367ee473-f681-4af5-912a-f237c75c4c8e Copernicus Marine Service product specification CMEMS servicedesk.cmems@mercator-ocean.eu http://marine.copernicus.eu/ 2022-06-29T09:01:23.049Z ISO 19139, MyOcean profile 0.2 2 0.3 0.3 false WGS 84 / Plate Carree (EPSG:32662) EPSG [ARCHIVE] Mediterranean Sea Surface Chlorophyll Concentration from Multi Satellite and Sentinel-3 OLCI observations OCEANCOLOUR_MED_CHL_L3_NRT_OBSERVATIONS_009_040 2013-03-13 2.1 2021-04-26 367ee473-f681-4af5-912a-f237c75c4c8e '''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR), in Rome, distributes surface chlorophyll concentration (mg m-3) derived from multi-sensor (MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, and Sentinel3A-OLCI at 300m of resolution) (at 1 km resolution) and Sentinel3A-OLCI (at high resolution, 300m) Rrs spectra. Chlorophyll datasets are obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 waters, Berthon and Zibordi, 2004). Discrimination between the two water types is performed by comparing the satellite spectrum at pixel-by-pixel level with the average water type spectral signature from in situ measurements for both water types. Reference insitu dataset is MedBiOp (Volpe et al., 2019) where pure Case II spectra are selected using a k-mean cluster analysis (Melin et al., 2015). Merging of Case 1 and Case 2 information is performed estimating the Mahalanobis distance between the observed and reference spectra and using it as weight for the final merged value. This product identifies the average chlorophyll content of the surface layer as defined by the first optical depth (roughly one fifth of the euphotic depth). For multi-sensor observations, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. The current day data temporal consistency is evaluated as Quality Index (QI): QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). '''Processing information:''' Multi-sensor products are constituted by MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI. For consistency with NASA L2 dataset, BRDF correction was applied to Sentinel3A-OLCI prior to band shifting and multi sensor merging. Hence, the single sensor OLCI data set is also distributed after BRDF correction. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution (300m for OLCI) using cylindrical equirectangular projection. Afterwards, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. This technique is developed by The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR, Rome). Then geophysical fields (i.e. chlorophyll, kd490, bbp, aph and adg) are estimated via state-of-the-art algorithms for better product quality. '''Description of observation methods/instruments:''' Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour, which is affected by the presence of phytoplankton. '''Quality / Accuracy / Calibration information:''' A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''Dataset names:''' *dataset-oc-med-chl-multi-l3-chl_1km_daily-rt-v02 *dataset-oc-med-chl-olci-l3-chl_300m_daily-rt-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant '''DOI (product) :''' https://doi.org/10.48670/moi-00111 E.U. Copernicus Marine Service Information Rosalia SANTOLERI OC-CNR-ROMA-IT rosalia.santoleri@cnr.it Emanuele BÖHM OC-CNR-ROMA-IT gsdk@isac.cnr.it Gianluca VOLPE OC-CNR-ROMA-IT gianluca.volpe@cnr.it Simone COLELLA OC-CNR-ROMA-IT simone.colella@cnr.it Javier CONCHA OC-CNR-ROMA-IT gsdk@isac.cnr.it / 20:00 https://catalogue.marine.copernicus.eu/documents/IMG/OCEANCOLOUR_MED_CHL_L3_NRT_OBSERVATIONS_009_040.png OCEANCOLOUR_MED_CHL_L3_NRT_OBSERVATIONS_009_040 Oceanographic geographical features GEMET - INSPIRE themes, version 1.0 2009-09-22 satellite-observation Discipline 2017-04-25 geonetwork.thesaurus.local.discipline.myocean.discipline mass_concentration_of_chlorophyll_a_in_sea_water sensormask Climate and Forecast Standard Names 2009-06-12T00:00:00 near-real-time Temporal scale 2009-06-12 geonetwork.thesaurus.local.temporal-scale.myocean.temporal-scale coastal-marine-environment weather-climate-and-seasonal-forecasting marine-safety marine-resources Area of benefit 2009-09-09 geonetwork.thesaurus.local.area-of-benefit.myocean.area-of-benefit mediterranean-sea Reference Geographical Areas 2009-09-09 geonetwork.thesaurus.local.reference-geographical-area.myocean.reference-geographical-area Level 3 Processing level 2021-07-26 geonetwork.thesaurus.local.processing-level.myocean.processing-level Not Applicable Model assimilation 2016-02-03 geonetwork.thesaurus.external.theme.myocean.model-assimilation See Copernicus Marine Environment Monitoring Service Data commitments and licence at: http://marine.copernicus.eu/web/27-service-commitments-and-licence.php No limitations on public access 7bdc830b-6321-4ffb-ae76-8707256c6d03 e0f8a482-d960-4c72-8976-8c63873ea4de Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004 2019-05-08 Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772. 2019-05-08 Mélin, F., & Vantrepotte, V. (2015). How optically diverse is the coastal ocean?. Remote Sensing of Environment, 160, 235-251 2019-05-08 Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146. 2019-05-08 eng oceans bounding box -6.00 36.50 30.00 46.00 daily-mean 2016-04-25 0 0 display priority: 11710 observation temporal resolution: daily mean vertical level number: 1 false Grid NetCDF-4 Classic model COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification The myOcean products depends on other products for production or validation. The detailed list of dependencies is given in ISO19115's aggregationInfo (ISO19139 Xpath = "gmd:MD_Metadata/gmd:identificationInfo/gmd:aggregationInfo[./gmd:MD_AggregateInformation/gmd:initiativeType/gmd:DS_InitiativeTypeCode/@codeListValue='upstream-validation' or 'upstream-production']")