From land to sea, a review of hypertemporal remote sensing advances to support ocean surface science
| 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.contributor.funder | Horizon 2020 | en |
| dc.date.accessioned | 2019-12-04T12:25:47Z | |
| dc.date.available | 2019-12-04T12:25:47Z | |
| dc.date.issued | 2019-10-31 | |
| 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.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.articleid | 2286 | en |
| 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.doi | 10.3390/w11112286 | en |
| dc.identifier.endpage | 26 | en |
| dc.identifier.issn | 2073-4441 | |
| dc.identifier.issued | 11 | en |
| dc.identifier.journaltitle | Water | en |
| dc.identifier.startpage | 1 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/9324 | |
| dc.identifier.volume | 11 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.relation.project | info:eu-repo/grantAgreement/EC/H2020::RIA/687289/EU/Coastal Waters Research Synergy Framework/Co-ReSyF | 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 |
