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...
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GFZ German Research Centre for Geosciences
2023
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ftdatacite:10.57757/iugg23-3415 2023-07-23T04:21:21+02:00 Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... Chen, Yueli Li, Shile Wang, Lingxiao Mittermeier, Magdalena Bernier, Monique Ludwig, Ralf 2023 https://dx.doi.org/10.57757/iugg23-3415 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019522 unknown GFZ German Research Centre for Geosciences Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Article ConferencePaper Oral 2023 ftdatacite https://doi.org/10.57757/iugg23-3415 2023-07-03T21:06:04Z <!--!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) ... Conference Object permafrost Nunavik DataCite Metadata Store (German National Library of Science and Technology) Canada Nunavik |
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<!--!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) ... |
format |
Conference Object |
author |
Chen, Yueli Li, Shile Wang, Lingxiao Mittermeier, Magdalena Bernier, Monique Ludwig, Ralf |
spellingShingle |
Chen, Yueli Li, Shile Wang, Lingxiao Mittermeier, Magdalena Bernier, Monique Ludwig, Ralf Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... |
author_facet |
Chen, Yueli Li, Shile Wang, Lingxiao Mittermeier, Magdalena Bernier, Monique Ludwig, Ralf |
author_sort |
Chen, Yueli |
title |
Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... |
title_short |
Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... |
title_full |
Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... |
title_fullStr |
Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... |
title_full_unstemmed |
Retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western Nunavik (Québec, Canada) ... |
title_sort |
retrieval of freeze/thaw-cycles using remote sensing data with deep learning approach in western nunavik (québec, canada) ... |
publisher |
GFZ German Research Centre for Geosciences |
publishDate |
2023 |
url |
https://dx.doi.org/10.57757/iugg23-3415 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019522 |
geographic |
Canada Nunavik |
geographic_facet |
Canada Nunavik |
genre |
permafrost Nunavik |
genre_facet |
permafrost Nunavik |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.57757/iugg23-3415 |
_version_ |
1772186822357549056 |