Regional objective analysis for merging high resolution MERIS, MODIS/Aqua and SeaWiFS Chlorophyll-a data from 1998 to 2008 on

In this paper we define the method used to merge high resolution multi-sensor chlorophyll-a data on the Ireland-Biscay-Iberia Regional Ocean Observing System (IBIROOS) area from 1998 to present at a resolution of 1.1 km. The method is based on geostatistics and is known as kriging. The merged variable is the daily anomaly of chlorophyll-a, the anomaly being defined as the difference between the daily image and the mean historical field for the considered day. For each day, the continuous anomaly is generated using the kriging method and the mean historical field is then added to obtain the cloudless field of chlorophyll-a. The initial satellite chlorophyll-a dataset used in the merging procedure is derived from the daily level-2 water leaving radiances of three ocean color sensors: the Sea-viewing Wide Field of View Sensor (SeaWiFS) on the Orbview platform, the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua platform, and the Medium Resolution Imaging Spectrometer Instrument (MERIS) on the ENVISAT platform. The chlorophyll-a concentration is obtained using a specific algorithm developed by Ifremer, known as OC5 product. Before merging, each satellite-derived chlorophyll-a product has been compared to in-situ data and validated using a matchup dataset. After this validation against in-situ data, inter-comparisons between the satellite datasets have been performed. As the chlorophyll-a anomaly is not stationary on the whole area, local space-time semi-variograms have been calculated. The semi-variograms are used to estimate the spatio-temporal dependence of the anomalies and are defined by their nuggets (noise), their spatial and temporal range (maximum distance for a non null covariance between the anomalies) and their sill (maximum variance). The spatial range of the semi-variograms have been approximated using local estimations on a regular grid. The nuggets and the sills have been deduced from the square of the mean of the chlorophyll-a concentration (the historical mean reference) as we have observed a classical proportionality effect between the square of the chlorophyll-a mean, the variance of the distribution, and the parameters of the semivariograms. Compared to each original product, the analysis shows a complete coverage and statistical comparisons with the in-situ equivalent to those observed with the initial satellite dataset. The merged product offers number of applications for environmental monitoring such as the monitoring of the eutrophication risk required by the Water Framework Directive of the European Union.