Validation of the GlobColour Full product set ( FPS ) over open ocean Case 1 waters

1. Validation of the GlobColour Full product set (FPS) over open ocean Case 1 waters David Antoine Laboratoire d’Océanographie de Villefranche Inputs from the GlobColour team Special thanks to Gilbert Barrot, Julien Demaria and Christophe Lerebourg

2. Objectives - questions • Are the overall geographical distributions valid in the merged data set? (e.g., any artificial boundaries?) • Are the statistics derived from the match up analysis of the FPS with field data at least not worst (and hopefully better) than the individual-sensors’ statistics? • Is the data set usable for delivery of operational services (GMES-MCS) and for “carbon cycle research”? • Recommendations for the next steps

3. Plan • Examination of global composites • Validation against field data (OBPG, NOMAD, BOUSSOLE) • Looking at time series and histograms over selected areas • A bit more sophisticated analyses as well (distributions, anomalies) • Conclusions • Recommendations

4. The GlobColour product list • Chlorophyll-a concentration (Chl-a) derived from reflectance ratios or, for the GSM method, from aph • Error bar on the Chlorophyll concentration (GSM) • Colored dissolved + particulate (“detrital”) organic matter (CDM) either for MERIS or from the GSM01 method • Particle backscattering at 443 nm (bbp443) from the GSM algorithm • Total suspended matter (TSM) • Diffuse attenuation coefficient for downward irradiance (Kd490) Chl-based algorithm (original Kd’s from SeaWiFS and MODIS are not used) • Fully normalized water leaving radiances (nLw’s) • Excess of radiance at 560 nm (turbid Case 2 waters) • Photosynthetically available radiation (PAR) • Aerosol optical thickness • Cloud fraction • Data quality flags

5. The GlobColour products we have considered in the open ocean validation (in red) • Chlorophyll-a concentration (Chl-a) derived from reflectance ratios or, for the GSM method, from aph • Error bar on the Chlorophyll concentration (GSM) • Colored dissolved + particulate (“detrital”) organic matter (CDM) either for MERIS or from the GSM01 method • Particle backscattering at 443 nm (bbp443) from the GSM algorithm • Total suspended matter (TSM) • Diffuse attenuation coefficient for downward irradiance (Kd490) Chl-based algorithm (original Kd’s from SeaWiFS and MODIS are not used) • Fully normalized water leaving radiances (nLw’s) • Excess of radiance at 560 nm (turbid Case 2 waters) • Photosynthetically available radiation (PAR) • Aerosol optical thickness • Cloud fraction • Data quality flags

6. 1st step: looking over some global composites Examples for may 2006

7. nLw(412), may 2006

8. nLw(443), may 2006

9. nLw(490), may 2006

10. nLw(555), may 2006

11. Chlorophyll (weighted average), may 2006

12. Chlorophyll (GSM), may 2006

13. GSM Bbp(443), may 2006

14. GSM CDM, may 2006

15. Kd(490), may 2006 Kd(490) = 0.0166 + 0.0835[Chl]0.633 (Morel et al., 2007)

16. 2nd step: match ups with field data (NOMAD + OBPG + BOUSSOLE)

17. Matchups with the merged products 1 – NASA’s OBPG data set Location of the OBPG validation dataset used for the GlobColour Level-3 validation

18. Matchups with the merged products 2 – NASA’s NOMAD data set Werdell and Bailey, 2005: An improved bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment , 98(1), 122-140. First contributed by the NASA SIMBIOS Program (NRA-96-MTPE-04 and NRA-99-OES-09)

19. Matchups with the merged products 3 – BOUSSOLE data set 3 years of data From Sept. 2003 to Sept 2006 nLw’s 412, 443, 490, 510, 560, 665, 681 nm HPLC TChl-a during monthly servicing cruises

20. Matchups using all three data sets

21. Matchups using all three data sets

22. Matchups using all three data sets

23. Matchups using all three data sets: overall statistics

24. Do we perform better after the merging process ? Evolution of the statistical indicators from the individual-sensors’ products to the merged products The answer is definitely YES

25. 3rd step: 9-year time series Overall consistency, trends? Jumps?

