Zhang et al. (2026) Remote sensing of the global cryosphere: Status, processes, and trends

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-01-08
• Authors: Guoqing Zhang, Hongjie Xie Xie, Alfonso Fernández, Christophe Kinnard, Stef Lhermitte
• DOI: 10.1016/j.rse.2025.115220

Research Groups

This editorial synthesizes contributions from multiple research groups, labs, and departments whose work is featured in the "Remote sensing of the global cryosphere: status, processes, and trends" special issue of Remote Sensing of Environment.

Short Summary

This editorial summarizes 23 studies published between 2023 and 2025, showcasing how multi-sensor satellite observations, high-resolution digital elevation models, and deep learning are revolutionizing the monitoring of glaciers, snow, glacial lakes, permafrost, sea ice, and ice shelves across Earth's three poles.

Objective

• To synthesize and highlight recent advancements in remote sensing, image processing, and deep learning techniques for monitoring the status, processes, and trends of the global cryosphere, as presented in a special issue of 23 studies.

Study Configuration

• Spatial Scale: Global, with a focus on the Arctic (including Greenland), Antarctica, and High Mountain Asia (the Third Pole).
• Temporal Scale: The special issue covers studies published between 2023 and 2025, reflecting recent advancements and trends in cryospheric change over various temporal scales.

Methodology and Data

• Models used: Cutting-edge deep learning techniques, image processing algorithms, and physics-informed modeling (identified as future directions).
• Data sources: Multi-sensor satellite observations, high-resolution digital elevation models (DEMs), multisource DEMs, optical imagery, thermal imagery, passive microwave imageries, RADAR, LiDAR, Gravity Recovery and Climate Experiment (GRACE) observations, GRACE Follow-On (FO), and Synthetic Aperture Radar (SAR).

Main Results

• Multi-sensor satellite observations, high-resolution DEMs, and deep learning techniques are revolutionizing the monitoring of glaciers, snow, glacial lakes, permafrost, sea ice, and ice shelves across the Arctic, Antarctica, and High Mountain Asia.
• These integrated datasets and methodologies enable the quantification of glacier mass balance, mapping of glacial lakes, assessment of permafrost thermal conditions, classification of sea-ice types, and detection of icebergs.
• The special issue highlights advancements across five thematic areas: changes in cryospheric components, hydrological and hazard impacts, limitations in data acquisition, mechanisms shaping cryosphere evolution, and impacts on local populations and ecosystems.

Contributions

• Provides a comprehensive synthesis of the latest advancements (2023-2025) in remote sensing and deep learning applications for cryospheric science.
• Showcases the transformative impact of multi-sensor data and advanced analytical techniques on monitoring the global cryosphere across the three poles.
• Organizes and summarizes diverse datasets and methodologies employed in recent cryospheric research.
• Outlines critical future directions for cryospheric remote sensing, including multi-sensor data fusion, physics-informed modeling, and AI-driven approaches.

Funding

No specific funding projects or programs are listed for this editorial article.

Citation

@article{Zhang2026Remote,
  author = {Zhang, Guoqing and Xie, Hongjie Xie and Fernández, Alfonso and Kinnard, Christophe and Lhermitte, Stef},
  title = {Remote sensing of the global cryosphere: Status, processes, and trends},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2025.115220},
  url = {https://doi.org/10.1016/j.rse.2025.115220}
}