Daily imaging from China's HJ-2A/B satellites enables yearly mapping of regional glacial lakes

The rapid expansion of glacial lakes in high-mountain regions, both in number and area, increases the risk of Glacial Lake Outburst Floods (GLOFs) to downstream communities. Optical remote sensing is essential for regional monitoring, as these lakes are frequently widespread and often inaccessible. However, clear image acquisition in mountainous areas is regularly hindered by shadowing, seasonal snow, and cloud cover. In the Himalayas, more frequent imaging is necessary to accurately inventory lakes during the short, snow-free periods. The HJ-2A/B satellites deployed by China now provide daily revisits at 16 m resolution, enabling comprehensive, high-quality mapping of glacial lakes. This study introduces a novel framework that uses a deep-learning U-Net model to detect lakes in HJ-2 imagery automatically and assesses its efficacy for monitoring lake changes in the China—Nepal section of the Central Himalayas (CNCH). Our findings indicate that the frequent imaging capabilities of HJ-2 permit annual lake mapping within a specific 1-mon period, achieving an accuracy and sensitivity to area changes exceeding 99%, with the ability to detect changes as small as 0.004 km². In our case study, we identify 2738 lakes in 2022 and 2739 lakes in 2023, with respective areas of 256.51 ± 14.41 km² and 260.89 ± 14.59 km². Despite certain limitations in geometric and radiometric calibration, this study establishes that HJ-2 imagery is highly effective for consistent, frequent monitoring of glacial lake changes and GLOF risks compared to previous satellite imagery.