26. Analysis of time series: the selected areas

27. Analysis of time series: global ocean (50S-50N, depth>1000m)

28. Analysis of time series: South east Pacific

29. Analysis of time series: Mediterranean Sea

30. Analysis of time series: Chl at the BOUSSOLE site only

31. Analysis of time series: bbp(443) at the BOUSSOLE site only

32. Analysis of time series: Main results from what we have seen and from the other areas as well • - The best agreement between the 3 sensors is for L(490) • GSM Chl is always larger for MERIS than for MODIS-A & SeaWiFS • GSM Chl is often smaller than the weighted average Chl • L(555) is often smaller for MODIS-A than for MERIS and SeaWiFS, and this is due to lower values for clear waters. It is also “flatter” (less seasonality) in many occasions. The average value (0.3 mW cm-2mm-1 sr-1 is, however, closer to the “clear water radiance” (Gordon and Clark, 1981). • Good results for the preliminary validation of the bbp(443) at BOUSSOLE • The merged data set is close to the SeaWiFS data set

33. 4th (and last) step: global Chl distributions and Global Chl “anomalies”

34. Mesotrophic Oligotrophic Eutrophic 52% 44% 38% Global chlorophyll distributions 51% 43% 38%

35. Global chlorophyll “anomalies” Differences between the global Chl stock (mgChl m-2) of a given month and the same stock for the corresponding “climatological month”, i.e., the average for this month over 9 years (1998-2006) From the Behrenfeld et al. (2006) paper in Nature Chl from the weighted average GSM Chl Which one is right? Raises the question about the validity of the FPS for long-term analyses

36. Conclusions (1/3) - Based on the match up with the global data set of field data (Chl and nLw’s), the GlobColour FPS is validated. - The statistics favourably evolve for the normalized water-leaving radiance in the blue bands (412 and 443 nm), as compared to what they are for the individual sensors - In terms of Chl, the statistics for the GSM Chl are a little better than those for the product from the weighted average. - The normalized water-leaving radiance at 490 nm is by far the most homogeneous product among the 3 sensors, so the confidence in the merged product is higher for this peculiar band - The MODIS-A L(555) is often smaller than the L(555) for the two other sensors - The merged product has not degraded the situation as compared to each of the 3 single-sensor data set

37. Conclusions (2/3) - The good results in term of global match ups are somewhat fortuitous, however, and may often result from compensating effects, in particular between MODIS-A and MERIS. - The GlobColour FPS is often close to the SeaWiFS data set alone. - This is not totally satisfactory: the merged data set would not be validated in case another sensor, with its specific bias, would be added, or if one the presently used sensor would be removed from the merging process. - This is not due to the merging process, but to remaining uncertainties in the vicarious calibration of the various ocean color sensors, and to differences in algorithms (atmospheric corrections in particular)

38. Conclusions (3/3) • - The GlobColour FPS is definitely qualified and usable for operational uses, such as assimilation into global models (there is a pixel-by-pixel error bar delivered with the GSM Chl), or delivery of services. • The GlobColour FPS is not yet qualified to perform temporal analysis over the period 1998-2007. • In other words, the GlobColour FPS doesn’t yet meet the standards for being qualified as a “climate quality data record”

39. Recommendations - We need several in situ long-term time series, in order to add the temporal dimension to our statistical analyses (match ups). - An international effort is still needed to standardize & improve the vicarious calibration methodologies. We are still not at the desired level of confidence (see, e.g., Ohring et al., EOS Trans AGU, 88(11), 13 march 2007) - Establish a collaborative frame between space Agencies (ESA, NASA and others), so that vicarious calibration and related issues (atmospheric corrections) can be standardized. This may need a specific body where these issues are discussed and the methods are implemented (see, e.g., The GHRSST). - Incorporate new approaches, for instance where the TOA level-1 observations of all instruments are processed the same way (same algorithm), which also means that they are all vicariously calibrated against the same standard. It might become obvious at some point that this approach is mandatory if one thinks to CDRs (“climate quality data records”).

40. Thank you for your attention

41. Statistical measures (indicators) Coef. of determination

42. The full product set “FPS” • 10 years of global data from the 3 sensors • Systematic application of the 2 merging methods (weighted average and GSM) • Generation of all products