Glacial lake mapping: Progress, challenges, and future directions

Glacial lakes are a critical water resource, an indicator of climate change, and a potential source for glacial lake outburst floods. Accurate and frequent mapping is important for understanding glacial lake dynamics and associated hazards, particularly in a changing climate. Many datasets and mapping techniques are currently used to delineate glacial lakes. This review offers a comprehensive analysis of the datasets and methods used for glacial lake mapping at regional and global scales, highlights their strengths and limitations, provides suggestions for choosing appropriate data and methods, and proposes potential future research directions. We find that field observations provide the most detailed information on glacial lakes. However, resource constraints limit field surveys, and remote sensing–based datasets and threshold-based approaches are the standard practice. Recently, machine learning models have become more common due to their ability to automatically delineate glacial lakes. Cloud computing has simplified the access to large remote sensing datasets and plays a vital role in the automation of glacial lake mapping. In the future, we recommend the establishment of standardized glacial lake definitions and a global glacial lake monitoring system based on deep learning models. Fostering interinstitutional and community collaborations is expected to help achieve these goals.