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Daily Prediction of Sea Ice Drift at Kilometer-Scale Using Deep-Learning Method

文献类型：期刊论文

作者Ren, Jialong3,4; Qiu, Yujia3,4; Li, Xiao-Ming2,4; Li, Xiaofeng1,5
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2025
卷号18页码:25980-25992
关键词Sea ice Arctic Spatial resolution Synthetic aperture radar Predictive models Input variables Vectors Data models Wind forecasting Trajectory deep learning sea ice drift (SID) prediction synthetic aperture radar (SAR)
ISSN号1939-1404
DOI10.1109/JSTARS.2025.3617067
通讯作者Li, Xiao-Ming(lixm@radi.ac.cn)
英文摘要Arctic sea ice drift (SID), a crucial dynamic characteristic of sea ice, is also an important parameter for sea ice mass change quantification as it directly determines the regional sea ice distribution and influences the annual polar sea ice export. Common methods of obtaining high spatial resolution SID depend on the time-series Synthetic Aperture Radar (SAR) data, necessitating multiple data scenes. Here, we developed a deep-learning model incorporating single scene high spatial resolution SAR data to predict daily SID at 1-km resolution, achieving a mean absolute error of 1.14 cms(-1) compared to International Arctic Buoy Programme buoy data. The proposed method has significant advantages over traditional machine-learning methods in establishing complex relationships between multiple variables and SID. Additionally, we found that wind has the greatest impact on both the speed and direction of SID prediction. Latitude and longitude together serve as the second most important predictors of SID, after wind. Furthermore, the SAR data inputs have reduced the mean absolute error for speed and direction by 0.24 cms(-1) and 1.62 degrees, enhancing the SID predictions' accuracy, particularly in regions with thick ice.
WOS关键词MOTION ; DEFORMATION ; IMPROVEMENT ; SENTINEL-1 ; RADIOMETER ; RETRIEVAL ; THICKNESS ; ACCURACY ; TRACKING ; PRODUCT
资助项目National Key R&D Program of China[2022YFC2807000]
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001598833700002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.qdio.ac.cn/handle/337002/203671]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Li, Xiao-Ming
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.Aerosp Informat Technol Univ, Jinan 250200, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
5.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China
推荐引用方式
GB/T 7714		 Ren, Jialong,Qiu, Yujia,Li, Xiao-Ming,et al. Daily Prediction of Sea Ice Drift at Kilometer-Scale Using Deep-Learning Method[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:25980-25992.
APA Ren, Jialong,Qiu, Yujia,Li, Xiao-Ming,&Li, Xiaofeng.(2025).Daily Prediction of Sea Ice Drift at Kilometer-Scale Using Deep-Learning Method.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,25980-25992.
MLA Ren, Jialong,et al."Daily Prediction of Sea Ice Drift at Kilometer-Scale Using Deep-Learning Method".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):25980-25992.

入库方式： OAI收割

来源：海洋研究所

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