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Earth Observation of Glacier and Snow Cover Mapping in Cold Regions

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 5727

Special Issue Editors

Prof. Dr. Qing Cheng

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing information processing and applications; quality improvement of remote sensing images; data fusion; regional and global environmental changes
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Huang

E-Mail Website
Guest Editor
School of Geographical Sciences, East China Normal University, Shanghai 200062, China
Interests: remote sensing spatiotemporal modeling and analysis; remote sensing of cryosphere; global climate and environmental change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Glaciers and snow cover are core components of the Earth's cryosphere and key indicators for monitoring climate change, especially in cold regions. Their unique high reflectivity can reflect solar radiation, thereby regulating the energy balance of the surface and significantly affecting global and regional climate patterns. In addition, ice and snow regions are important freshwater resource reservoirs and play a crucial role in global water supply, especially for areas that mainly rely on meltwater supply. However, due to global warming, ice and snow coverage is rapidly diminishing, potentially leading to sea-level rise and severe impacts on regional water supply. The reductions in glacier size and snow cover may also increase the instability of the climate system, leading to more frequent and intense extreme climate events. With the rapid development of remote sensing technology and continuous progress in artificial intelligence and algorithms, we can now monitor and analyse changes in ice and snow more accurately and efficiently.

This Special aims to showcase recent research and progress in the application of Earth observation technologies for mapping and monitoring glaciers and snow cover in cold regions. Topics may cover anything from the basic estimation of glacial and snow variables to more comprehensive aims and scales. Therefore, multisource data fusion, multiscale methods, or studies focused on cryosphere monitoring are welcome.

Articles may address, but are not limited, to the following topics:

  • Dynamic remote sensing monitoring of glaciers, snow cover and ice sheets;
  • Analysis of spatiotemporal changes in glaciers, snow cover and ice sheets;
  • Research on the relationship between the hydrological cycle and ice and snow;
  • Extreme climate monitoring;
  • Cryosphere;
  • Differences in ice and snow between the North and South Poles;
  • The impacts of climate change on glaciers, snow cover and ice sheets;
  • Applications of machine learning and deep learning in the cryosphere.

Prof. Dr. Qing Cheng
Dr. Yan Huang
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

  • glacier
  • snow cover
  • ice sheet
  • remote sensing
  • cryosphere
  • climate change

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Published Papers (5 papers)

