Abstract. Glacial lakes are critical indicators of the effects climate change and significant sources of natural hazards, such as Glacial Lake Outburst Floods (GLOFs), cascading events, etc. Monitoring their formation and evolution is essential for understanding cryospheric dynamics and supporting risk management, yet systematic mapping is hindered by the complexity of high-mountain environments. Developing robust, automated methods using remote sensing remains challenging due to rugged topography, snow, ice, and shadows causing misclassification.This paper proposes a multi-sensor methodology for glacial lake detection and monitoring, integrating optical data from Sentinel-2 and Synthetic Aperture Radar (SAR) data from Sentinel-1. The study focuses on the Western Alps using data from 2022 to 2024. The methodology applies optical indices using a double thresholding strategies and tests machine learning algorithms. On the other hand, it investigates the potential of the recently developed OASIS index for SAR-based detection, aiming to overcome cloud cover and illumination limitations inherent in optical imagery.Preliminary results show that optical indices perform well but require dynamic thresholding, as snowmelt and shadows remain major sources of uncertainty. Machine learning approaches demonstrate good potential in mitigating these limitations. The OASIS index (SAR) proves to be a promising complementary tool, especially under cloudy conditions, though still challenged by surface roughness. The integration of optical and radar data significantly increases the robustness of lake detection and reduces temporal gaps in monitoring. This methodology contributes to advancing automated systems for hazard assessment and climate change effects monitoring in alpine regions.
Lodigiani et al. (Tue,) studied this question.