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See detailCloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology.
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

in Journal of Sea Research (2011), 65(1), 114-130

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 ... [more ▼]

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. [less ▲]

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See detailReconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data
Nechad, Bouchra; Alvera Azcarate, Aïda ULg; Ruddick, Kevin et al

in Ocean Dynamics (2011)

In situ measurements of total suspended matter (TSM) over the period 2003–2006, collected with two autonomous platforms from the Centre for Environment, Fisheries and Aquatic Sciences (Cefas) measuring ... [more ▼]

In situ measurements of total suspended matter (TSM) over the period 2003–2006, collected with two autonomous platforms from the Centre for Environment, Fisheries and Aquatic Sciences (Cefas) measuring the optical backscatter (OBS) in the southern North Sea, are used to assess the accuracy of TSM time series extracted from satellite data. Since there are gaps in the remote sensing (RS) data, due mainly to cloud cover, the Data Interpolating Empirical Orthogonal Functions (DINEOF) is used to fill in the TSM time series and build a continuous daily “recoloured” dataset. The RS datasets consist of TSM maps derived from MODIS imagery using the bio-optical model of Nechad et al. (Rem Sens Environ 114: 854–866, 2010). In this study, the DINEOF time series are compared to the in situ OBS measured in moderately to very turbid waters respectively in West Gabbard and Warp Anchorage, in the southern North Sea. The discrepancies between instantaneous RS, DINEOF-filled RS data and Cefas data are analysed in terms of TSM algorithm uncertainties, space–time variability and DINEOF reconstruction uncertainty. [less ▲]

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See detailEstimating pCO2 from remote sensing in the Belgian coastal zone
Borges, Alberto; Ruddick, Kevin; Lacroix, Geneviève et al

in ESA Special Publication SP-686 (2010)

In coastal waters, a purely field observation based approach will probably be insufficient to better constrain estimates of air-sea CO2 fluxes, to study their inter-annual variability and their long-term ... [more ▼]

In coastal waters, a purely field observation based approach will probably be insufficient to better constrain estimates of air-sea CO2 fluxes, to study their inter-annual variability and their long-term changes. One approach to achieve these goals is to use remotely sensed fields of relevant biogeochemical variables to extrapolate available data, and produce maps of the partial pressure of CO2 (pCO2) and air-sea CO2 fluxes. In the open ocean this approach has to some extent been successfully used based on fields of chlorophyll-a (Chla) and sea surface temperature (SST). This approach remains challenging in coastal waters that have complex optical properties (Case-II waters) and that exhibit highly dynamic pCO2 temporal and spatial variations. In the frame of the Belgian funded BELCOLOUR-II project (Optical remote sensing of marine, coastal and inland waters; http://www.mumm.ac.be/BELCOLOUR/), three field cruises per year (April, July and September) for optical measurements were carried in 2007, 2008, 2009 in the Southern Bight of the North Sea (SBNS). Based on these data-sets, we derived algorithms to compute pCO2 from Chl-a and sea surface salinity (SSS) using multipolynomial regressions (MPR). Here we report the first application of the MPR algorithms to derive pCO2 fields in the Belgian coastal zone based on data gathered in 2007, using remote sensed Chl-a (MERIS) and SSS computed with a 3-D hydrodynamical model of SBNS (COHERENS). [less ▲]

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See detailReconstruction of Missing Satellite Total Suspended Matter Data over the Southern North Sea and English Channel using Empirical Orthogonal Function Decomposition of Satellite Imagery and Hydrodynamical Modelling
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2008, October)

Optical remote sensing data archives generally have many gaps caused by clouds or other retrieval problems. However, for the light forcing of ecosystem models continuous fields are required. For ... [more ▼]

Optical remote sensing data archives generally have many gaps caused by clouds or other retrieval problems. However, for the light forcing of ecosystem models continuous fields are required. For parameters exhibiting strong spatial and temporal correlations for regions of similar dynamics or from day to day, the missing data can be estimated by use of statistical techniques. In this context, the Data Interpolation with Empirical Orthogonal Functions (DINEOF) method is used for reconstruction of complete space-time information for surface total suspended matter (TSM) and chlorophyll a from a 5-year archive of MODIS and MERIS products over the Southern North Sea and English Channel. The DINEOF univariate methodology has been previously demonstrated for Mediterranean sea surface temperature data (Alvera-Azcarate et al., 2005, Beckers et al., 2006). Alvera-Azcarate et al (2007) showed that SST reconstructions could be improved by using a multivariate approach in which SST, chlorophyll and wind fields were taken into account together for the analyses. Here, TSM images will be used in combination with information from the COHERENS hydrodynamical model to provide a complete and continuous estimate of surface TSM for the Southern North Sea throughout the period 2003-2005. In addition to the remotely sensed TSM, the DINEOF multivariate analysis will consider wind fields, depth integrated currents, surface elevations and bottom stresses. Reconstucted images are compared with the original incomplete images. Validation of the method is achieved by estimation of information removed from the training data by exclusion of entire images and by addition of artificial clouds. The data reconstruction technique has further applications in the processing and quality control of optical remote sensing data. Perspectives will be outlined for improving the quality control of retrieved parameters and for the improvement of retrievals by adding statistical information to the conventional spectral processing. References: Alvera-Azcarate, A., Barth, A., Rixen, M., and Beckers, J.-M.: Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea, Ocean Modelling, 9, 325–346, 2005. Alvera-Azcarate, A., Barth, A., Beckers, J. M., and Weisberg, R. H.: Multivariate Reconstruction of Missing Data in Sea Surface Temperature, Chlorophyll and Wind Satellite Fields, Journal of Geophysical Research, 112, C03008, doi:10.1029/2006JC003660, 2007. Beckers J.-M., A. Barth & A. Alvera-Azcarate, DINEOF reconstruction of clouded images including error maps. Application to the Sea-Surface Temperature around Corsican Island, Ocean Sciences, 2: 183–199, 2006. [less ▲]

