Page 9GlobColour CDR Meeting – July 10-11, 2006, ESRIN NOMAD In Situ Data Conversion to Fully Normalised Water Leaving Radiance OBPG nLw GlobCOLOUR nLw 412443490 510555670
Page 10GlobColour CDR Meeting – July 10-11, 2006, ESRIN Data Processing L2 (M)LAC L3-DDS Generator In-situ meta L3 DDS In-situ data In GC-NOMAD template DDS Match-Up Time diff <24 hrs Location diff <=0.02° NO match-up NO match-up Extract 3x3 kernel L3-DDS Reader GC in-situ Reader Y N Import to Excel Stat Template N Preparation/Generation Extraction Statistics/Result T diff < 24 hrs FLAG !=NoData T pix > 5 Match-up Result Y Exclude N
Page 11GlobColour CDR Meeting – July 10-11, 2006, ESRIN Data Processing Number of generated DDS
Page 19GlobColour CDR Meeting – July 10-11, 2006, ESRIN Normalised Water Leaving Radiance Further discussion and analysis is occurring with respect to the derivation of in-situ normalised water leaving radiances as this is a key step in the characterisation process. Propose that this work should be ongoing and the characterisations will be updated as additional insitu data becomes available. The results presented so far indicate that it is particularly important to seek out datasets with high normalised water leaving radiances.
Page 21GlobColour CDR Meeting – July 10-11, 2006, ESRIN DJF V2.0 Coastal Waters Kai Sørensen and Jo Høkedal
Page 22GlobColour CDR Meeting – July 10-11, 2006, ESRIN Coastal waters - Guianas Coast MERSEA-IP The provinces, Guianas Coastal (GUIA) and Guinea Current Coastal (GUIN) are both coastal stripes influenced by land and river inputs. On the African side (GUIN) there is also a strong impact of atmospheric conditions (cloud coverage, biomass burning and desert dust aerosols) on the ocean colour products. The two provinces are characterized by the largest differences of the provinces (in this study) between sensor products. Between SeaWiFS and MODIS–Aqua the differences (defined as the root mean square relative difference) was as high a 21.3 % and 24.7 % on average for GUIA and GUIN, respectively. The differences compared to MERIS are 3-4 % higher.
Page 23GlobColour CDR Meeting – July 10-11, 2006, ESRIN Coastal water - Baltic Sea MERSEA-IP and FerryBox-EU An optically complex water with a high load of CDOM, and summer blooming of Cyanobacteria causing large changes in the IOPs. An average difference of MERIS vs SeaWiFS or MODIS-Aqua of around 25%, while between SeaWiFS and MODIS-Aqua of 19.2 %. MERIS Algal_1 and Algal_2 show erroneous data in the bloom, but Algal_2 after the 2nd processing gave better agreement. Even if the MERIS Neural Network Case 2 products can be trained for this area it will be problematic due to the high IOP variability. The validation will also be a challenge during such extreme blooms.
Page 24GlobColour CDR Meeting – July 10-11, 2006, ESRIN North Sea – Skagerrak Case1 Chl-a Algorithms, Folkestad, 2005 R2R2 Sensors compared (small areas of 25 pixels) All stations Without #7 MODIS/Aqua vs MERIS 0.600.76 SeaWiFS vs MERIS 0.150.44 SeaWiFS vs MODIS/Aqua 0.820.91 MODIS/Aqua vs MERIS SeaWiFS vs MERIS SeaWiFS vs MODIS/Aqua
Page 25GlobColour CDR Meeting – July 10-11, 2006, ESRIN MERIS Skagerrak (2nd processing) Sørensen, 2006. MERIS Algal_2 vs Chl-a_HPLC MERIS Algal_2 binned one month vs Chl-a fluorescence from the Ferrybox systems (+/- 1. Stdev.dev.) Central Skagerrak Danish Coast Oslo Fjord
Page 26GlobColour CDR Meeting – July 10-11, 2006, ESRIN Coast and Open Sea – Spatial variability Vertical bars: Max-min
Page 27GlobColour CDR Meeting – July 10-11, 2006, ESRIN Summary It is clear from the findings by many authors that SeaWiFS and MODIS do not resolve the true values in Case 2 water and that multivariate complex Case 2 waters need to have complex algorithms e.g. MERIS NN. It is presently difficult to give any recommendation on how to solve the issue of combining data from different sensors in coastal water without dealing with all the Case 2 problems. The only combining possibilities is then to merge MERIS Case 2 products with Case 1 products, but boundaries will probably be present. Alternative are to use Case 1 algorithms into the coast and flag Case2 water. To be discussed.
Page 28GlobColour CDR Meeting – July 10-11, 2006, ESRIN Sensor Cross Characterisation Antoine Mangin
Page 29GlobColour CDR Meeting – July 10-11, 2006, ESRIN Cross characterisation Cross comparison between MERIS/MODIS/SeaWifs – attempt to detect systematic biases: At global scale and regional scale Check of the consistency with JRC results Harmonisation of Kd algorithm
Page 30GlobColour CDR Meeting – July 10-11, 2006, ESRIN Cross comparison between MERIS/MODIS/SeaWifs – attempt to detect systematic biases: At global scale and regional scale comparison
Page 31GlobColour CDR Meeting – July 10-11, 2006, ESRIN March 030609 12 030609 12 Slope of the regression Determination coeff. r 2 Mediterranean Summary for Mediterranean
Page 32GlobColour CDR Meeting – July 10-11, 2006, ESRIN 030609 12 030609 12 Slope of the regression Determination coeff. r 2 Global Summary for Global results
Page 33GlobColour CDR Meeting – July 10-11, 2006, ESRIN From JRCs assessment: Global Regional: very fluctuant, seasonal dependency – in agreement with our daily results Confrontation with other sources There is a bias between sensors
Page 34GlobColour CDR Meeting – July 10-11, 2006, ESRIN Either…. …or…. We get a faithful caracterisation of bias wrt season and region and correct for it prior to merging. We anticipate the impact of using biased data. We apply inputs as is. The impact will be reflected into the error bar estimates wrt to season/region Not mature enoughRecommended
Page 35GlobColour CDR Meeting – July 10-11, 2006, ESRIN Harmonisation of Kd algorithm Kd
Page 36GlobColour CDR Meeting – July 10-11, 2006, ESRIN Overall Conclusions Used some large databases and produced a large number of DDS files (1387), but as is often the case with ocean colour data the number of match-up points is considerably smaller than the number of original insitu points. The characterisation will undergo additional work within the next couple of months to tie up the loose ends and come to a final set of conclusions. For now the merging will use the following characterisation results: normalised water leaving radiance: GlobCOLOUR chlorophyll: NASA (will split GlobCOLOUR into low/high groupings) diffuse attenuation coefficient: GlobCOLOUR For Case 2 waters, a decision on the alternatives of using (1) MERIS Case2 products for the coast or (2) using Case1 products only with flagging information must be taken.