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Development of ocean color algorithms in the Mediterranean Sea

Development of ocean color algorithms in the Mediterranean Sea Rosalia Santoleri 1, , Gianluca Volpe 1 , Simone Colella 1,3 , Salvatore Marullo 2 , Maurizio Ribera D’Alcalà 3 , Vincenzo Vellucci 3. 1. 2. ENEA -CR Casaccia – Sezione Modellistica Oceanografica.

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Development of ocean color algorithms in the Mediterranean Sea

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  1. Development of ocean color algorithms in the Mediterranean Sea Rosalia Santoleri1,, Gianluca Volpe 1, Simone Colella1,3, Salvatore Marullo2, Maurizio Ribera D’Alcalà3, Vincenzo Vellucci3 1 2 ENEA -CR Casaccia – Sezione Modellistica Oceanografica Stazione Zoolologica ‘A. Dohrn’ Laboratorio di Oceanografia Biologica 3

  2. ISAC Contribution to Ocean Color activity Mediterranean high resolution surface chlorophyll mapping • Use available bio-optical data sets to estimate the uncertainties of the existing ocean colour algorithms and to define an optimal chlorophyll algorithm for the Mediterranean Sea. • Adapt the OC processing software to include selected regional algorithms and validate satellite chlorophyll estimates on the basis of in situ data. • Evaluate the uncertainties of all current global satellite chlorophyll products available from public archive (e.g. DAAC) in the Mediterranean • Reprocessing SeaWiFS dataset using the selected Mediterranean algorithm • Prepare Mediterranean gridded data compatible with model requirements.

  3. Why Mediterranean needs regional algorithm ? • Chlorophyll concentrations over the oligotrophic waters of the Mediterranean Sea are systematically overestimated when global algorithms (e.g. OC4v4) are used to convert blue-to-green reflectance ratios in to chlorophyll-a concentrations: • Gitelson et al. (Journal of Marine System, 1996) • D’Ortenzio et al. (SIMBIOS meeting January 2001) • D’Ortenzio et al. (Remote sensing of the Environment, 2002) • Bricaud et al . (Remote sensing of the Environment, 2002) • Claustre et al. (Geoph. Res. Letters, 2002) • From these works it results that global algorithms cannot be applied to-court to the Mediterranean Sea but a specific cal/val activity is needed.

  4. Mediterranean Ocean Color CAL/VAL DATA SETS 10 Mediterranean cruises from 1998 up to now were organized by ISAC in the framework of Italian National Projects Bio-optical stations Bio-optical measurements: (143 chl/opt measurement points) to define the Mediterranean regional algorithm • In water downwelling irradiance and upwelling radiance profiles using SATLANTIC SPMR • above water measurements using the SIMBAD and SIMBADA radiometer • In the bio-optical stations phytoplankton pigments distribution (HPLC and spectro-fluorometric analysis) and ancillary biological data were also acquired following NASA protocols. In situ chlorophyll-a data: (872 chl profiles) to validate SeaWiFS, Polder, MODIS, MERIS chlorophyll products and merged level 3 binned dataproduced by Mersea

  5. REGIONAL Validation of Ocean Color Algorithms OC4v4:R is log10 of either the 443/555 or the 490/555 or the 510/555 band reflectance ratios, depending on its value (the maximum is chosen) D’Ortenzio et al. 2002 (DORMA):R is log10 of the 490/555 band reflectance ratios. Bricaud et al. 2002: R is log10 of the 443/555 band reflectance ratios for Chl<0.2 (OC4v4 is used in the other cases) GLOBAL

  6. NEW MEDITERRANEAN ALGORITHM MEDOC4 : R is log10 of either the 443/560 or the 490/555 or the 510/555 band reflectance ratios. The switch from one band ratio to another one is based on the chlorophyll concentration itself (the Maximum is Chosen)

  7. SeaWiFS data validation A Match-up dataset between concurrent SeaWiFS data and in situ measurements has been contructed • SeaWiFS L1A passes corresponding to in situ observation were selected • The regional algorithms (DORMA, Bricout et al, and the new MED OC4) were inserted in the SeaDAS Code • SeaWiFS L1A passes were processed using the different algorithms:OC4v4, DORMA, Bricout, MEDOC4 • A Match-up dataset is contructed.

  8. Map of the Satellite-in situ match-up stations

  9. Merging OC data in the MED • Define the error of global OC color products at regional scale and define a strategy to take into account the regional optical properties of the MED in the Merging procedure • Define of the suitable intercalibration and merging tecnique for OC • Define an optimal interpolation algorithm that takes in to account the different characteristics of ocean colour retrieval in case I/case II waters.

  10. 10 March 2003

  11. 25 March 2003

  12. Primary production in the Mediterranean Sea from remote sensing data: a model adaptation

  13. Depth (m) Depth (m) CHL (mg m-3) CHL (mg m-3)

  14. WEST MED: 197 gc m-2 y-1 Antoine et al.,1995 EST MED: 137 gc m-2 y-1 WEST MED: 172 gc m-2 y-1 Bosc et al.,2003 WEST MED: 78-150 gc m-2 y-1 EST MED: 123 gc m-2 y-1 In situ C14 Method EST MED: 55-97 gc m-2 y-1

  15. Remote Sensed data of pigment concentration Trophic States e Morel and Berthon’s (1989) Correlations Primary Production Maps

  16. Morel and Berthon carried out relationships between Ctot vs Cpd and Ctot vs Csat. Cpd is defined as the mean pigment concentration in a layer named penetration depth Zpd=1/k where k is the attenuation coefficient for the downwelling PAR irradiance: Zpd can be estimated from Ze on the base of the relation: Zpd=Ze/4.6 . Instead, Csat is defined as the weighted mean pigment concentration in the layer Zpd: Csat is very similar to the Optically Weighted Pigment (OWP) concentration considered as the best representation of what satellite sensors can measurement.

  17. In situ bio-optical data has been used to verify the relationship between the euphotic depth (Ze) and total pigment concetration insite this layer.Ze: depth where the PAR (Photosynthetic Active Radiation) become 1% of the surface value

  18. = Ctot=40.6 Cpd0.46 Ctot=40.6 Csat0.459 ≠ Ctot=54.679 Cpd0.6532 Ctot=48.13 Csat0.627

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