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Remote Sensing of the Cryosphere

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 30683

Special Issue Editors

Earth and Environment Discipline, Department of Natural Sciences, University of Michigan-Dearborn, 4901 Evergreen Rd., 211 Science Faculty Center, Dearborn, MI 48128, USA
Interests: cryosphere; environmental change; environmental hazards; human-environment interactions; mountain geography; quaternary geology
Special Issues, Collections and Topics in MDPI journals
Department of Physical Geography and Landscape Design, Saint-Petersburg State University, 199034 St. Petersburg, Russia
Interests: glaciology and glacial geomorphology; geocryology; palaeogeography of mountainous Eurasian countries in Pleistocene and Holocene; rhythms in landscape and space
Special Issues, Collections and Topics in MDPI journals
Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City 700000, Vietnam
Interests: environmental assessment and monitoring; remote sensing of the cryosphere; remote sensing of wetlands; Andes; Himalayas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The cryosphere, the frozen water part of the Earth system, is sensitive to changes in global climate; hence, scientists monitor its state and changes, particularly with remote sensing. We welcome a broad spectrum of contributions to this Special Issue:

  • Frozen ground, glacial geomorphology, glaciers, ice caps and sheets, lake/river/sea ice, and snow cover;
  • Recent state of our cryosphere;
  • Changes in the cryosphere such as deglaciation;
  • Cryospheric hazards and risks;
  • Theories, methodologies, and applications;
  • Laboratory and field investigations;
  • Terrestrial and space measurements;
  • Local, regional, and global scales;
  • Extraterrestrial cryospheres;
  • Any other topic concerned with the cryosphere.

This Special Issue aims to represent the frontier in remote sensing research on the cryosphere. Cryospheric science is an interdisciplinary earth science, and we welcome authors from disciplines such as geology, hydrology, meteorology, and climatology, as well as from other disciplines such as biology, engineering, and environmental science.

Prof. Dr. Ulrich Kamp
Prof. Dr. Dmitry Ganyushkin
Dr. Bijeesh K. Veettil
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cryosphere
  • GIS
  • glacier
  • ice
  • frozen ground
  • permafrost
  • remote sensing
  • snow

Published Papers (16 papers)

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17 pages, 8572 KiB  
Article
Combining Ground Penetrating Radar and Frequency Domain Electromagnetic Surveys to Characterize the Structure of the Calderone Glacieret (Gran Sasso d’Italia, Italy)
Remote Sens. 2023, 15(10), 2615; https://doi.org/10.3390/rs15102615 - 17 May 2023
Viewed by 977
Abstract
Ice is a rich reservoir of past climate information, and the well-documented increasing rate of glacier retreat represents a great loss for paleoclimate studies. In this framework, the Ice Memory project aims to extract and analyze ice cores from glacier regions worldwide and [...] Read more.
Ice is a rich reservoir of past climate information, and the well-documented increasing rate of glacier retreat represents a great loss for paleoclimate studies. In this framework, the Ice Memory project aims to extract and analyze ice cores from glacier regions worldwide and store them in Antarctica as a heritage record for future generations of scientists. Ice coring projects usually require a focused geophysical investigation, often based on Ground Penetrating Radar (GPR) prospecting to assess the most suitable drilling positions. As a novel approach in the Calderone Glacieret, we integrated the GPR method with Frequency Domain Electromagnetic (FDEM) surveys, a technique not commonly applied in the glacial environment. We used a separated-coils FDEM instrument to characterize the glacieret structure. The acquired FDEM datasets were inverted and compared to the GPR data and borehole information. The results demonstrated the capability of the FDEM technique to define the structure of the glacieret correctly; therefore, the potential to be applied in frozen subsoil environments. This opens new perspectives for the use of the FDEM technique to characterize periglacial environments, such as rock glaciers, where the coarse-blocky surface hinders data acquisition and enhances the problem of signal scattering. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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20 pages, 52956 KiB  
Article
Comparing Thermal Regime Stages along a Small Yakutian Fluvial Valley with Point Scale Measurements, Thermal Modeling, and Near Surface Geophysics
Remote Sens. 2023, 15(10), 2524; https://doi.org/10.3390/rs15102524 - 11 May 2023
Viewed by 1140
Abstract
Arctic regions are highly impacted by the global temperature rising and its consequences and influences on the thermo-hydro processes and their feedbacks. Theses processes are especially not very well understood in the context of river–permafrost interactions and permafrost degradation. This paper focuses on [...] Read more.
