Аңдатпа
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.