Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning

doi:10.5194/essd-16-5131-2024

IOCAS-IR > 海洋环流与波动重点实验室

	Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning
	Zhang, Xudong 1,2; Li, Xiaofeng 1,2
	2024-11-06
发表期刊	EARTH SYSTEM SCIENCE DATA
ISSN	1866-3508
卷号	16 期号:11 页码:5131-5144
通讯作者	Li, Xiaofeng([email protected])
摘要	Internal waves (IWs) are an important ocean phenomenon facilitating energy transfer between multiscale ocean processes. Understanding such processes necessitates the collection and analysis of extensive observational data. IWs predominantly occur in marginal seas, with the South China Sea (SCS) being one of the most active regions, characterized by frequent and large-amplitude IW activities. In this study, we present a comprehensive IW dataset for the northern SCS (10.12157/IOCAS.20240409.001, Zhang and Li, 2024), covering the area from 112.40 to 121.32 degrees E and from 18.32 to 23.19 degrees N, spanning the period from 2000 to 2022 with a 250 m spatial resolution. During the 22 years, a total of 15 830 MODIS images were downloaded for further processing. Out of these, 3085 high-resolution MODIS true-color images were identified to contain IW information and were included in the dataset with precise IW positions extracted using advanced deep learning techniques. IWs in the northern SCS are categorized into four regions based on extracted IW spatial distributions. This classification enables detailed analyses of IW characteristics, including their spatial and temporal distributions across the entire northern SCS and its specific sub-regions. Interestingly, our temporal analysis reveals characteristic "double-peak" patterns aligned with the lunar day, highlighting the strong connection between IWs and tidal cycles. Furthermore, our spatial analysis identifies two IW quiescent zones within the IW clusters influenced by underwater topography, highlighting regional variations in IW characteristics and suggesting underlying mechanisms which merit further investigation. There are also three gap regions between distinct IW clusters, which may indicate different IW sources. The constructed dataset holds significant potential for studying IW-environment interactions, developing monitoring and prediction models, validating numerical simulations, and serving as an educational resource to promote awareness and interest in IW research.
DOI	10.5194/essd-16-5131-2024
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China-Shandong Joint Fund; NASA Earth Observing System Data and Information System (EOSDIS)
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001348310600001
出版者	COPERNICUS GESELLSCHAFT MBH
WOS关键词	SOLITARY WAVES ; NUMERICAL-SIMULATION ; DONGSHA ATOLL ; OCEAN ; REFRACTION
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/199412
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Key Lab Ocean Observat & Forecasting, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xudong,Li, Xiaofeng. Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning[J]. EARTH SYSTEM SCIENCE DATA,2024,16(11):5131-5144.
APA	Zhang, Xudong,&Li, Xiaofeng.(2024).Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning.EARTH SYSTEM SCIENCE DATA,16(11),5131-5144.
MLA	Zhang, Xudong,et al."Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning".EARTH SYSTEM SCIENCE DATA 16.11(2024):5131-5144.