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Study on Cryospheric Sciences Using Remote Sensing Technology

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8425

Special Issue Editors


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Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
Interests: climate Science; geodynamics; geophysics

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Guest Editor
Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, China
Interests: remote sensing data processing; remote sensing of glacier; sea ice and lake ice; hydrological remote sensing; climate change

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Guest Editor
Department of Geological Engineering, Montana Technological University, Butte, MT 59701, USA
Interests: remote sensing theory; applied geophysics; instrumentation; algorithm development; image processing; applications in hydrology; ecology; snow and ice; environmental monitoring; geophysical mineral exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cryospheric science is the interdisciplinary study of permafrost, snow, ice, ecosystems and their interactions. The cryosphere is an Earth layer that is severely affected by climate change. It is prominently manifested in the severe retreat of glaciers, the rapid reduction in Arctic sea ice extent and snow in the Northern Hemisphere, the thickening of active layers of permafrost, and cryospheric environmental changes. At the same time, the cryosphere has a significant impact on natural and socio-economic systems through mass and energy exchanges with atmosphere and hydrosphere. The harsh environment of the cryosphere makes it tremendously challenging to conduct field observations, which makes airborne technologies, including unmanned aircraft systems and satellite-based remote sensing, perfect for cryospheric studies. Recent developments in remote sensing technology have led to increasingly precise data in terms of accuracy and improved spatial and temporal resolution, providing us unprecedent opportunities to better observe and understand the spatial and temporal characteristics in cryospheric components and parameters.

This Special Issue aims to collect and present the latest developments and advance in remote sensing technology and applications in cryospheric science. Topics may cover the role of remote sensing technology in regional- or global-scale studies of sea ice, lake ice, river ice, snow cover, glaciers, ice caps, ice sheets, frozen ground, and cryospheric ecosystems, in particular the development of new technologies and algorithms or applications of high-quality data from new platforms and sensors.

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

  • Physical properties of components of the cryosphere related to remote sensing;
  • Snow and ice: properties, processes, hazards;
  • Remote sensing of ice sheets, ice shelves, and glaciers;
  • Remote sensing of snow and ice over sea, lake, and river;
  • Remote sensing of permafrost and frozen ground;
  • Remote sensing missions and the cryosphere;
  • Remote sensing of cryospheric ecosystems.

Prof. Dr. Tonie Van Dam
Prof. Dr. Chang-Qing Ke
Prof. Dr. Xiaobing Zhou
Dr. Zheng Duan
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
  • remote sensing
  • glaciers
  • glaciers
  • sea ice
  • lake and river ice
  • ice sheets
  • permafrost

Published Papers (4 papers)

