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Remote Sensing of the Cryosphere (Second Edition)

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

Deadline for manuscript submissions: 28 March 2025 | Viewed by 12221

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Special Issue Editors

Prof. Dr. Ulrich Kamp

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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
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Prof. Dr. Dmitry Ganyushkin

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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
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Dr. Bijeesh K. Veettil

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Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Vietnam
Interests: environmental assessment and monitoring; remote sensing of the cryosphere; remote sensing of wetlands; Andes; Himalayas
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Special Issue Information

Dear Colleagues,

The first volume of the Special Issue “Remote Sensing of the Cryosphere” received positive feedback and includes 16 contributions (https://www.mdpi.com/journal/remotesensing/special_issues/RS_Cryosphere). The second volume “Remote Sensing of the Cryosphere II” aims to continue this success. The call remains identical as follows:

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

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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
remote sensing
snow

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Published Papers (9 papers)

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17 pages, 9390 KiB

Open AccessArticle

Applicability of Relatively Low-Cost Multispectral Uncrewed Aerial Systems for Surface Characterization of the Cryosphere

by Colby F. Rand and Alia L. Khan

Remote Sens. 2024, 16(19), 3662; https://doi.org/10.3390/rs16193662 - 1 Oct 2024

Viewed by 939

Abstract

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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22 pages, 6670 KiB

Open AccessArticle

Spatiotemporal Changes of Glaciers in the Yigong Zangbo River Basin over the Period of the 1970s to 2023 and Their Driving Factors

by Suo Yuan, Ninglian Wang, Jiawen Chang, Sugang Zhou, Chenlie Shi and Mingjie Zhao

Remote Sens. 2024, 16(17), 3272; https://doi.org/10.3390/rs16173272 - 3 Sep 2024

Viewed by 852

Abstract

The glaciers in southeastern Tibet Plateau (SETP) influenced by oceanic climate are sensitive to global warming, and there remains a notable deficiency in accurate multitemporal change analyses of these glaciers. We conduct glacier inventories in the Yigong Zangbo River Basin (YZRB) in SETP for the years 1988, 2015, and 2023 utilizing Landsat and Sentinel-2 imagery, and analyze the glacier spatiotemporal variation incorporating the existing glacier inventory data. Since the 1970s until 2023, the glaciers significantly retreated at a rate of 0.76 ± 0.11%·a⁻¹, with the area decreasing from 2583.09 ± 88.80 km² to 1635.89 ± 71.74 km², and the ice volume reducing from 221.7017 ± 7.9618 km³ to 152.7429 ± 6.1747 km³. The most significant retreat occurred in glaciers smaller than 1 km². Additionally, glaciers on southern aspects retreated slower than the northern counterparts. The glaciers in the western YZRB witnessed a significantly greater shrinkage rate than those in the eastern section, with the most pronounced changes occurring in Aso Longbu River Basin. Furthermore, severe glacier mass deficits were observed from 2000 to 2019, averaging a loss rate of 0.57 ± 0.06 m w.e. a⁻¹. The continuous rise in air temperature has primarily induced a general widespread glacier change in the YZRB. However, diverse topography led to spatial variability in glacier changes with discrepancies as large as several times. The features of individual glaciers, such as glacier size, debris cover, and the development of ice-contact glacial lakes enhanced the local complexity of glacier change and elusive response behaviors to climate warming led by the different topographic conditions. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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18 pages, 3947 KiB

Open AccessArticle

Potential of the Bi-Static SAR Satellite Companion Mission Harmony for Land-Ice Observations

by Andreas Kääb, Jérémie Mouginot, Pau Prats-Iraola, Eric Rignot, Bernhard Rabus, Andreas Benedikter, Helmut Rott, Thomas Nagler, Björn Rommen and Paco Lopez-Dekker

