the non-surging ones (−0.09 km2 a−1). Additionally, our analysis uncovered a surge in Austfonna, Basin-7 starting in 2019 and not reported previously, emphasising the capability of annually updated inventories to complement other methods of surge detection.
Our methods have the potential to be transferred to other glacierised regions and enhance monitoring and understanding of glacier area changes on larger scales using Sentinel-1 data.
How to cite: Maslov, K., Schellenberger, T., Persello, C., and Stein, A.: Deep learning applied to Sentinel-1 data shows doubling of glacier area loss in Svalbard compared to 1980–2010 , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9898, https://doi.org/10.5194/egusphere-egu25-9898, 2025.
Glaciers are vital components of Alpine ecosystems and are increasingly threatened by climate change. Therefore monitoring glacier elevation change over time is an essential task and aids in modeling future freshwater availability. By leveraging remote sensing technologies with high revisit frequencies, we can gain a comprehensive understanding of glacier dynamics, including retreat rates, the influence of landslides, and overall glacier health.
Unmanned Aerial Vehicles (UAVs) provide the most precise means of tracking glacier surface changes, however, their use is often constrained by high costs and the difficulty of conducting in-situ measurements in extreme weather or remote locations. In these cases, remote sensing and satellite altimetry offer a practical and viable alternative.
In this study, we present a novel methodology utilizing Global Ecosystem Dynamics Investigation (GEDI) altimetry data. GEDI is a LiDAR (Light Detection and Ranging) sensor collecting altimetric data with a 25 m footprint size and 60 m along-track spacing from the International Space Station [1,2]. GEDI was active from early 2019 till 2023 when it was temporarily hibernated and has recently been reactivated.
The proposed method relies exclusively on available GEDI bands and is fully implemented within Google Earth Engine (GEE). We have applied the methodology to three Alpine glaciers using nine GEDI acquisitions and evaluated its performance through comparisons with reference Digital Surface Models (DSMs) generated from aerial and drone photogrammetry and LiDAR data.
After applying outlier detection techniques solely based on GEDI bands, GEDI-derived glacier profiles along the tracks provided valuable surface elevation information. The results showed a strong correlation (r = 0.99) with reference DSMs along with low dispersion and R2 of 0.99, based on an average of 135 GEDI footprints per glacier. Additionally, the analysis indicated that GEDI could capture seasonal variations in glacier surfaces, detecting the melt and gain in the snowpack.
While GEDI lacks the capability to map an entire glacier extent as photogrammetric block imagery does, its higher acquisition rate, including coverage of smaller glaciers, offers a significant advantage. Integrating GEDI with traditional approaches thus enables more continuous and comprehensive glacier monitoring.
References:
[1] Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: Glacier Monitoring Using GEDI Data in Google Earth Engine: Outlier Removal and Accuracy Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10176, https://doi.org/10.5194/egusphere-egu24-10176, 2024.
[2] Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: GEDI DATA WITHIN GOOGLE EARTH ENGINE: PRELIMINARY ANALYSIS OF A RESOURCE FOR INLAND SURFACE WATER MONITORING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 131–136, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-131-2023, 2023.
How to cite: Hamoudzadeh, A., Ravanelli, R., and Crespi, M.: Glacier Monitoring in the Alps: Leveraging GEDI Altimetry for Surface Elevation Change Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12715, https://doi.org/10.5194/egusphere-egu25-12715, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 4
EGU25-19031 | Posters virtual | VPS18
Antarctic ice shelf crevasse detection using multi-source remote sensing data and machine learningWed, 30 Apr, 14:00–15:45 (CEST) | vP4.4
Ice crevasses are pervasive features across the Arctic and Antarctic ice sheets. These deep, open fractures in the ice surface serve as critical conduits for transporting surface meltwater into the englacial system, significantly impacting ice sheet hydrology and stability. Accurate mapping of the spatial and temporal distribution of ice crevasses is vital for advancing our understanding of ice sheet dynamics and their evolution. Remote sensing technology provides a robust platform to achieve this purpose, while the rapid advancement of machine learning algorithms offers substantial benefits for automated crevasse detection, facilitating efficient and large-scale mapping. This study conducts a comprehensive comparison of the performance of various machine learning models, including CNN, U-Net, ResNet, and DeepLab, for ice crevasse extraction. Through quantitative evaluation metrics and visual inspection, the optimal machine learning model was selected to map ice crevasses on Antarctic ice shelves using multi-source remote sensing data, such as SAR and optical satellite imagery. Furthermore, this work explores the strengths and limitations of various machine learning in detecting ice crevasse and proposes potential solutions for further refinement. This study aims to contributes to enhancing ice crevasse detection and offering robust ice crevasse datasets, which is crucial for reliable analyzing the dynamic of the Antarctic ice sheet in the future.
How to cite: Liang, S. and Xiao, X.: Antarctic ice shelf crevasse detection using multi-source remote sensing data and machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19031, https://doi.org/10.5194/egusphere-egu25-19031, 2025.
