Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
  • edoc-Server Home
  • Artikel und Monographien
  • Zweitveröffentlichungen
  • View Item
2017-12-14Zeitschriftenartikel DOI: 10.3390/w9120936
A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods
Akbari, Elahe
Alavipanah, Seyed
Jeihouni, Mehrdad
Hajeb, Mohammad
Haase, Dagmar cc
Alavipanah, Sadroddin
Mathematisch-Naturwissenschaftliche Fakultät
Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth’s ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical) approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.
Files in this item
Thumbnail
water-09-00936.pdf — Adobe PDF — 2.721 Mb
MD5: 12d584367d3b35816433a007ef791f89
Cite
BibTeX
EndNote
RIS
(CC BY 4.0) Attribution 4.0 International(CC BY 4.0) Attribution 4.0 International
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.3390/w9120936
Permanent URL
https://doi.org/10.3390/w9120936
HTML
<a href="https://doi.org/10.3390/w9120936">https://doi.org/10.3390/w9120936</a>