Reference : Cloud filling of ocean colour and sea surface temperature remote sensing products ove...
Scientific journals : Article
Life sciences : Aquatic sciences & oceanology
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
http://hdl.handle.net/2268/81349
Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology.
English
Sirjacobs, Damien mailto [Université de Liège - ULg > Département des sciences de la vie > Algologie, mycologie et systématique expérimentale >]
Alvera Azcarate, Aïda mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Barth, Alexander mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Lacroix, Geneviève mailto [Royal Belgian Institute of Natural Sciences (RBINS) > Management Unit of the North Sea Mathematical Models (MUMM) > > >]
Park, Youngje [CSIRO Land and Water > Environmental Remote Sensing Group > > >]
Nechad, Bouchra mailto [Royal Belgian Institute of Natural Sciences (RBINS) > Management Unit of the North Sea Mathematical Models (MUMM) > > >]
Ruddick, Kevin [Roylal Belgian Institute of Natural Sciences (RBINS) > Management Unit of the North Sea Mathematical Models (MUMM) > > >]
Beckers, Jean-Marie mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Jan-2011
Journal of Sea Research
Elsevier Science
65
1
114-130
Yes (verified by ORBi)
International
1385-1101
Amsterdam
The Netherlands
[en] Remote sensing ; cloud filling ; quality control ; empirical orthogonal functions ; ocean colour ; SST ; North Sea ; English Channel
[en] Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions
methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space-time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5 % for CHL and 97.2 % for TSM. For the MODIS TSM dataset, 97.5 % of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98 % of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006) [6]. Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithms artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases.
Centre Interfacultaire de Recherches en Océanologie - MARE
Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy
RECOLOUR
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/81349
10.1016/j.seares.2010.08.002
http://www.elsevier.com
Available on-line since 30/08/2010 at doi:10.1016/j.seares.2010.08.002

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