From land to sea, a review of hypertemporal remote sensing advances to support ocean surface science

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dc.contributor.author Scarrott, Rory
dc.contributor.author Cawkwell, Fiona
dc.contributor.author Jessopp, Mark J.
dc.contributor.author O'Rourke, Eleanor
dc.contributor.author Cusack, Caroline
dc.contributor.author de Bie, Kees
dc.date.accessioned 2019-12-04T12:25:47Z
dc.date.available 2019-12-04T12:25:47Z
dc.date.issued 2019-10-31
dc.identifier.citation Scarrott, R. G., Cawkwell, F., Jessopp, M., O’Rourke, E., Cusack, C. and de Bie, K. (2019) 'From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science', Water, 11(11), 2286. (26pp.) doi: 10.3390/w11112286 en
dc.identifier.volume 11 en
dc.identifier.issued 11 en
dc.identifier.startpage 1 en
dc.identifier.endpage 26 en
dc.identifier.issn 2073-4441
dc.identifier.uri http://hdl.handle.net/10468/9324
dc.identifier.doi 10.3390/w11112286 en
dc.description.abstract Increases in the temporal frequency of satellite-derived imagery mean a greater diversity of ocean surface features can be studied, modelled, and understood. The ongoing temporal data “explosion” is a valuable resource, having prompted the development of adapted and new methodologies to extract information from hypertemporal datasets. Current suitable methodologies for use in hypertemporal ocean surface studies include using pixel-centred measurement analyses (PMA), classification analyses (CLS), and principal components analyses (PCA). These require limited prior knowledge of the system being measured. Time-series analyses (TSA) are also promising, though they require more expert knowledge which may be unavailable. Full use of this resource by ocean and fisheries researchers is restrained by limitations in knowledge on the regional to sub-regional spatiotemporal characteristics of the ocean surface. To lay the foundations for more expert, knowledge-driven research, temporal signatures and temporal baselines need to be identified and quantified in large datasets. There is an opportunity for data-driven hypertemporal methodologies. This review examines nearly 25 years of advances in exploratory hypertemporal research, and how methodologies developed for terrestrial research should be adapted when tasked towards ocean applications. It highlights research gaps which impede methodology transfer, and suggests achievable research areas to be addressed as short-term priorities. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher MDPI en
dc.rights ©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Hypertemporal en
dc.subject Earth Observation data en
dc.subject Remote sensing en
dc.subject Methologies en
dc.subject Oceanography en
dc.title From land to sea, a review of hypertemporal remote sensing advances to support ocean surface science en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Rory Gordon Scarrott, Department of Geography and Environmental Research Institute, University College Cork, Cork, Ireland. +353-21-490-3000 Email:r.scarrott@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Horizon 2020 en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Water en
dc.internal.IRISemailaddress r.scarrott@ucc.ie en
dc.identifier.articleid 2286 en
dc.relation.project info:eu-repo/grantAgreement/EC/H2020::RIA/687289/EU/Coastal Waters Research Synergy Framework/Co-ReSyF en


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©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Except where otherwise noted, this item's license is described as ©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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