Arctic regions are highly impacted by the global temperature rising and its consequences and influences on the thermo-hydro processes and their feedbacks. Theses processes are especially not very well understood in the context of river–permafrost interactions and permafrost degradation. This paper focuses on the thermal characterization of a river–valley system in a continuous permafrost area (Syrdakh, Yakutia, Eastern Siberia) that is subject to intense thawing, with major consequences on water resources and quality. We investigated this Yakutian area through two transects crossing the river using classical tools such as in–situ temperature measurements, direct active layer thickness estimations, unscrewed aerial vehicle (UAV) imagery, heat transfer numerical experiments, Ground-Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). Of these two transects, one was closely investigated with a long-term temperature time series from 2012 to 2018, while both of them were surveyed by geophysical and UAV data acquisition in 2017 and 2018. Thermodynamical numerical simulations were run based on the long-term temperature series and are in agreement with river thermal influence on permafrost and active layer extensions retrieved from GPR and ERT profiles. An electrical resistivity-temperature relationship highlights the predominant role of water in such a complicated system and paves the way to coupled thermo-hydro-geophysical modeling for understanding permafrost–river system evolution. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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39 pages, 102171 KiB  
Article
Post-Little Ice Age Glacier Recession in the North-Chuya Ridge and Dynamics of the Bolshoi Maashei Glacier, Altai
Remote Sens. 2023, 15(8), 2186; https://doi.org/10.3390/rs15082186 - 20 Apr 2023
Cited by 1 | Viewed by 1792
Abstract
The glacier recession of the North-Chuya ridge, Altai, after the maximum of the Little Ice Age (LIA) is estimated based on remote sensing and in situ studies of the Bolshoi Maashei glacier. The glacier area decreased from 304.9 ± 23.49 km2 at [...] Read more.
The glacier recession of the North-Chuya ridge, Altai, after the maximum of the Little Ice Age (LIA) is estimated based on remote sensing and in situ studies of the Bolshoi Maashei glacier. The glacier area decreased from 304.9 ± 23.49 km2 at the LIA maximum to 140.24 ± 16.19 km2 in 2000 and 120.02 ± 16.19 km2 in 2021. The average equilibrium-line altitude (ELA) rise after the LIA was 207 m. The reduction of glaciers was caused by the warming trend, most rapid in the 1990s, and by the decrease in precipitation after the mid-1980s. The volume of glaciers decreased from approximately 16.5 km3 in the LIA maximum to 5.6–5.8 km3 by 2021. From the LIA maximum to 2022, the Bolshoi Maashei glacier decreased from 17.49 km2 to 6.25 km2, and the lower point rose from 2160 m to 2225 m. After the LIA, the glacial snout retreat was about 1 km. The fastest retreat of the glacier terminus was estimated in 2010–2022 as 14.0 m a−1 on average. The glacier mass balance index was calculated, with the results showing a strong negative trend from the mid-1980s until now. Strong melt rates caused the increase in the area of the Maashei lake, which could lead to the weakening of its dam, and prepared for its failure in 2012. The current climatic tendencies are unfavorable for the glaciers. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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26 pages, 51334 KiB  
Article
An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales
Remote Sens. 2023, 15(5), 1231; https://doi.org/10.3390/rs15051231 - 23 Feb 2023
Cited by 1 | Viewed by 1500
Abstract
Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, [...] Read more.
Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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26 pages, 10729 KiB  
Article
Automated Identification of Thermokarst Lakes Using Machine Learning in the Ice-Rich Permafrost Landscape of Central Yakutia (Eastern Siberia)
Remote Sens. 2023, 15(5), 1226; https://doi.org/10.3390/rs15051226 - 23 Feb 2023
Cited by 4 | Viewed by 2099
Abstract
The current rate and magnitude of temperature rise in the Arctic are disproportionately high compared to global averages. Along with other natural and anthropogenic disturbances, this warming has caused widespread permafrost degradation and soil subsidence, resulting in the formation of thermokarst (thaw) lakes [...] Read more.
