A global process-oriented sea surface temperature anomaly dataset retrieved from remote sensing products

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-12-14 DOI:10.1080/20964471.2021.1988426
C. Xue, Yangfeng Xu, Yawen He
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引用次数: 4

Abstract

ABSTRACT From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. This dataset is called GDPoSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The GDPoSSTA dataset is available on ScienceDB platform (http://www.doi.org/10.11922/sciencedb.j00076.00090).
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基于遥感产品的全球过程型海面温度异常数据集
海温异常(SSTA)从最初发展开始,将在空间和时间上不断发展,直至消散。虽然有许多海温产品可供选择,但在试图直接探索海温演变的过程中仍然面临着巨大的挑战。为了解决这些问题,本研究开发了一个包含SSTA空间结构及其时间演变细节的全球SSTA数据集。这个数据集叫做GDPoSSTA。GDPoSSTA由三个数据集和两个关系文件组成,涵盖了1982年1月至2009年12月的时间。三个数据集均为SHP格式,由处理过的面向对象SSTA数据集DSPOSSTA、排序过的面向对象SSTA序列数据集DSSOSSTA和变化面向对象SSTA数据集DSVOSSTA组成。这两个关系文件采用CSV格式,存储了SSTA序列对象和SSTA变化对象的演化行为。最后,对DSPOSSTA进行了地理时空统计,并将TITAN应用于DSVOSSTA和DSPOSSTA进行了对比,验证了GDPoSSTA的可行性和适用性。GDPoSSTA数据集可在ScienceDB平台(http://www.doi.org/10.11922/sciencedb.j00076.00090)上获得。
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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