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Applications of Remote Sensing in the Monitoring of the Mountain Cryosphere

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

Deadline for manuscript submissions: 25 March 2025 | Viewed by 3069

Special Issue Editors

Dr. Pedro Fidel Espín-López

E-Mail Website
Guest Editor
Centre Tecnologic de Telecomunicacions de Catalunya, Barcelona, Spain
Interests: radar; snow cover; electromagnetics; SAR
Dr. Massimiliano Barbolini

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering, Università degli Studi di Pavia, Pavia, Italy
Interests: snow cover; monitoring; avalanche forecast; risk management; hydrology
Dr. Andreas J. Dietz

E-Mail Website
Guest Editor
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany
Interests: snow; multispectral data; cryosphere; hyperspectral data; impact of climate change on the cryosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In mountain environments, the cryosphere (comprising snow, river and lake ice, glaciers, and frozen ground) plays a central role in the climate system, affecting the surface energy budget, the water cycle, and the safety of the populations living there.

The implications of a reduction in/alteration of these elements are profound. Snow serves as a crucial water storage reservoir, releasing water slowly during the spring and summer months when it is most needed for various ecological processes and human activities, such as agriculture, hydropower generation, and tourism. Ice acts as a protective cover over the Earth and our oceans, reflecting incoming solar radiation back into space and, in turn, keeping the planet cooler. Permafrost plays an essential role in the high mountain ecosystem by supporting the structural stability of the rocks found there, making the ground watertight and maintaining the network of wetlands and lakes that provide habitats for animals and plants, not to mention its important function as a carbon sink.

Understanding the dynamics of the different cryosphere components accurately is of the utmost importance for effective water resource and risk management in mountain areas and the development of adaptation strategies which can mitigate the impacts of climate change. Therefore, there is an urgent need to explore innovative approaches that can provide reliable and timely information about the characteristics of the cryosphere across the world.

This Special Issue calls for papers dealing with remote sensing applications focused on monitoring the cryosphere in mountain environments.

This Special Issue invites both research papers and review articles on recent advances in microwave, hyperspectral, and optical remote sensing with either ground-based, airborne, UAVs, or satellite systems, covering topics spanning from the design of new instrumentation to the development of algorithms which push the boundaries of what is considered the state of the art.

Suggested themes and article types for submissions.

Possible topics include:

  • Microwave remote sensing of snow/glaciers
  • Ground-based/airborne/satellite systems for cryosphere monitoring
  • GNSS-based systems
  • Radiometric systems
  • Cryosphere monitoring with UAVs
  • Modelling of cryosphere processes
  • Hyperspectral remote sensing of snow (e.g. for quantifying pollution)

Dr. Pedro Fidel Espín-López
Dr. Massimiliano Barbolini
Dr. Andreas J. Dietz
Guest Editors

Dr. Martina Lodigiani
Guest Editor Assistant
Department of Electronic, Computer and Electical Engineering, University of Pavia, 27100 Pavia, Italy
Email: [email protected]
Webpage: https://microwave.unipv.it/lodigiani-staff/
Interests: snow monitoring; glacier monitoring; radar; proximal sensing; electromagnetic modeling

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

  • snow cover
  • glaciers
  • remote sensing
  • permafrost
  • snow avalanches
  • climate change
  • water management
  • hyperspectral
  • black carbon

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Published Papers (3 papers)

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19 pages, 4666 KiB  
Article
Quantifying Qiyi Glacier Surface Dirtiness Using UAV and Sentinel-2 Imagery
by Jiangtao Chen, Ninglian Wang, Yuwei Wu, Anan Chen, Chenlie Shi, Mingjie Zhao and Longjiang Xie
Remote Sens. 2024, 16(17), 3351; https://doi.org/10.3390/rs16173351 - 9 Sep 2024
Viewed by 754
Abstract
The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of [...] Read more.
The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R2 ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in the Monitoring of the Mountain Cryosphere)
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Other

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15 pages, 1077 KiB  
Technical Note
Quantifying Annual Glacier Mass Change and Its Influence on the Runoff of the Tuotuo River
by Lin Liu, Xueyu Zhang and Zhimin Zhang
Remote Sens. 2024, 16(20), 3898; https://doi.org/10.3390/rs16203898 - 20 Oct 2024
Viewed by 728
Abstract
Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this [...] Read more.
Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this study, we employed an albedo-based model with a daily land surface albedo dataset to derive the annual glacier mass balance over the Tuotuo River Basin (TRB). During 2000–2022, an annual glacier mass balance range of −0.89 ± 0.08 to 0.11 ± 0.11 m w.e. was estimated. By comparing with river runoff records from the hydrometric station, the contribution of glacier mass change to river runoff was calculated to be 0.00–31.14% for the studied period, with a mean value of 9.97%. Moreover, we found that the mean contribution in drought years is 20.07%, which is approximately five times that in wet years (4.30%) and twice that in average years (9.49%). Therefore, our results verify that mountain glaciers act as a significant buffer against drought in the TRB, at least during the 2000–2022 period. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in the Monitoring of the Mountain Cryosphere)
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15 pages, 6153 KiB  
Technical Note
Elevation Changes of A’nyemaqen Snow Mountain Revealed with Satellite Remote Sensing
by Huai Lin, Yuande Yang, Leiyu Li, Qihua Wang and Minyi Guo
Remote Sens. 2024, 16(13), 2446; https://doi.org/10.3390/rs16132446 - 3 Jul 2024
Viewed by 1084
Abstract
A’nyemaqen Snow Mountain (ASM) is the largest glacier area in the Yellow River source area and has been experiencing significant ablation in recent years. To investigate spatial–temporal elevation changes in ASM, a 21–year Digital Elevation Model (DEM) time series was obtained using the [...] Read more.
A’nyemaqen Snow Mountain (ASM) is the largest glacier area in the Yellow River source area and has been experiencing significant ablation in recent years. To investigate spatial–temporal elevation changes in ASM, a 21–year Digital Elevation Model (DEM) time series was obtained using the MicMac ASTER (MMASTER) algorithm and ASTER L1A V003 data. It covers the period from January 2002 to January 2023. The mean elevation of ASM decreased by −7.88 ± 3.37 m during this period, with highly spatial variation. The elevation decrease occurred mainly in the lower elevations and opposite in the higher elevations. The corresponding elevation decrease was −12.99 ± 11.29 and −4.45 ± 11.36 m at the southern Yehelong Glacier and the northern Weigeledangxiong Glacier, respectively. Moreover, there exists a temporal variation in ASM. The maximum elevation was observed in February for both ASM and the southern Yehelong Glacier but March for Weigeledangxiong Glacier, with about 1 month lagged. With the elevation time series and climate data from ERA5 datasets, we applied the random forest technique and found that the temperature is the main factor to elevation change in ASM. Furthermore, the response of elevation changes to temperature appeared with a lag and varied with the location. Based on the elevation time series, the ARIMA model was further used to forecast the elevation changes in the next 5 years. All regions will experience the elevation decrease, with a mean decline −1.74 ± 0.39 m and a corresponding rate −0.35 ± 0.08 m/a in ASM. This is similar to that of −0.38 ± 0.16 m/a between 2002 and 2003, showing its stability in the near future. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in the Monitoring of the Mountain Cryosphere)
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