Remote Sens. 2024, 16(16), 2918; https://doi.org/10.3390/rs16162918 - 9 Aug 2024

Viewed by 1294

Abstract

The EarthExplorer 10 mission Harmony by the European Space Agency ESA, scheduled for launch around 2029–2030, consists of two passive C-band synthetic-aperture-radar companion satellites flying in a flexible constellation with one Sentinel-1 radar satellite as an illuminator. Sentinel-1 will serve as transmitter and receiver of radar waves, and the two Harmonys will serve as bistatic receivers without the ability to transmit. During the first and last year of the 5-year mission, the two Harmony satellites will fly in a cross-track interferometric constellation, such as that known from TanDEM-X, about 350 km ahead or behind the assigned Sentinel-1. This constellation will provide 12-day repeat DEMs, among other regions, over most land-ice and permafrost areas. These repeat DEMs will be complemented by synchronous lateral terrain displacements from the well-established offset tracking method. In between the cross-track interferometry phases, one of the Harmony satellites will be moved to the opposite side of the Sentinel-1 to form a symmetric bistatic “stereo” constellation with ±~350 km along-track baseline. In this phase, the mission will provide opportunity for radar interferometry along three lines of sight, or up to six when combining ascending and descending acquisitions, enabling the measurement of three-dimensional surface motion, for instance sub- and emergence components of ice flow, or three-dimensional deformation of permafrost surfaces or slow landslides. Such measurements would, for the first time, be available for large areas and are anticipated to provide a number of novel insights into the dynamics and mass balance of a range of mass movement processes. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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23 pages, 30405 KiB

Open AccessArticle

North American Circum-Arctic Permafrost Degradation Observation Using Sentinel-1 InSAR Data

by Shaoyang Guan, Chao Wang, Yixian Tang, Lichuan Zou, Peichen Yu, Tianyang Li and Hong Zhang

Remote Sens. 2024, 16(15), 2809; https://doi.org/10.3390/rs16152809 - 31 Jul 2024

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Abstract

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake’s lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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24 pages, 22139 KiB

Open AccessArticle

Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data

by Anna Mangilli, Claude R. Duguay, Justin Murfitt, Thomas Moreau, Samira Amraoui, Jaya Sree Mugunthan, Pierre Thibaut and Craig Donlon

Remote Sens. 2024, 16(14), 2510; https://doi.org/10.3390/rs16142510 - 9 Jul 2024

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Abstract

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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49 pages, 36963 KiB

Open AccessArticle

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

by Ute C. Herzfeld, Lawrence J. Hessburg, Thomas M. Trantow and Adam N. Hayes

Remote Sens. 2024, 16(11), 1854; https://doi.org/10.3390/rs16111854 - 23 May 2024

Viewed by 1621

Abstract

The objectives of this paper are to investigate the trade-offs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100–200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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18 pages, 11498 KiB

Open AccessArticle

Glacier Changes from 1990 to 2022 in the Aksu River Basin, Western Tien Shan

by Pei Ren, Xiaohui Pan, Tie Liu, Yue Huang, Xi Chen, Xiaofei Wang, Ping Chen and Shamshodbek Akmalov

Remote Sens. 2024, 16(10), 1751; https://doi.org/10.3390/rs16101751 - 15 May 2024