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See detailRemote Sensing of Suspended Particulate Matter in Turbid Waters: State of the Art and Future Perspectives.
Ruddick, Kevin; Nechad, Bouchra; Park, Youngje et al

Conference (2008, October)

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See detailCloud filling of total suspended matter, chlorophyll and sea surface temperature remote sensing products by the Data Interpolation with Empirical Orthogonal Functions methodology, application to the BELCOLOUR-1 database
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

in ESA Special Publication SP666 (2008, September)

Space-time filling of the gaps in satellite data archives is an important step for the improvement of various marine ecosystem studies. The Data Interpolation with Empirical Orthogonal Functions ... [more ▼]

Space-time filling of the gaps in satellite data archives is an important step for the improvement of various marine ecosystem studies. The Data Interpolation with Empirical Orthogonal Functions methodology (DINEOF) allows calculating missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set [1]. It was successfully applied to SST reconstructions as in [1] and [2]. Here, the DINEOF reconstruction method is applied to surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) data over the Southern North Sea and English Channel obtained from the BELCOLOUR archive. 1. Beckers, J.-M. and Rixen, M. (2003). EOF Calculations and Data Filling from Incomplete Oceanographic Datasets. Journal of Atmospheric and Oceanic Technology, 20:18391856. 2. Alvera-Azcárate, A., Barth, A., Rixen, M. and Beckers, J.-M. (2005). Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea surface temperature. Ocean Modelling, 9:325–346. [less ▲]

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See detailREconstruction of COLOUR scenes
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2008, February 12)

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See detailDINEOF univariate reconstruction of missing satellite data from the North Sea Belcolour-1 database.
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2008)

The Belcolour-1 database holds more than 4 years of uniformly resampled MERIS chlorophyll (CHL), total suspended matter (TSM), MODIS-AQUA CHL, TSM and sea surface temperature (SST) over the North Sea. A ... [more ▼]

The Belcolour-1 database holds more than 4 years of uniformly resampled MERIS chlorophyll (CHL), total suspended matter (TSM), MODIS-AQUA CHL, TSM and sea surface temperature (SST) over the North Sea. A first step of the RECOLOUR* project consists in the univariate reconstruction of missing data with the DINEOF method (Data Interpolating Empirical Orthogonal Functions). In particular, the DINEOF treatment of MERIS CHL and TSM images available for the year 2003 allowed an efficient synthesis of the coherent modes of variability existing at the scale of the whole North Sea. For both parameters, 4 modes were retained by general cross validation as an optimum for the reconstruction of missing data. For CHL, the first spatial mode shows the high influence of coastal nutrients outputs (mainly continental estuaries and diffused coastal sources) and the lower concentration in the well stratified central and northern part of the North Sea compared to the southern bight and the eastern English Channel. The spatial trends described by the first mode are permanent features during the year, although slightly enhanced during the summer and reduced during winter. The second spatial mode correspond to the main algal blooming events (spring and autumn) with increased concentrations in the whole southern bight of the north sea, around the Isle of Wight and in frontal alike structure north-west from Denmark. The third Eofs describes well the dynamics of an early phytoplankton bloom occurring in march along the Norwegian coast, where a strong stratification induced by an output of cold water from Baltic Sea provides good light conditions to phytoplankton. Concerning TSM, the first spatial mode shows the dominant influence of large estuaries and of resuspension from shallow coastal sedimental plains. The patterns suggest a general transport of sediments from south-east England up to the northern Dutch coastal waters, as a clear distinction between the stratified northern part and the well mixed and charged southern and German bights. Although these trends are permanent during the year, the range of the spatial variations are slightly reduced during the summer, following the reduction of resuspension, of total sediment outputed by rivers and of advection along continental coasts. The second mode shows a clear seasonal signal. The winter influence of the second spatial mode can be understood as general sediment enrichment due to higher resuspension, but a clear influence of intense winter terrestrial water outflows leading to lower sediment concentration in the plumes then in the surrounding waters. This is clear for the Elbe river discharge, the whole natural part of the Wadden Sea and the Seine river plume. The Scheelde and Thames rather seems to be just neutralizing the seasonal TSM resuspension signal. The Rhine river discharge seems to make exception as no influence is detected in the second spatial mode. During summer, the contribution of the second EOF is reversed with a general reduction of suspended matter concentration in most part of the area but some local sediment enrichment at specific river discharges. Original MERIS CHL and TSM images were filled and reconstructions were produced at a daily interval based on a linear interpolation of the temporal modes. From this, weekly averages could be calculated at stations such as the turbidity maximum of the Scheelde river plume, showing the onset of the spring bloom co-occurring with a period characterised both by the TSM seasonal reduction and by important TSM temporal variability. [less ▲]

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