The current rate and magnitude of temperature rise in the Arctic are disproportionately high compared to global averages. Along with other natural and anthropogenic disturbances, this warming has caused widespread permafrost degradation and soil subsidence, resulting in the formation of thermokarst (thaw) lakes in areas of ice-rich permafrost. These lakes are hotspots of greenhouse gas emissions (CO2 and CH4), but with substantial spatial and temporal heterogeneity across Arctic and sub-Arctic regions. In Central Yakutia (Eastern Siberia, Russia), nearly half of the landscape has been affected by thermokarst processes since the early Holocene, resulting in the formation of more than 10,000 partly drained lake depressions (alas lakes). It is not yet clear how recent changes in temperature and precipitation will affect existing lakes and the formation of new thermokarst lakes. A multi-decadal remote sensing analysis of lake formation and development was conducted for two large study areas (~1200 km2 each) in Central Yakutia. Mask Region-Based Convolutional Neural Networks (R-CNN) instance segmentation was used to semi-automate lake detection in Satellite pour l’Observation de la Terre (SPOT) and declassified US military (CORONA) images (1967–2019). Using these techniques, we quantified changes in lake surface area for three different lake types (unconnected alas lake, connected alas lake, and recent thermokarst lake) since the 1960s. Our results indicate that unconnected alas lakes are the dominant lake type, both in the number of lakes and total surface area coverage. Unconnected alas lakes appear to be more susceptible to changes in precipitation compared to the other two lake types. The majority of recent thermokarst lakes form within 1 km of observable human disturbance and their surface area is directly related to air temperature increases. These results suggest that climate change and human disturbances are having a strong impact on the landscape and hydrology of Central Yakutia. This will likely affect regional and global carbon cycles, with implications for positive feedback scenarios in a continued climate warming situation. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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22 pages, 65930 KiB  
Article
A Multi-Resolution Approach to Point Cloud Registration without Control Points
Remote Sens. 2023, 15(4), 1161; https://doi.org/10.3390/rs15041161 - 20 Feb 2023
Cited by 3 | Viewed by 2288
Abstract
Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the [...] Read more.
Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (<1 km) vertical glacier calving front reconstructed from two cameras at Fountain Glacier, Nunavut and a long-range (>3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of point clouds from terrestrial imagery with acceptable accuracy, opening the door for new possibilities in remote monitoring for change-detection, such as monitoring calving rates, glacier surges, or other seasonal changes at remote field locations. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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20 pages, 5631 KiB  
Article
The First Inventory of Rock Glaciers in the Zhetysu Alatau: The Aksu and Lepsy River Basins
Remote Sens. 2023, 15(1), 197; https://doi.org/10.3390/rs15010197 - 30 Dec 2022
Cited by 2 | Viewed by 1983
Abstract
While rock glaciers (RGs) are widespread in the Zhetysu Alatau mountain range of Tien Shan (Kazakhstan), they have not yet been systematically investigated. In this study, we present the first rock glacier inventory of this region containing 256 rock glaciers with quantitative information [...] Read more.
While rock glaciers (RGs) are widespread in the Zhetysu Alatau mountain range of Tien Shan (Kazakhstan), they have not yet been systematically investigated. In this study, we present the first rock glacier inventory of this region containing 256 rock glaciers with quantitative information about their locations, geomorphic parameters, and downslope velocities, as established using a method that combines SAR interferometry and optical images from Google Earth. Our inventory shows that most of the RGs are talus-derived (61%). The maximum downslope velocity of the active rock glaciers (ARGs) was 252 mm yr−1. The average lower height of rock glaciers in this part of the Zhetysu Alatau was 3036 m above sea level (ASL). The largest area of rock glaciers was located between 2800 and 3400 m ASL and covered almost 86% of the total area. Most rock glaciers had a northern (northern, northeastern, and northwestern) orientation, which indicated the important role of solar insolation in their formation and preservation. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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16 pages, 8101 KiB  
Article
Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China
Remote Sens. 2022, 14(23), 5989; https://doi.org/10.3390/rs14235989 - 26 Nov 2022
Cited by 1 | Viewed by 1365
Abstract
Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to [...] Read more.
Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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16 pages, 18518 KiB  
Article
Mapping Area Changes of Glacial Lakes Using Stacks of Optical Satellite Images
Remote Sens. 2022, 14(23), 5973; https://doi.org/10.3390/rs14235973 - 25 Nov 2022
Cited by 4 | Viewed by 2123
Abstract
Glacial lakes are an important and dynamic component of terrestrial meltwater storage, responding to climate change and glacier retreat. Although there is evidence of rapid worldwide growth of glacial lakes, changes in frequency and magnitude of glacier lake outbursts under climatic changes are [...] Read more.