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Research

19 pages, 12502 KiB  
Article
Quantifying Spatiotemporal Changes in Supraglacial Debris Cover in Eastern Pamir from 1994 to 2024 Based on the Google Earth Engine
by Hehe Liu, Zhen Zhang, Shiyin Liu, Fuming Xie, Jing Ding, Guolong Li and Haoran Su
Remote Sens. 2025, 17(1), 144; https://doi.org/10.3390/rs17010144 - 3 Jan 2025
Cited by 1 | Viewed by 812
Abstract
Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the [...] Read more.
Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the overall slightly positive mass balance or stable state of eastern Pamir glaciers has been referred to as the “Pamir-Karakoram anomaly”. It is important to note that spatial heterogeneity in glacier change has drawn widespread research attention. However, research on the spatiotemporal changes in the debris cover in this region is completely nonexistent, which has led to an inadequate understanding of debris-covered glacier variations. To address this research gap, this study employed Landsat remote sensing images within the Google Earth Engine platform, leveraging the Random Forest algorithm to classify the supraglacial debris cover. The classification algorithm integrates spectral features from Landsat images and derived indices (NDVI, NDSI, NDWI, and BAND RATIO), supplemented by auxiliary factors such as slope and aspect. By extracting the supraglacial debris cover from 1994 to 2024, this study systematically analyzed the spatiotemporal variations and investigated the underlying drivers of debris cover changes from the perspective of mass conservation. By 2024, the area of supraglacial debris in eastern Pamir reached 258.08 ± 20.65 km2, accounting for 18.5 ± 1.55% of the total glacier area. It was observed that the Kungey Mountain region demonstrated the largest debris cover rate. Between 1994 and 2024, while the total glacier area decreased by −2.57 ± 0.70%, the debris-covered areas expanded upward at a rate of +1.64 ± 0.10% yr−1. The expansion of debris cover is driven by several factors in the context of global warming. The rising temperature resulted in permafrost degradation, slope destabilization, and intensified weathering on supply slopes, thereby augmenting the debris supply. Additionally, the steep supply slope in the study area facilitates the rapid deposition of collapsed debris onto glacier surfaces, with frequent avalanche events accelerating the mobilization of rock fragments. Full article
(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)
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18 pages, 11697 KiB  
Article
Spatiotemporal Variation in Aerosol Optical Depth and Its Potential Effects on Snowmelt in High Mountain Asia from 2004 to 2023
by Lichen Yin, Xin Wang, Wentao Du, Jizu Chen, Youyan Jiang, Weijun Sun, Chengde Yang, Bowen Li, Xingyu Xue and Changsheng Lu
Remote Sens. 2024, 16(23), 4410; https://doi.org/10.3390/rs16234410 - 25 Nov 2024
Viewed by 925
Abstract
Light-absorbing particles, which are vital components of aerosols, can cause significant snow albedo darkening and accelerate melting. However, restricted by the poor quality of remote sensing-based aerosol products in High Mountain Asia (HMA), previous studies have seldom reported the long-term pattern of aerosols. [...] Read more.
Light-absorbing particles, which are vital components of aerosols, can cause significant snow albedo darkening and accelerate melting. However, restricted by the poor quality of remote sensing-based aerosol products in High Mountain Asia (HMA), previous studies have seldom reported the long-term pattern of aerosols. In this study, we analyzed the spatial and temporal distribution characteristics of AOD in HMA and surrounding areas using Moderate Resolution Imaging Spectroradiometer and Ozone Monitoring Instrument data from 2004 to 2023. The Mann-Kendall test was applied to analyze the temporal trend and abrupt changes in AOD, while Rotated Empirical Orthogonal Function was used to identify subregions and investigate spatiotemporal variations. Moreover, random forest and XGBoost-Shap models were employed to quantify the contributions of the aerosols to changes in snow albedo and melting. The results indicate that the annual (monthly) average highest and lowest AOD occurred in 2021 (April) and 2022 (September) between 2004 and 2023, respectively. The AOD first increased and then decreased during our study period and an abrupt decline was detected in 2013. The REOF model revealed three regions in HMA (northern, southwestern, and southeastern parts) with strong variations in AOD load, which are strongly correlated with atmospheric circulation and monsoon driving. Specifically, REOF1, REOF2, and REOF3 are primarily associated with frequent dust events during springtime atmospheric circulation and anthropogenic emission transport during the monsoon season. Aerosol types were divided into four types, BC aerosol, DUST aerosol, MIX aerosol, and clean conditions, whose proportions were 16.7%, 16.1%, 6.6%, and 60.6%, respectively. The clean conditions constituted the main aerosol type in the region. The AOD notably decreased snow albedo (17.8%) and increased snowmelt (9.0%); we highlight the contribution of AOD to the intensification of snowmelt. These results could provide guidance for further studies on the relationship between snowmelt and AOD. Full article
(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)
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19 pages, 11478 KiB  
Article
A Comparative Study of Methods for Estimating the Thickness of Glacial Debris: A Case Study of the Koxkar Glacier in the Tian Shan Mountains
by Jun Liu, Yan Qin, Haidong Han, Qiudong Zhao and Yongqiang Liu
Remote Sens. 2024, 16(23), 4356; https://doi.org/10.3390/rs16234356 - 22 Nov 2024
