Monitoring glacial lake outburst flood susceptibility using Sentinel-1 SAR data, Google Earth Engine, and persistent scatterer interferometry

Continuous monitoring of glacial lakes, their parent glaciers and surroundings is crucial because possible outbursts these lakes pose a serious hazard to downstream areas. Ongoing climate change increases the risk this globally due recession leading formation expansion permafrost degradation which impacts stability glaciers, slopes moraines. Here, we demonstrate capability our approach for lake outburst susceptibility using time-series Sentinel-1 Synthetic Aperture Radar (S-1 SAR) data. We selected Lunana in Bhutanese Himalayas as an example region it highly susceptible floods suitable baseline data were available. used Google Earth Engine (GEE) calculate average radar backscatter intensity (ARBI) basins, To determine periodicity highest lowest intensity, denoised ARBI Fast Fourier Transform autocorrelated Pearson correlation function. Additionally, determined glacier melt area, basin open water ice area The Persistent Scatterer Interferometry (PSI) technique was investigate moraines around lakes. PSI results qualitatively validated by comparison with high-resolution digital elevation model differencing results. Our showed that basins underwent seasonal periodic changes related snow melt. Lakes also variation but not periodic. could infer such rapid Bechung Tsho. analysis all terminal stable except Drukchung Its moraine subsidence at rate –5.18 mm/yr. Sidewalls exception Lugge Tsho site 4. Due free availability S-1 SAR data, efficiency processing large amount imagery within GEE, technique, able understand great detail. regular acquisition enables continuous A similar concept can be transferred any geographic on earth shares challenges monitoring.