Glacial lakes are an important and dynamic component of terrestrial meltwater storage, responding to climate change and glacier retreat. Although there is evidence of rapid worldwide growth of glacial lakes, changes in frequency and magnitude of glacier lake outbursts under climatic changes are not yet understood. This study proposes and discusses a method framework for regional-scale mapping of glacial lakes and area change detection using large time-series of optical satellite images and the cloud processing tool Google Earth Engine in a semi-automatic way. The methods are presented for two temporal scales, from the 2-week Landsat revisit period to annual resolution. The proposed methods show how constructing an annual composite of pixel values such as minimum or maximum values can help to overcome typical problems associated with water mapping from optical satellite data such as clouds, or terrain and cloud shadows. For annual-resolution glacial lake mapping, our method set only involves two different band ratios based on multispectral satellite images. The study demonstrates how the proposed method framework can be applied to detect rapid lake area changes and to produce a complete regional-scale glacial lake inventory, using the Greater Caucasus as example. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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20 pages, 16744 KiB  
Article
Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter
Remote Sens. 2022, 14(22), 5644; https://doi.org/10.3390/rs14225644 - 08 Nov 2022
Cited by 3 | Viewed by 1597
Abstract
Satellite-based C-band synthetic aperture radar (SAR) imagery is an effective tool to map and monitor river ice on regional scales because the SAR backscatter is affected by various physical properties of the ice, including roughness, thickness, and structure. Validation of SAR-based river ice [...] Read more.
Satellite-based C-band synthetic aperture radar (SAR) imagery is an effective tool to map and monitor river ice on regional scales because the SAR backscatter is affected by various physical properties of the ice, including roughness, thickness, and structure. Validation of SAR-based river ice classification maps is typically performed using expert interpretation of aerial or ground reference images of the river ice surface, using visually apparent changes in surface roughness to delineate different ice classes. Although many studies achieve high classification accuracies using this qualitative technique, it is not possible to determine if the river ice information contained within the SAR backscatter data originates from the changes in surface roughness used to create the validation data, or from some other ice property that may be more relevant for ice jam forecasting. In this study, we present the first systematic, quantitative investigation of the effect of river ice surface roughness on C-band Sentinel-1 backscatter. We use uncrewed aerial vehicle-based Structure from Motion photogrammetry to generate high-resolution (0.03 m) digital elevation models of river ice surfaces, from which we derive measurements of surface roughness. We employ Random Forest models first to repeat previous ice classification studies, and then as regression models to explore quantitative relationships between ice surface roughness and Sentinel-1 backscatter. Classification accuracies are similar to those reported in previous studies (77–96%) but poor regression performance for many surface roughness metrics (5–113% mean absolute percentage errors) indicates a weak relationship between river ice surface roughness and Sentinel-1 backscatter. Additional work is necessary to determine which physical ice properties are strong controls on C-band SAR backscatter. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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24 pages, 7833 KiB  
Article
Ice Aprons in the Mont Blanc Massif (Western European Alps): Topographic Characteristics and Relations with Glaciers and Other Types of Perennial Surface Ice Features
Remote Sens. 2022, 14(21), 5557; https://doi.org/10.3390/rs14215557 - 03 Nov 2022
Cited by 3 | Viewed by 2116
Abstract
Ice aprons are poorly studied and not well-defined thin ice bodies adhering to high altitude steep rock faces, but are present in most Alpine-type high mountain environments worldwide. This study aims to precisely define ice aprons based on a detailed analysis of their [...] Read more.
