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Cloud filling of TSM, CHL and SST remote sensing products by the Data Interpolation with Empirical Orthogonal Functions methodology (DINEOF), application to the MERIS, MODIS and SEAWIFS datasets of the BELCOLOUR-1 database.

Damien Sirjacobs(1), Aida ALVERA-AZCARATE(1), Alexander BARTH(1), Youngje PARK(2), Bouchra NECHAD(2), KEvin RUDDICK(2) and Jean-Marie BECKERS(1)

(1) University of Liège, Allée de la Physique, B5 Sart-Tilman, 4000 Liège, Belgium
(2) Royal Belgian Institute of Natural Sciences, 100 Gulledelle, 1200 Bruxelles, Belgium

Abstract

Space-time filling of the gaps in satellite data archives caused by clouds or other retrieval problems is an important step for the improvement of various marine ecosystem studies (forcing of ecosystem models, algae bloom detection). The Data Interpolation with Empirical Orthogonal Functions (DINEOF) is an efficient methodology allowing to calculate missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set (Beckers and Rixen, 2003). Well suited to the processing of remote sensing archives, It was successfully applied to SST reconstructions (Alvera Azcárate et al, 2005; Beckers et al, 2006). This study demonstrates the use of this method for reconstruction of complete space-time information for surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) from the BELCOLOUR archive holding 5 years of MERIS, MODIS and SEAWIFS products over the North Sea Sea and English Channel (http://www.mumm.ac.be/BELCOLOUR/). The univariate signals are synthetised into variable number of dominants modes according to the parameter analysed. Each of these modes are described by a spatial EOF (2D map) and a corresponding temporal EOF (1D time series, also called the EOF amplitudes). The optimal number of modes, their patterns and the quality of the reconstructions are presented and compared for the different datasets processed, allowing comparison between satellites for the same parameters. 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. Factors affecting performance of the reconstruction are discussed. The complete time series generated from the reconstruction are compared with point data from the original images and with in situ measurements. Monthly averaged reconstructed fields are illustrated for typical situations, underlying the interest of the global method for the establisment of precise surface water seasonal climatologies.

References:

J.M. Beckers and M. Rixen. 2003. EOF Calculations and Data Filling from Incomplete Oceanographic Datasets. Journal of Atmospheric and Oceanic Technology, 20:18391856.

A. Alvera Azcárate, A. Barth, M. Rixen, and J. M. Beckers. 2005. Reconstruction of incomplete oceanographic data sets using Empirical Orthogonal Functions. Application to the Adriatic Sea surface temperature. Ocean Modelling, 9:325–346.

J.M. Beckers, A. Barth, and A. AlveraAzcárate. 2006. DINEOF reconstruction of clouded images including error maps. Application to the Sea Surface Temperature around Corsican Island. Ocean Science, 2(2):183–199.

 

Workshop presentation

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry