Project presentation

Polar-orbiting multispectral ocean colour sensors such as MODIS-AQUA and ENVISAT-MERIS have become well-established sources of chlorophyll a and Total Suspended Matter (TSM) data for the global oceans and coastal zones. While significant progress can still be expected for polar-orbiting multi- or hyper-spectral ocean colour sensors, two major obstacles can be identified, where optical remote sensing from a geostationary orbit provides strong advantages:

  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. With multiple observations during the day, a geostationary sensor can reduce these data gaps.
  2. Processes in coastal regions and in the deep ocean have significant variability at time scales shorter than the daily sampling of wide swath polar-orbiting sensors. Data from polar orbiting sensors thus will be contaminated by unresolved or aliased variability. During cloud-free days, a geostationary sensor is able to resolve high frequency marine processes.

GEOCOLOUR 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.

GEOCOLOUR builds on experience in geostationary remote sensing and on geostatistical analysis of remote sensing data built up in the BELCOLOUR-2 and RECOLOUR projects respectively. The key scientific questions are:

  1. What extra information can be obtained for the marine environment when the temporal resolution of ocean colour imagery is increased from daily (MODIS, MERIS, etc.) to data every 5 minutes (e.g. SEVIRI Rapid Scan)?
  2. What new algorithmic approaches are needed to fully exploit this potential?
  3. How should future geostationary ocean colour sensors be designed to maximise utility?

SEVIRI turbidity animation

In BELCOLOUR-2, (Neukermans et al. 2009) demonstrated the feasibility of processing data from the geostationary SEVIRI sensor to give high frequency (every 15 minutes) mapping of Total Suspended Matter in the Southern North Sea. The study was also considered as a precursor for future geostationary missions with dedicated ocean colour sensors, enabling early confrontation of new problems associated with this orbit, including high viewing angles and hence critical atmospheric correction problems.

Time series of diurnal variability of total suspended matter (TSM) concentration derived from SEVIRI imagery on 29 June 2006.
Time series of diurnal variability of total suspended matter (TSM) concentration derived from SEVIRI imagery on 29 June 2006. Replicated from Neukermans et al. 2009 (Neukermans G., Ruddick K., Bernard E., Ramon D., Nechad B. & Deschamps P.-Y. (2009). Mapping total suspended matter from geostationary satellites: a feasibility study with SEVIRI in the Southern North Sea. Optics Express, Vol. 17(16), pp. 14029–14052.)

In RECOLOUR, (Sirjacobs et al. 2011) used the Data Interpolating Empirical Orthogonal Functions (DINEOF) approach to analyse a 4-year archive of Total Suspended Matter, Chlorophyll a and Sea Surface Temperature data from MODIS and MERIS. DINEOF was used to a) fill in gaps in the data archive where the satellite data was missing due to problems of cloudiness or insufficient data quality, b) produce multitemporal composite maps better suited for use by marine ecosystem models, c) provide automatic flagging of outlier data to be considered either as suspect or as corresponding to extreme natural events. 

Original MERIS TSM and chlorophyll, and MODIS SST data (top row), with outlier fields (middle) and datasets filled using DINEOF (bottom row).
Original MERIS TSM and chlorophyll, and MODIS SST data (top row), with outlier fields (middle) and datasets filled using DINEOF (bottom row). Undetected clouds and cloud edge problems are highlighted in the original data and outliers field, and removed by the interpolation. Replicated from Sirjacobs et al., 2011 (Sirjacobs D., Alvera-Azcarate A., Barth A., Lacroix G., Park Y., Nechad B., Ruddick K. & Beckers J.-M. (2011). Cloud filling of ocean color and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology. Journal of Sea Research, Vol. 65, pp. 114–130. DOI: 10.1016/j.seares.2010.08.002.)