Cited by 2 | Viewed by 1248

Abstract

Mountain glaciers are considered natural indicators of warming and a device for climatic change. In addition, it is also a solid reservoir of freshwater resources. Along with climate change, clarifying the dynamic changes of glacier in the Aksu River Basin (ARB) are important for hydrological processes. The study examined the variations in glacier area, elevation, and their reaction to climate change in the ARB between 1990 and 2022. The glacier melt on the runoff is explored from 2003 to 2020. This investigation utilized Landsat and Sentinal-2 images, ICESat, CryoSat, meteorological and hydrological data. The findings suggest that: (1) The glacier area in the ARB retreated by 309.40 km² (9.37%, 0.29%·a⁻¹) from 1990 to 2022. From 2003 to 2021, the ARB glacier surface elevation retreat rate of 0.38 ± 0.12 m·a⁻¹ (0.32 ± 0.10 m w.e.a⁻¹). Comparison with 2003–2009, the retreat rate is faster from 2010 to 2021. (2) From 1990 to 2022, the Toxkan and the Kumalak River Basin’s glacier area decreases between 61.28 km² (0.28%·a⁻¹) and 248.13 km² (0.30%·a⁻¹). Additionally, the rate of glacier surface elevation declined by −0.34 ± 0.11 m·a⁻¹, −0.42 ± 0.14 m·a⁻¹ from 2003 to 2021. (3) The mass balance sensitivities to cold season precipitation and ablation-phase accumulated temperatures are +0.27 ± 0.08 m w.e.a⁻¹(10%)⁻¹ and −0.33 ± 0.10 m w.e.a⁻¹ °C⁻¹, respectively. The mass loss is (962.55 ± 0.57) × 10⁶ m³ w.e.a⁻¹, (1087.50 ± 0.68) × 10⁶ m³ w.e.a⁻¹ during 2003–2009, 2010–2021 respectively. Warmer ablation-phase accumulated temperatures dominate glacier retreat in the ARB. (4) Glacier meltwater accounted for 34.57% and 41.56% of the Aksu River’s runoff during the ablation-phase of 2003–2009 and 2010–2020, respectively. The research has important implications for maintaining the stability of water resource systems based on glacier meltwater. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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25 pages, 12362 KiB

Open AccessArticle

Spatiotemporal Evolution of the Land Cover over Deception Island, Antarctica, Its Driving Mechanisms, and Its Impact on the Shortwave Albedo

by Javier F. Calleja, Rubén Muñiz, Jaime Otero, Francisco Navarro, Alejandro Corbea-Pérez, Carleen Reijmer, Miguel Ángel de Pablo and Susana Fernández

Remote Sens. 2024, 16(5), 915; https://doi.org/10.3390/rs16050915 - 5 Mar 2024

Viewed by 1125

Abstract

The aim of this work is to provide a full description of how air temperature and solar radiation induce changes in the land cover over an Antarctic site. We use shortwave broadband albedo (albedo integrated in the range 300–3000 nm) from a spaceborne sensor and from field surveys to calculate the monthly relative abundance of landscape units. Field albedo data were collected in January 2019 using a portable albedometer over seven landscape units: clean fresh snow; clean old snow; rugged landscape composed of dirty snow with disperse pyroclasts and rocky outcrops; dirty snow; stripes of bare soil and snow; shallow snow with small bare soil patches; and bare soil. The MODIS MCD43A3 daily albedo products were downloaded using the Google Earth Engine API from the 2000–2001 season to the 2020–2021 season. Each landscape unit was characterized by an albedo normal distribution. The monthly relative abundances of the landscape units were calculated by fitting a linear combination of the normal distributions to a histogram of the MODIS monthly mean albedo. The monthly relative abundance of the landscape unit consisting of rugged landscape composed of dirty snow with dispersed clasts and small rocky outcrops exhibits a high positive linear correlation with the monthly mean albedo (R² = 0.87) and a high negative linear correlation with the monthly mean air temperature (R² = 0.69). The increase in the solar radiation energy flux from September to December coincides with the decrease in the relative abundance of the landscape unit composed of dirty snow with dispersed clasts and small rocky outcrops. We propose a mechanism to describe the evolution of the landscape: uncovered pyroclasts act as melting centers favoring the melting of surrounding snow. Ash does not play a decisive role in the melting of the snow. The results also explain the observed decrease in the thaw depth of the permafrost on the island in the period 2006–2014, resulting from an increase in the snow cover over the whole island. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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17 pages, 3201 KiB

Open AccessTechnical Note

Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape

by Franklin B. Sullivan, Adam G. Hunsaker, Michael W. Palace and Jennifer M. Jacobs

Remote Sens. 2023, 15(21), 5091; https://doi.org/10.3390/rs15215091 - 24 Oct 2023

Viewed by 1266

Abstract

Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s⁻¹, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s⁻¹ and 4 m s⁻¹ and snow-on flight speeds of 6 m s⁻¹ over open fields and between 2 and 4 m s⁻¹ over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover. Full article

(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))

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