气候变化领域集成服务门户(CCPortal): Using Deep Learning for Glacier Thickness Estimation at a Regional Scale

CCPortal

DOI	10.1109/LGRS.2024.3353575
	Using Deep Learning for Glacier Thickness Estimation at a Regional Scale
	Uroz, Lorenzo Lopez; Yan, Yajing; Benoit, Alexandre; Rabatel, Antoine; Giffard-Roisin, Sophie; Lin-Kwong-Chon, Christophe
发表日期	2024
ISSN	1545-598X
EISSN	1558-0571
起始页码	21
卷号	21
英文摘要	Mountain glaciers play a critical role for mountain ecosystems and society with major concerns related to their future evolution and related water resources. Modeling glacier future evolution allows anticipating climate change impacts and informing policy decisions. It relies on accurate ice thickness estimation at regional scales. This letter proposes a deep learning-based approach in a supervised learning framework for ice thickness estimation at a regional scale from surface ice velocity measurements and a digital elevation model (DEM). A neural network model built upon a ResNet architecture is proposed based on the trade-off between the model complexity and the prediction efficiency. Promising results are obtained from data including 1400 glaciers in the Swiss Alps, highlighting the potential of deep learning-based approach for large-scale ice thickness estimation. The incorporation of expert's knowledge into the neural network model further helps refine the model prediction and improve the model relevance. The ice volume difference between the reference issued from ground penetrating radar (GPR) measurements and the predictions by the proposed neural network model varies between 0.5% and 16% of the reference volume. Larger ice volume difference is mainly related to over-deepening of the bedrock resulting from past larger extent of the glacier, which information is not included in the data.
英文关键词	Deep learning; glacier flow velocity; ice thickness; neural network; regional scale; surface slope
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001166715200002
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296024
作者单位	Universite Savoie Mont Blanc; INRAE; Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Institut National Polytechnique de Grenoble; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite Gustave-Eiffel; Universite Savoie Mont Blanc
推荐引用方式 GB/T 7714	Uroz, Lorenzo Lopez,Yan, Yajing,Benoit, Alexandre,et al. Using Deep Learning for Glacier Thickness Estimation at a Regional Scale[J],2024,21.
APA	Uroz, Lorenzo Lopez,Yan, Yajing,Benoit, Alexandre,Rabatel, Antoine,Giffard-Roisin, Sophie,&Lin-Kwong-Chon, Christophe.(2024).Using Deep Learning for Glacier Thickness Estimation at a Regional Scale.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,21.
MLA	Uroz, Lorenzo Lopez,et al."Using Deep Learning for Glacier Thickness Estimation at a Regional Scale".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21(2024).