1. New Fluorescence Algorithms for the MERIS Sensor Yannick Huot and Marcel Babin Laboratoire d’Océanographie de Villefranche Antoine Mangin and Odile Fanton d'Andon ACRI-ST 28 September 2005 Research funded by: A fellowship from the Natural Sciences and Engineering Research Council (NSERC)

2. What is fluorescence and why would one care?

3. Phytoplankton fluorescence Just outside the cells: Fluorescence volume flux Quantum yield of fluorescence Chlorophyll concentration Chlorophyll specific absorption Reabsorption parameter Scalar Irradiance

4. Two products of interest The Biomass Index of physiological status Today we are developing algorithms for these two products applicable to case 1 waters May be useful in regions where the chlorophyll concentration cannot be obtained with standard ocean colour algorithms Processes studies in case 1 waters

5. Measurement optics Terms from previous page Subsurface upwelling radiance due to fluorescence Geometrical factor Attenuation of downwelling and upwelling light

6. Adapting recently published MODIS algorithms to MERIS

7. The two products Fixed quantum yield Measured chlorophyll concentration

8. Satellite algorithms: – : MERIS PAR –Chl: MERIS case 1, blue to green ratio algorithm –L uf (0 - ): Transform from  w to L u and baseline method –K d (490): “Improved” blue to green ratio algorithm Case 1 waters relationships functions of K d (490) – versus measured K d (490); Bricaud et al. 1998 statistics, vs. chl Morel et al. 2001, K d (490) vs. chl Concept of the algorithm

9. WARNING: For today’s presentation some approximations are made that would not be necessary in standard algorithms.

10. Scalar irradiance MERIS product gives E d (0 -,PAR) –For fluorescence we want We assumed: 1) Upwelling irradiance negligible 2)  d (0 - ) = 0.75 for the whole scene We thus use:

11. Satellite algorithms: – : MERIS PAR –Chl: MERIS case 1, blue to green ratio algorithm –L uf (0 - ): Transform from  w to L u and baseline method –K d (490): “Improved” blue to green ratio algorithm Case 1 waters relationships functions of K d (490) – versus measured K d (490); Bricaud et al. 1998 statistics, vs. chl Morel et al. 2001, K d (490) vs. chl Concept of the algorithm

12. Going from  w to L u Problem: MERIS algorithm returns  w : Atmospheric transmission (t d ) has to be approximated when one doesn’t have access to the processing chain intermediate products (probably a small error) to calculate a quantum yield we need L u (0 - ): First step to L w (0 + ) Second step to L u (0 - )

13. The baseline method MERIS bands dedicated to the natural fluorescence measurements are: –665, 681, and 709 nm (bands 7, 8, 9) This approximation is good in case 1 waters (Huot et al. 2005) but great care must be taken in case 2 waters (see next talk by Babin and Huot)

14. Concept of the algorithm Satellite algorithms: – : MERIS PAR –Chl: MERIS case 1, blue to green ratio algorithm –L uf (0 - ): Transform from  w to L u and baseline method –K d (490): “Improved” blue to green ratio algorithm Case 1 waters relationships functions of K d (490) – versus measured K d (490); Bricaud et al. 1998 statistics, vs. chl Morel et al. 2001, K d (490) vs. chl

15. K d 490: A “MERIS” algorithm NOMAD dataset: see Werdell, P.J. and S.W. 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. Thank you to all contributors… See also: http://oceancolor.gsfc.nasa.gov/REPROCESSING/Aqua/R1.1/

16. Checking the K d (490) algorithm Morel and Maritorena 2001 Best fit polynomial -The two algorithms are consistent, with some bias at high chl -Waters examined do not depart strongly from case 1 relationships

17. Concept of the algorithm Satellite algorithms: – : MERIS PAR –Chl: MERIS case 1, blue to green ratio algorithm –L uf (0 - ): Transform from  w to L u and baseline method –K d (490): “Improved” blue to green ratio algorithm Case 1 waters relationships functions of K d (490) – versus measured K d (490); Bricaud et al. 1998 statistics, vs. chl Morel et al. 2001, K d (490) vs. chl

18. Case 1 water relationships Bricaud, A., H. Claustre, J. Ras, and K. Oubelkheir. 2004. Journal of Geophysical Research 109: C11010,doi:11010.11029/12004JC002419. Morel, A., and S. Maritorena. 2001. Journal of Geophysical Research 106: 7163-7180. A little algebra: A little more algebra: Some calculus and a numerical model:

19. Let’s apply it on an image now…

20. Noise in the 681nm band Scene from the Benguela upwelling region measured on July 14, 2003, Second reprocessing.

21. Measured by MERIS

22. Chlorophyll algorithm Best fit 1:1 line

23. Chlorophyll algorithm map

24. Quantum yield algorithm

26. Comparison with MODIS:  f MERIS MODIS -Very noisy -Hard to use presently -However, much of the noise is not random and it may be possible to correct for it - Quantum yield too high?

27. Are we measuring something real? No clear reason for this trend Consistent with non- photochemical quenching

28. Are we measuring something real? Answer: Perhaps, but what?

29. Conclusion We proposed two algorithms for MERIS fluorescence bands –One for chlorophyll –One for the quantum yield MERIS band at 681 nm is more noisy than the 665 and 709 bands Algorithms need to be fully validated but preliminary results are encouraging Future prospects We hope to implement the algorithms with intermediate products of the processing chain to avoid the limitations of the level two products. We are testing iterative fluorescence algorithms using only the fluorescence bands for the retrieval of chlorophyll.

30. Future prospects : first glimpse FLH Today’s algorithm Fluorescence bands only It’s potential will depend on our ability to reduce the noise observed in the 681 nm channel

31. Thanks to David Antoine Norman Fomferra André Morel