Viewed by 880
Abstract
The local or overall mass balance of a glacier is significantly influenced by the spatial heterogeneity of its overlying debris thickness. Accurately estimating the debris thickness of glaciers is essential for understanding their hydrological processes and the impact of climate change. This study [...] Read more.
The local or overall mass balance of a glacier is significantly influenced by the spatial heterogeneity of its overlying debris thickness. Accurately estimating the debris thickness of glaciers is essential for understanding their hydrological processes and the impact of climate change. This study focuses on the Koxkar Glacier in the Tian Shan Mountains, using debris thickness data to compare the accuracy of three commonly used approaches for estimating the spatial distribution of debris thickness. The three measurement approaches include two empirical relationships between the land surface temperature (LST) and debris thickness approaches, empirical relationship approach 1 and empirical relationship approach 2, and the energy balance of debris approach. The analysis also explores the potential influence of topographic factors on the debris distribution. By incorporating temperature data from the debris profiles, this study examines the applicability of each approach and identifies areas for possible improvement. The results indicate that (1) all three debris thickness estimation approaches effectively capture the distribution characteristics of glacial debris, although empirical relationship approach 2 outperforms the others in describing the spatial patterns; (2) the accuracy of each approach varies depending on the debris thickness, with the energy balance of debris approach being most accurate for debris less than 50 cm thick, while empirical relationship approach 1 performs better for debris thicker than 50 cm and empirical relationship approach 2 demonstrates the highest overall accuracy; and (3) topographic factors, particularly the elevation, significantly influence the accuracy of debris thickness estimates. Furthermore, the empirical relationships between the LST and debris thickness require field data and focus solely on the surface temperature, neglecting other influencing factors. The energy balance of debris approach is constrained by its linear assumption of the temperature profile, which is only valid within a specific range of debris thickness; beyond this range, it significantly underestimates the values. These findings provide evidence-based support for improving remote-sensing methods for debris thickness estimation. Full article
(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)
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21 pages, 7709 KiB  
Article
Impacts of GCP Distributions on UAV-PPK Photogrammetry at Sermeq Avannarleq Glacier, Greenland
by Haiyan Zhao, Gang Li, Zhuoqi Chen, Shuhang Zhang, Baogang Zhang and Xiao Cheng
Remote Sens. 2024, 16(21), 3934; https://doi.org/10.3390/rs16213934 - 22 Oct 2024
Cited by 2 | Viewed by 1246
Abstract
Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured [...] Read more.
Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured at Sermeq Avannarleq glacier in western Greenland from 4 August 2021 to the 6th, covering approximately 85 km2, with the furthest distance being 13.22 km away from the coastline. Benefited by the meandering coastline, 11 roving stations roughly uniformly distributed on bare rock were surveyed with the RTK technique. PPK-geotagged images were processed in Agisoft Metashape Professional to derive the DSMs, utilizing eight different configurations of GCP distributions that gradually extended longitudinally (along the glacier flow direction) to the upper part of the glacier. The accuracy of DSMs was evaluated by referring to the validation points (VPs) that were not employed in the Bundle Block Adjustment (BBA). The results indicated that the RMSE values of the easting, northing, and height of the reconstruction model georeferenced by only PPK geotagging (no GCPs applied) were 0.038 m, 0.031 m, and 0.146 m, respectively. Applying four GCPs located at one side of the region but with both longitudinal and lateral distribution improved the RMSE values in easting, northing, and vertical to 0.037 m, 0.031 m, and 0.081 m, respectively, and these values were stable even when distributing four GCPs evenly or when increasing the number of GCPs to eleven. Moreover, the cross-validation with ICESat-2 and ArcticDEM performed only at an off-glacier region also suggested that vertical accuracy shows significant improvements for every configuration of GCPs compared to the reconstruction model optimized only by PPK, but such improvements were not obvious if the number of GCPs exceeded four. Moreover, no elevation ramps appeared in the UAV DSM, even for the GCP configuration with only two GCPs distributed at the terminus. Therefore, combining PPK with only a few GCPs but distributing in both directions of the surveying region can offer a viable solution for obtaining glacier DSMs at the coastline with decimeter-level accuracy. Full article
(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)
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17 pages, 13809 KiB  
Article
Assessing the Response of the Net Primary Productivity to Snow Phenology Changes in the Tibetan Plateau: Trends and Environmental Drivers
by Jiming Liu, Lu Shen, Zhaoming Chen, Jingwen Ni and Yan Huang
Remote Sens. 2024, 16(19), 3566; https://doi.org/10.3390/rs16193566 - 25 Sep 2024
Viewed by 972
Abstract
Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology [...] Read more.
Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP. Full article
(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)
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