Research project SR/00/139 (Research action SR)
Context and objectives
The GEOCOLOUR project is a spin-off of the BELCOLOUR-2 project and has the general objective of improving the quality and quantity of marine optical products from the existing SEVIRI geostationary sensor and to prepare the design of the next generation of geostationary ocean colour sensors.
Polar-orbiting multispectral ocean colour sensors such as MODIS-AQUA have become well-established sources of chlorophyll a data for the global oceans and for coastal zones. While significant further progress can still be expected for polar-orbiting multi- or hyper-spectral ocean colour sensors, two major obstacles can be identified:
1. Cloudiness and/or sunglint in many regions reduces the data availability from typically once per day (e.g. mid-latitude MODIS-AQUA) to significantly less.
2. Processes in many regions have significant variability at time scales shorter than the daily sampling frequency of polar-orbiters. Tidal resuspension and advection of particulate matter may give variability of a factor of two or more during a day, which, if unresolved or aliased, will severely contaminate data from once-daily sampling.
GEOCOLOUR will investigate and develop the potential of optical remote sensing from geostationary sensors to: a) increase very significantly the availability of data during periods of scattered clouds, and b) provide information on processes occurring at subdiurnal time scales during cloudfree periods.
Methodology
Starting from the propotype algorithm of [Neukermans et al, 2009, Optics Express, 17(16)], the processing of MSG/SEVIRI will be improved by reduction of uncertainties in the atmospheric correction and in the TSM algorithm, by use of the HRV band to improve spatial resolution and by adding an algorithm for euphotic depth. Data will be processed for multi-year archive of the North Sea and will be generalised to allow automated processing of other subregions of the full earth disk seen from (0°N, 0°E). Products will be qulity controlled by matchup validation against in situ measurements and by intersensor cmparison (with MODIS and MERIS). A geostatistical analysis will be performed of the resulting dataset using the DINEOF technique [Sirjacobs et al, 2010, Journal of Sea Research (2011), 65(1), 114-130], giving cloud-filled TSM maps with corresponding error maps. This analysis will include the tidal variability that is cotained in such a high frequency satellite dataset.
