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Remote Sensing Letters Volume 10, 2019 - Issue 7
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A directed limited penetrable visibility graph (DLPVG)-based method of analysing sea surface temperature

Xuan YuSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaORCID Iconhttps://orcid.org/0000-0002-6896-9809View further author information
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Suixiang ShiSchool of Computer Engineering and Science, Shanghai University, Shanghai, China;Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin, ChinaORCID Iconhttps://orcid.org/0000-0002-8954-7293View further author information
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Lingyu XuSchool of Computer Engineering and Science, Shanghai University, Shanghai, China;Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-2992-3733View further author information
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Jie YuSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaORCID Iconhttps://orcid.org/0000-0001-9674-0449View further author information
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Yaya LiuSchool of Computer Engineering and Science, Shanghai University, Shanghai, ChinaORCID Iconhttps://orcid.org/0000-0001-9695-4260View further author information
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Lei WangDepartment of Marine Information Technology, East Sea Information Center SOA China, Shanghai, China;Marine Data Center, National Marine Data and Information Service, Tianjin, ChinaORCID Iconhttps://orcid.org/0000-0001-5760-2393View further author information
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Pages 609-618 | Received 06 Aug 2018, Accepted 22 Feb 2019, Published online: 27 Mar 2019
 
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ABSTRACT

The present paper studies the dynamic association among the sea surface temperature (SST) data through directed limited penetrable visibility graph (DLPVG) method. We analyse the fluctuation trend of temperature in different regions by constructing an SST-dynamic association network to explore the topology structure. By analysing the SST data of the East China Sea, we find that there are association patterns among multivariate SST and a few types of patterns play a significant role in the movement of the sea water. Furthermore, the experiments also show that the multivariate SST has a close relationship with El Niño events, which indicates that the network is of great significance to the research and prediction of the El Niño phenomenon.

Additional information

Funding

This work was supported by National Program on Key Research Project of China [2016YFC1401900].

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