Publication
Ocean surface partitioning strategies using Ocean Colour Remote Sensing: a review
| dc.contributor.author | Krug, Lilian | |
| dc.contributor.author | Platt, T. | |
| dc.contributor.author | Sathyendranath, S. | |
| dc.contributor.author | Barbosa, Ana B. | |
| dc.date.accessioned | 2017-12-21T15:39:33Z | |
| dc.date.available | 2017-12-21T15:39:33Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The ocean surface is organized into regions with distinct properties reflecting the complexity of interactions between environmental forcing and biological responses. The delineation of these functional units, each with unique, homogeneous properties and underlying ecosystem structure and dynamics, can be defined as ocean surface partitioning. The main purposes and applications of ocean partitioning include the evaluation of particular marine environments; generation of more accurate satellite ocean colour products; assimilation of data into biogeochemical and climate models; and establishment of ecosystem-based management practices. This paper reviews the diverse approaches implemented for ocean surface partition into functional units, using ocean colour remote sensing (OCRS) data, including their purposes, criteria, methods and scales. OCRS offers a synoptic, high spatial-temporal resolution, multi-decadal coverage of bio-optical properties, relevant to the applications and value of ocean surface partitioning. In combination with other biotic and/or abiotic data, OCRS-derived data (e.g., chlorophyll-a, optical properties) provide a broad and varied source of information that can be analysed using different delineation methods derived from subjective, expert-based to unsupervised learning approaches (e.g., cluster, fuzzy and empirical orthogonal function analyses). Partition schemes are applied at global to mesoscale spatial coverage, with static (time-invariant) or dynamic (time-varying) representations. A case study, the highly heterogeneous area off SW Iberian Peninsula (NE Atlantic), illustrates how the selection of spatial coverage and temporal representation affects the discrimination of distinct environmental drivers of phytoplankton variability. Advances in operational oceanography and in the subject area of satellite ocean colour, including development of new sensors, algorithms and products, are among the potential benefits from extended use, scope and applications of ocean surface partitioning using OCRS. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.pocean.2017.05.013 | pt_PT |
| dc.identifier.other | AUT: ABA00694; | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/10269 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.subject | Ocean colour remote sensing | pt_PT |
| dc.subject | Satelite oceanography | pt_PT |
| dc.subject | Ocean partitioning | pt_PT |
| dc.subject | Phytoplankton | pt_PT |
| dc.subject | Ecosystem managment | pt_PT |
| dc.subject | Phytoplankton | pt_PT |
| dc.subject | Iberia | pt_PT |
| dc.title | Ocean surface partitioning strategies using Ocean Colour Remote Sensing: a review | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 53 | pt_PT |
| oaire.citation.startPage | 41 | pt_PT |
| oaire.citation.title | Progress in Oceanography | pt_PT |
| oaire.citation.volume | 155 | pt_PT |
| person.familyName | Krug | |
| person.familyName | Barbosa | |
| person.givenName | Lilian | |
| person.givenName | Ana | |
| person.identifier.ciencia-id | 501F-2A75-BBD3 | |
| person.identifier.ciencia-id | F514-2180-4CF8 | |
| person.identifier.orcid | 0000-0002-0066-7679 | |
| person.identifier.orcid | 0000-0002-7402-246X | |
| person.identifier.scopus-author-id | 23479793500 | |
| person.identifier.scopus-author-id | 7103244778 | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | e28133b9-1fff-4ce6-8a80-8aace4103fee | |
| relation.isAuthorOfPublication | 5b72648c-41b5-46c0-9a7f-7fa2f514b973 | |
| relation.isAuthorOfPublication.latestForDiscovery | e28133b9-1fff-4ce6-8a80-8aace4103fee |