Using deep learning and multi-source remote sensing images to map landlocked lakes in Antarctica

Abstract

Antarctic landlocked lake open water (LLOW) plays an important role in the Antarctic ecosystem and serves as a reliable climate indicator. However, since field surveys are currently the main method to study Antarctic landlocked lakes, the spatial and temporal distribution of landlocked lakes across Antarctica remains understudied. We first developed an automated detection workflow for Antarctic LLOW using deep learning and multi-source satellite images. The U-Net model and LLOW identification model achieved average F1 scores of 0.90 and 0.89 on testing datasets, respectively, demonstrating strong spatiotemporal robustness across various study areas. We chose four typical ice-free areas located along coastal Antarctica as our study areas. After applying our LLOW identification model to a total of 79 Landsat 8 Operational Land Imager (OLI) images and 330 Sentinel-1 synthetic aperture radar (SAR) images in these four areas, we generated high-spatiotemporal-resolution LLOW time series from January to April between 2017 and 2021. We analyzed the fluctuation of LLOW areas in the four study areas and found that during expansion of LLOW, over 90 % of the changes were explained by positive degree days, while during contraction, negative degree day changes accounted for more than 50 % of the LLOW area fluctuations. It is shown that our model can provide long-term LLOW time series products that help us better understand how lakes change under a changing climate.