From Spectral Indices to Artificial Intelligence: A Review of Remote Sensing Methodologies for Glacier Mapping

Glaciers are critical indicators of global climate change, and their accelerated retreat has profound implications for sea-level rise, water resources, and ecosystem stability. Accurate and timely mapping of glacier extent is essential for monitoring these changes. This review provides a comprehensive overview of the evolution of remote sensing techniques for glacier mapping, charting the progression from traditional spectral indices to the current state-of-the-art machine learning (ML) and deep learning (DL) models. We analyze the strengths and limitations of various methods, including the computational efficiency of indices like the Normalized Difference Snow Index (NDSI), the classificatory power of ML algorithms like Random Forest (RF), and the superior performance of DL architectures, particularly U-Net and its variants, for semantic segmentation of glacier mapping. Our analysis highlights a clear trend towards automated, data-driven approaches that have significantly enhanced the accuracy and scale of glacier delineation. However, progress is slowed by key challenges, most importantly the difficulty in getting accurate ‘ground truth’ data due to a lack of standardized, high-resolution training and validation datasets. Other key limitations include an over-reliance on a few model architectures and the need to bridge the gap between research-level accuracy and operational, real-time monitoring systems. Future progress in the field will depend on community-led efforts to create robust benchmark datasets, explore more diverse and efficient model architecture, develop sophisticated data fusion techniques, and improve model transferability and uncertainty quantification. By integrating cutting-edge AI with improved data practices, the remote sensing community can deliver the crucial data needed to understand and respond to the impacts of a changing climate.