A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning ...
While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolutio...
Main Authors: | , , , , , , , , |
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Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
ETH Zurich
2024
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Subjects: | |
Online Access: | https://dx.doi.org/10.3929/ethz-b-000647860 http://hdl.handle.net/20.500.11850/647860 |
Summary: | While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as ... : Remote Sensing of Environment, 301 ... |
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