Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ...
<!--!introduction!--> As an important component in the terrestrial cryosphere, the soil freeze-thaw (FT) cycle plays a determinant role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. The FT-state can be monitored exactly with in-situ field measure...
| Main Authors: | , , , , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-3415 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019522 |
| Summary: | <!--!introduction!--> As an important component in the terrestrial cryosphere, the soil freeze-thaw (FT) cycle plays a determinant role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. The FT-state can be monitored exactly with in-situ field measurements, which is costly and limited to single chosen sites. Remote sensing data provides the possibility of collecting information over a large area repeatedly. To explore a more effective way to monitor the FT states in the terrestrial cryosphere, we used microwave and optic remote sensing data and introduced the Deep Learning approach to simulate the soil FT state. MLP and CNN networks were trained and tested with, respectively, over 35000 and about 54000 randomly selected data samples over the entire western part of Nunavik, Canada. The data were labeled following chosen FT reference periods in a year. The trained CNN networks generally performed better than MLP networks and reached model accuracies of around ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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