Using deep learning and multi-source remote sensing images to map landlocked lakes in Antarctica
Year: 2024
Type: article
Abstract: . Antarctic landlocked lakes' 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 Kappa values of 0.85 and 0.62 on testing datasets respectively, demonstrating strong spatio-temporal robustness across various study areas. We chose four typical ice-free areas located along the coastal Antarctica as our study areas. After applying our LLOW identification model to a total of 79 Landsat 8-9 images and 390 Sentinel-1 images in these four regions, 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, air temperature changes accounted for more than 50 % of the LLOW area fluctuations. It is shown that our model can provide long-term LLOW series products that help us better understand how lakes change under a changing climate in the future.
Source: The cryosphere
Authors: Anyao Jiang, Xin Meng, Yan Huang, Guitao Shi
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,574
OpenAlex ID: W4404599896