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Special Issue "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: 30 April 2023 | Viewed by 7514

Special Issue Editors

Prof. Dr. Ulrich Kamp
E-Mail Website
Guest Editor
Department of Natural Sciences, University of Michigan-Dearborn, 4901 Evergreen Rd., 114 Science Faculty Center, Dearborn, MI 48128, USA
Interests: cryosphere; environmental change; hazards; human–environment interactions; mountains
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dmitry Ganyushkin
E-Mail Website
Guest Editor
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
Dr. Bijeesh K. Veettil
E-Mail Website
Guest Editor
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 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 2500 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 (10 papers)

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Research

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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
Viewed by 602
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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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
Viewed by 421
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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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
Viewed by 663
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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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 1 | Viewed by 532
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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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
Viewed by 783
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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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
Viewed by 531
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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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
Viewed by 612
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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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 1 | Viewed by 910
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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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
Viewed by 950
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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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 182
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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