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Research

18 pages, 2371 KiB  
Article
Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic
by Wenyu Zhao, Cuicui Mu, Xiaodong Wu, Xinyue Zhong, Xiaoqing Peng, Yijing Liu, Yanhua Sun, Benben Liang and Tingjun Zhang
Remote Sens. 2023, 15(16), 3976; https://doi.org/10.3390/rs15163976 - 10 Aug 2023
Viewed by 1094
Abstract
The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized [...] Read more.
The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes. Full article
(This article belongs to the Special Issue Study on Cryospheric Sciences Using Remote Sensing Technology)
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20 pages, 4461 KiB  
Article
Spatio-Temporal Evolution of Glacial Lakes in the Tibetan Plateau over the Past 30 Years
by Xiangyang Dou, Xuanmei Fan, Xin Wang, Ali P. Yunus, Junlin Xiong, Ran Tang, Marco Lovati, Cees van Westen and Qiang Xu
Remote Sens. 2023, 15(2), 416; https://doi.org/10.3390/rs15020416 - 10 Jan 2023
Cited by 18 | Viewed by 2831
Abstract
As the Third Pole of the Earth and the Water Tower of Asia, the Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region but concurrently pose severe [...] Read more.
As the Third Pole of the Earth and the Water Tower of Asia, the Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region but concurrently pose severe threats to the valley population by means of sudden glacial lake outbursts and consequent floods (GLOFs). The lack of high-resolution multi-temporal inventory of glacial lakes in TP hampers a better understanding and prediction of the future trend and risk of glacial lakes. Here, we created a multi-temporal inventory of glacial lakes in TP using a 30-year record of 42,833 satellite images (1990–2019), and we discussed their characteristics and spatio-temporal evolution over the years. Results showed that their number and area had increased by 3285 and 258.82 km2 in the last 3 decades, respectively. We noticed that different regions of the TP exhibited varying change rates in glacial lake size; most regions show a trend of expansion and increase in glacial lakes, while some regions show a trend of decreasing such as the western Pamir and the eastern Hindu Kush. The mapping uncertainty is about 17.5%, which is lower than other available datasets, thus making our inventory reliable for the spatio-temporal evolution analysis of glacial lakes in the TP. Our lake inventory data are publicly published, it can help to study climate change–glacier–glacial lake–GLOF interactions in the Third Pole and serve as input to various hydro-climatic studies. Full article
(This article belongs to the Special Issue Study on Cryospheric Sciences Using Remote Sensing Technology)
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18 pages, 8348 KiB  
Article
Continuous Karakoram Glacier Anomaly and Its Response to Climate Change during 2000–2021
by Drolma Lhakpa, Yubin Fan and Yu Cai
Remote Sens. 2022, 14(24), 6281; https://doi.org/10.3390/rs14246281 - 11 Dec 2022
Cited by 9 | Viewed by 1944
Abstract
Glacier mass balance is one of the most direct indicators reflecting corresponding climate change. In the context of global warming, most glaciers are melting and receding, which can have significant impacts on ecology, climate, and water resources. Thus, it is important to study [...] Read more.
Glacier mass balance is one of the most direct indicators reflecting corresponding climate change. In the context of global warming, most glaciers are melting and receding, which can have significant impacts on ecology, climate, and water resources. Thus, it is important to study glacier mass change, in order to assess and project its variations from past to future. Here, the Karakoram, one of the most concentrated glacierized areas in High-Mountain Asia (HMA), was selected as the study area. This study utilized SRTM-C DEM and ICESat-2 to investigate glacier mass change in the Karakoram, and its response to climatic and topographical factors during 2000–2021. The results of the data investigation showed that, overall, the “Karakoram Anomaly” still exists, with an annual averaged mass change rate of 0.02 ± 0.09 m w.e.yr-1. In different sub-regions, it was found that the western and central Karakoram glaciers gained ice mass, while the eastern Karakoram glaciers lost ice mass in the past two decades. In addition, it was discovered that the increasing precipitation trend is leading to mass gains in the western and central Karakoram glaciers, whereas increasing temperature is causing ice mass loss in the eastern Karakoram glacier. Generally, decreasing net shortwave radiation and increasing cloud cover in the Karakoram restricts ice mass loss, while topographical shading and debris cover also have dominant impacts on glacier mass change. Full article
(This article belongs to the Special Issue Study on Cryospheric Sciences Using Remote Sensing Technology)
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25 pages, 10379 KiB  
Article
Evaluation of Arctic Sea Ice Drift Products Based on FY-3, HY-2, AMSR2, and SSMIS Radiometer Data
by Hailan Fang, Xi Zhang, Lijian Shi, Meng Bao, Genwang Liu, Chenghui Cao and Jie Zhang
Remote Sens. 2022, 14(20), 5161; https://doi.org/10.3390/rs14205161 - 15 Oct 2022
Cited by 1 | Viewed by 1619
Abstract
Different radiometer sensors have different frequencies, spatial resolutions, and time resolutions, which lead to inconsistencies in ice drift products retrieved by radiometer sensors. Based on the continuous maximum cross-correlation method, in this paper, we used China’s FY-3 and HY-2 satellite radiometer data to [...] Read more.
Different radiometer sensors have different frequencies, spatial resolutions, and time resolutions, which lead to inconsistencies in ice drift products retrieved by radiometer sensors. Based on the continuous maximum cross-correlation method, in this paper, we used China’s FY-3 and HY-2 satellite radiometer data to generate sea ice drift products; we further evaluated the consistency between them and sea ice drift products retrieved from AMSR2 and SSMIS satellite radiometer data, which could help in future retrieval accuracies of more radiometer sea ice drift products. The results show that ice drift products with good reliability can be obtained by retrievals using 37 and 89 GHz channels of FY-3 and HY-2 radiometer bright temperature data. Compared with the buoy data, the root mean square errors (RMSEs) of the 37 GHz HY-2 sea ice drift product (at an interval of 6 days) were 1.40 cm/s and 7.31° for speed and direction, respectively, and the relative errors (REs) were 5.78% and 6.44%, respectively. The RMSEs of the 37 GHz FY-3 sea ice drift product were 0.77 cm/s and 6.49° for speed and direction, respectively, and the REs were 4.38% and 9.23%, respectively. Moreover, comparisons between sea ice drift vectors derived from AMSR2 and SSMIS satellites showed good quantitative agreement. Full article
(This article belongs to the Special Issue Study on Cryospheric Sciences Using Remote Sensing Technology)
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