A Deep Learning Approach for Mapping and Monitoring Glacial Lakes from Space

Climate change intensifies glacier melt which effectively leads to the formation of numerous new glacial lakes in the overdeepenings of former glacier beds. Additionally, the area of many existing glacial lakes is increasing. More than one thousand glacial lakes have emerged in Switzerland since the Little Ice Age, and hundreds of lakes are expected to form in the 21st century. Rapid deglaciation and formation of new lakes severely affect downstream ecosystem services, hydropower production and high-alpine hazard situations. Day by day, glacier lake inventories for high-alpine terrains are increasingly becoming available to the research community. However, a high-frequency mapping and monitoring of these lakes are necessary to assess hazards and to estimate Glacial Lake Outburst Flood (GLOF) risks, especially for lakes with high seasonal variations. One way to achieve this goal is to leverage the possibilities of satellite-based remote sensing, using optical and Synthetic Aperture Radar (SAR) satellite sensors and deep learning.There are several challenges to be tackled. Mapping glacial lakes using satellite sensors is difficult, due to the very small area of a great majority of these lakes. The inability of the optical sensors (e.g. Sentinel-2) to sense through clouds creates another bottleneck. Further challenges include cast and cloud shadows, and increased levels of lake and atmospheric turbidity. Radar sensors (e.g. Sentinel-1 SAR) are unaffected by cloud obstruction. However, handling cast shadows and natural backscattering variations from water surfaces are hurdles in SAR-based monitoring. Due to these sensor-specific limitations, optical sensors provide generally less ambiguous but temporally irregular information, while SAR data provides lower classification accuracy but without cloud gaps.We propose a deep learning-based SAR-optical satellite data fusion pipeline that merges the complementary information from both sensors. We put forward to use Sentinel-1 SAR and Sentinel-2 L2A imagery as input to a deep network with a Convolutional Neural Network (CNN) backbone. The proposed pipeline performs a fusion of information from the two input branches that feed heterogeneous satellite data. A shared block learns embeddings (feature representation) invariant to the input satellite type, which are then fused to guide the identification of glacial lakes. Our ultimate aim is to produce geolocated maps of the target regions where the proposed bottom-up, data-driven methodology will classify each pixel either as lake or background. This work is part of two major projects: ESA AlpGlacier project that targets mapping and monitoring of the glacial lakes in the Swiss (and European) Alps, and the UNESCO (Adaptation Fund) GLOFCA project that aims to reduce the vulnerabilities of populations in the Central Asian countries (Kazakhstan, Tajikistan, Uzbekistan, and Kyrgyzstan) from GLOFs in a changing climate. As part of the GLOFCA project, we are developing a python-based analytical toolbox for the local authorities, which incorporates the proposed deep learning-based pipeline for mapping and monitoring the glacial lakes in the target regions in Central Asia.