Ice aprons are poorly studied and not well-defined thin ice bodies adhering to high altitude steep rock faces, but are present in most Alpine-type high mountain environments worldwide. This study aims to precisely define ice aprons based on a detailed analysis of their topographical characteristics in the Mont Blanc massif (western European Alps). For this, we accurately identified and precisely mapped 423 ice aprons using a combination of high-resolution optical satellite images from 2019. To better understand their relationship with other types of glaciers, especially the steep slope glaciers and other surface ice bodies, we built a detailed inventory at the scale of the massif that incorporates nine different types of perennial surface ice bodies. In addition, an analysis using different topographic factors helped us to better understand the preferred locations of the ice aprons. We show that they predominantly occur on west-oriented steep and topographically rugged rock slopes above the local Equilibrium Line Altitude (~3200 m a.s.l.), with concave profile curvatures around them that facilitate snow accumulation. They are also found in areas underlain by permafrost. The extensive inventory also helped us to identify different types of ice aprons based on their relationships with glaciers/ice bodies. The analysis shows that ice aprons existing at the headwall of large glaciers above a bergschrund are the most dominant ice apron type in the study area, with ~82% of the total. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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23 pages, 39094 KiB  
Article
Mapping Ice Flow Velocity of Tidewater Glaciers in Hornsund Fiord Area with the Use of Autonomous Repeat Image Feature Tracking (2018–2022)
Remote Sens. 2022, 14(21), 5429; https://doi.org/10.3390/rs14215429 - 28 Oct 2022
Cited by 2 | Viewed by 1565
Abstract
Dynamic climate changes are particularly apparent in polar regions. Glaciers are retreatng towards the land at a very fast pace. This study demonstrates the application of the feature tracking method in the analysis of ice flow velocity in the region of the Hornsund [...] Read more.
Dynamic climate changes are particularly apparent in polar regions. Glaciers are retreatng towards the land at a very fast pace. This study demonstrates the application of the feature tracking method in the analysis of ice flow velocity in the region of the Hornsund fiord, southern Spitsbergen, in the years 2018–2022. The calculations were based on the Geogrid and autoRIFT environments and on the Sentinel 1 images. The study also employed external data, such as a numerical terrain model and reference velocity values. The input data, e.g., the chip size and the search limit, were prepared accounting for the specific character of the investigated area. The velocities were calculated for nine biggest glaciers which terminated in the fiord. The accuracy of the results was identified by calculating the median absolute deviation (MAD) of the obtained displacement velocity values from the reference value for areas identified as stable. The study also attempted a causal analysis of the influence of weather factors on the dynamics of ice mass displacement. A systematic year-to-year decrease of the velocity was observed for the entire fiord. In the case of several glaciers, changes related to the ablation season (summer) are also clearly visible. The research results are promising and fill a research gap related to the absence of permanent monitoring and analysis of the dynamics of ice flow in polar regions. It is the first complex and precise study of glacier surface velocity changes, performed on the basis of satellite radar images for the entire Hornsund fiord. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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18 pages, 17320 KiB  
Article
Permafrost Early Deformation Signals before the Norilsk Oil Tank Collapse in Russia
Remote Sens. 2022, 14(19), 5036; https://doi.org/10.3390/rs14195036 - 09 Oct 2022
Cited by 2 | Viewed by 1698
Abstract
Despite the profound roles of surface deformation monitoring techniques in observing permafrost surface stability, predetermining the approximate location and time of possibly occurring severe permafrost degradation before applying these techniques is extremely necessary, but has received little attention. Taking the oil tank collapse [...] Read more.
Despite the profound roles of surface deformation monitoring techniques in observing permafrost surface stability, predetermining the approximate location and time of possibly occurring severe permafrost degradation before applying these techniques is extremely necessary, but has received little attention. Taking the oil tank collapse accident in the Norilsk region as a case, we explored this concern by analyzing the permafrost deformation mechanisms and determining early surface deformation signals. Regarding this case, we firstly applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to obtain its permafrost surface deformation rate, then utilized a sine model to decompose its interannual deformation and seasonal deformation, and finally compared the relationship between the topographic slope and deformation rate. Based on experimental results, we reveal that when the annual average temperature continuously increases at a rate of 2 °C/year for 2∼3 consecutive years, permafrost areas with relatively large topographic slopes (>15°) are more prone to severe surface deformation during the summer thaw period. Therefore, this paper suggests that permafrost areas with large topographic slopes (>15°) should be taken as the key surveillance areas, and that the appropriate monitoring time for employing surface deformation monitoring techniques should be the summer thawing period after a continuous increase in annual average temperature at a rate of 2 °C/year for 2∼3 years. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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36 pages, 5467 KiB  
Article
Changes over the Last 35 Years in Alaska’s Glaciated Landscape: A Novel Deep Learning Approach to Mapping Glaciers at Fine Temporal Granularity
Remote Sens. 2022, 14(18), 4582; https://doi.org/10.3390/rs14184582 - 14 Sep 2022
Cited by 6 | Viewed by 2487
Abstract
Glaciers are important sentinels of a changing climate, crucial components of the global cryosphere and integral to their local landscapes. However, many of the commonly used methods for mapping glacier change are labor-intensive and limit the temporal and spatial scope of existing research. [...] Read more.
