A Systematic Review of Methodological Advances in Glacier-Velocity Retrieval with an Emphasis on Debris-Covered Glaciers
Norova N. Samat A. Abuduwaili J.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
Remote Sensing
2026#18Issue 1
Highlights: What are the main findings? The review synthesizes over three decades of methodological development in glacier-velocity retrieval and demonstrates that hybrid SAR–optical, AI-enhanced, and multi-sensor approaches generally show superior performance, especially over debris-covered glaciers. The analysis identifies clear research gaps, including limited validation, geographic imbalance, and insufficient physical consistency in AI models, and provides a structured comparison of method performance specifically for debris-covered glacier environments, a niche not systematically addressed in prior general reviews. What are the implications of the main findings? The demonstrated advantages of hybrid and AI-augmented frameworks highlight a pathway toward more reliable, high-resolution, and near-real-time monitoring systems for debris-covered glaciers. The identified gaps provide a roadmap for future cryosphere research, emphasizing the need for open benchmarks, physically grounded AI, improved in situ validation, and expanded geographical representation to enable globally consistent glacier-motion monitoring. Monitoring glacier flow velocity is crucial for understanding ice dynamics, mass balance, and hydrological processes in a changing climate. This study provides a comprehensive systematic review of methodological advances in glacier-velocity retrieval, with a particular focus on debris-covered glaciers that remain underrepresented in current research. We used the PRISMA framework to identify 121 peer-reviewed studies published between 1992 and 2025, which we analyzed to identify key developments, data sources, and performance characteristics. The examined methodologies encompass feature tracking, InSAR, offset tracking, optical flow, deep learning algorithms, and data fusion strategies that integrate optical and SAR datasets. The findings demonstrate a clear trend away from manual and correlation-based approaches towards automated, AI-informed systems, driven by the increasing availability of satellite data and advances in computational power. Accuracy and uncertainty tests indicate persistent problems with debris-covered surfaces due to low surface contrast and heterogeneity. Emerging trends point toward increasing integration of data fusion and glaciological modeling, paving the way for more intelligent, automated, and physically informed monitoring systems. This underscores the necessity for open data, reproducible methodologies, and interdisciplinary collaboration to advance the accuracy and scalability of global glacier-velocity monitoring.
debris-covered glaciers (DCGs) , deep learning (DL) , glacier velocity , machine learning (ML) , SAR
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Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
University of Chinese Academy of Sciences, Beijing, 100049, China
China-Kazakhstan Joint Laboratory for RS Technology and Application, Al-Farabi Kazakh National University, Almaty, 050012, Kazakhstan
CAS Research Center for Ecology and Environment of Central Asia, Urumqi, 830011, China
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
University of Chinese Academy of Sciences
China-Kazakhstan Joint Laboratory for RS Technology and Application
CAS Research Center for Ecology and Environment of Central Asia
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