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Application of Remote Sensing in Snow and Ice Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 29 August 2025 | Viewed by 233

Special Issue Editors


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Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing Society of Jilin Province, Changchun, China
Interests: lake ice; cryosphere remote sensing; agricultural remote sensing

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Guest Editor
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing inversion of snow and ice; snow and ice pollution and climate change
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Guest Editor
College of Geography and Environment Science, Northwest Normal University, Lanzhou, China
Interests: cryosphere remote sensing; remote sensing estimation of mountain energy balance

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Guest Editor
Faculty of Biologal and Environmental Science, University of Helsinki, Helsinki, Finland
Interests: remote sensing of environment; lake ice

Special Issue Information

Dear Colleagues,

Snow and ice are the most active environmental factors in the cryosphere, and widely distributed in the Northern Hemisphere. Due to their high reflectance, low thermal conductivity, and the snowmelt water effect, snow and ice play vital roles in the global energy balance, hydrological and ecological models, and climate change. They influence the global water cycle, surface energy balance, and ecosystem carbon cycle. Accelerating climate change alters their distribution and characteristics, affecting energy and nutrient exchange and driving physical, chemical and ecological processes. On this note, remote sensing provides a powerful tool for regional and large-scale monitoring, enabling the exploration of interactions between snow, ice cover and climate change.

Satellite remote sensing with large-scale synchronous observation has become an important tool for monitoring snow and ice changes. Since the 1960s, many optical satellites have been used for snow and ice research because they have had low reflectivity in the shortwave infrared band (1.6μm), making it easy to distinguish them from clouds and other land covers. Both polarorbiting satellites and geostationary satellites have released a variety of high-quality snow cover products for free for global users, such as AVHRR, MODIS and Meteosat/MSG. Microwave remote sensing is also an important data source for monitoring snow and ice changes. It not only has the characteristics of passing through clouds and fog, but also can effectively invert various snow and ice parameters such as snow density and depth. In recent years, various spaceborne altimeters, especially laser lidar (LiDAR), have also been widely used in the study of snow and ice.

This Special Issue aims to present recent progress in remote sensing applications for snow and ice cover. It provides a forum for researchers to share their findings, methodologies, and insight. We welcome contributions on a variety of topics, including the following:

  • Remote sensing algorithms for monitoring the key parameters of snow and ice cover using multi-sensors and multi-source data.
  • Spatial and temporal changes in snow and ice cover from regional to global scales.
  • Field measurements or experiences of snow and ice combining with remote sensing.
  • Interdisciplinary research and perspectives on snow and ice cover combining remote sensing, meteorology, hydrology and ecology.
  • Assessment of snow and ice applications related to human activities, such as tourism resources and nature disasters.

Review or surveys of recent applications, techniques and advancements in snow and ice remote sensing.

Dr. Qian Yang
Prof. Dr. Xiaohua Hao
Prof. Dr. Yanli Zhang
Dr. Yuwen Pang
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

  • snow cover
  • lake ice
  • ice dynamics
  • sea ice
  • climate change
  • multi-sensor and multi-source data
  • change detection
  • remote sensing applications
  • resources assessment

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Published Papers (1 paper)

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Research

23 pages, 1741 KiB  
Article
ASTER GDEM Correction Based on Stacked Ensemble Learning and ICEsat-2/ATL08: A Case Study from the Qilian Mountains
by Qi Wei, Yanli Zhang, Yalong Ma, Ruirui Yang and Kairui Lei
Remote Sens. 2025, 17(11), 1839; https://doi.org/10.3390/rs17111839 - 24 May 2025
Abstract
ASTER GDEM provides the fundamental data for remote sensing identification of snow cover in mountainous areas. Due to its elevation accuracy being easily affected by optical stereo images and local terrain, many studies have utilized machine learning (ML) models for correction. However, most [...] Read more.
ASTER GDEM provides the fundamental data for remote sensing identification of snow cover in mountainous areas. Due to its elevation accuracy being easily affected by optical stereo images and local terrain, many studies have utilized machine learning (ML) models for correction. However, most correction methods rely on a single ML model, which limits the improvement of DEM accuracy. Stacked ensemble learning (SEL) is a newly developed method of improving model performance by combining multiple ML models. This study proposes a DEM correction method based on SEL and ICESatand affiliations. -2/ATL08 products. Taking the Babao River Basin in Qilian Mountains as the study area, five ML models with good DEM correction effects (XGBoost, AdaBoost, LightGBM, BPNN, and CatBoost) were selected and trained using land cover and various terrain factors to obtain DEM errors, respectively. Then, the SEL algorithm was used to integrate the DEM errors of the five ML models and correct GDEM. Using 740 CORS measurements and 48,000 ATL08 points for accuracy validation, the results showed that the SEL achieved higher DEM accuracy than any single ML model. The root mean square error (RMSE) of the corrected GDEM decreased from 7.15 m to 4.13 m, while the mean absolute error (MAE) and mean bias error (MBE) values both decreased about by 38%. Furthermore, unmanned aerial vehicle (UAV) DEM data from five sample areas were selected for profile analysis, and it was found that the corrected GDEM was closer to the real surface. Further analysis revealed that the influence of slope, aspect, and land cover types on corrected DEM was weakened, with the most significant improvement in DEM accuracy observed in areas with slope ≥5°, north orientation, and bare land. This study can provide high-precision DEM scientific data for quantitative remote sensing, flood prediction, and other research. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Snow and Ice Monitoring)
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