Glaciers are important sentinels of a changing climate, crucial components of the global cryosphere and integral to their local landscapes. However, many of the commonly used methods for mapping glacier change are labor-intensive and limit the temporal and spatial scope of existing research. This study addresses some of the limitations of prior approaches by developing a novel deep-learning-based method called GlacierCoverNet. GlacierCoverNet is a deep neural network that relies on an extensive, purpose-built training dataset. Using this model, we created a record of over three decades long at a fine temporal cadence (every two years) for the state of Alaska. We conducted a robust error analysis of this dataset and then used the dataset to characterize changes in debris-free glaciers and supraglacial debris over the last ~35 years. We found that our deep learning model could produce maps comparable to existing approaches in the capture of areal extent, but without manual editing required. The model captured the area covered with glaciers that was ~97% of the Randolph Glacier Inventory 6.0 with ~6% and ~9% omission and commission rates in the southern portion of Alaska, respectively. The overall model area capture was lower and omission and commission rates were significantly higher in the northern Brooks Range. Overall, the glacier-covered area retreated by 8425 km2 (−13%) between 1985 and 2020, and supraglacial debris expanded by 2799 km2 (64%) during the same period across the state of Alaska. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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12 pages, 16768 KiB  
Communication
Operational Processing of Big Satellite Data for Monitoring Glacier Dynamics: Case Study of Muldrow Glacier
Remote Sens. 2022, 14(11), 2679; https://doi.org/10.3390/rs14112679 - 03 Jun 2022
Cited by 2 | Viewed by 1724
Abstract
Frequent acquisition of Synthetic Aperture Radar (SAR) data by the European Sentinel-1 satellites provides an opportunity for monitoring the dynamics of worldwide glaciers. We present a fully-automated processing system for producing multi-dimensional time series of glacier flow. We then use this fully-automated processing [...] Read more.
Frequent acquisition of Synthetic Aperture Radar (SAR) data by the European Sentinel-1 satellites provides an opportunity for monitoring the dynamics of worldwide glaciers. We present a fully-automated processing system for producing multi-dimensional time series of glacier flow. We then use this fully-automated processing system to investigate the dynamics of Muldrow Glacier, located in the Denali National Park and Preserve (Alaska, AK, USA) during the October 2014—November 2021 period. We compute north, east, and vertical Surface-Parallel-Flow (SPF) and non-Surface-Parallel-Flow (nSPF) components of flow velocity and displacement with an average temporal resolution of 9 days and grid spacing of 100 m. During this period, we observe a glacier surge, a manifold increase in glacier flow velocity, that started as early as 2017 and continues until the present; however, the near completion of this surge is apparent. This glacier previously surged in 1906–1912 (the exact date is unknown) and in 1956–1957. We present our results in different ways to emphasize various aspects of the observed surge and demonstrate the full capability of our processing system. As the availability of SAR data improves, we expect that the fully-automated processing systems, similar to the one presented here, will play an increasingly dominant role and soon entirely replace manual processing. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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14 pages, 3544 KiB  
Technical Note
Variability of Surface Radiation Budget over Arctic during Two Recent Decades from Perspective of CERES and ERA5 Data
Remote Sens. 2023, 15(3), 829; https://doi.org/10.3390/rs15030829 - 01 Feb 2023
Viewed by 1659
Abstract
This study focused on surface radiation budget, one of the essential factors for understanding climate change. Arctic surface radiation budget was summarized and explained using a satellite product, Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF), and reanalysis [...] Read more.
This study focused on surface radiation budget, one of the essential factors for understanding climate change. Arctic surface radiation budget was summarized and explained using a satellite product, Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF), and reanalysis data, ERA5. Net radiation records indicated an increasing trend only in ERA5, with EBAF indicating a decreasing trend in the Arctic Circle (AC; poleward from 65°N) from 2000 to 2018. The differences in the net radiation trend between product types was due to longwave downward radiation. The extreme season was selected according to the seasonality of net radiation, surface air temperature, and sea ice extent. The surface radiation budget was synthesized for extreme season in the AC. Regardless of the data, net radiation tended to increase in the summer on an annual trend. By contrast, in the winter, trend of surface net radiation was observed in which ERA5 increased and EBAF decreased. The difference in surface radiation is represented in longwave of each data. This comprehensive information can be used to analyze and predict the surface energy budget, transport, and interaction between the atmosphere and surface in the Arctic. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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