“Regional” adjustments of SAA parameterization

1. “Regional” adjustments of SAA parameterization Mark Dowell & Timothy Moore EC-JRC NURC & UNH

2. Definitions • “Regional”: • Optical classification: • Classes: • Provinces:

3. Elements for Discussion (1/2) • Do we need “regional” SAA parameterization to make further progress ? • Do we need regional or optical class based parameterizations ? – how do we make this decision ? • Should effort be made not only for the IOP subcomponent models but also for the bulk IOP relationships (e.g. VSF variability)? • Should such methods be applicable independent of the inversion method (i.e. different inversion methods can be used for different optical water types)? • Will class-based approaches improve “ambiguity” issues associated with SAAs?

4. Elements for Discussion (2/2) • Are existing global datasets “clean” enough to parameterize sub-component models at the scale of a class? • What would be acceptable as far as increased processing time, for a class based approach to be included in operational processing ? • Do we need additional statistics for assessing class based approaches ? • If we base our classifications on in-situ AOPs how do we incorporate the uncertainties associated with satellite derived nLw when mapping province distributions?

5. Alternatives for SAA parameterizations(after Platt & Sathyendranath 2001) • Constant • i.e. constant aph* globally • Piecewise • i.e. different aph* for different geographic regional or “hard” optical classes • Continuous • i.e. aph* as function of unknown – aph*-f(CHL) • Piecewise continuous • Through mebership function and aph*-f(CHL)

6. IOP sub-component model parameterization • aph*(l) • Sgd • Slope of bbp • In a spectral un-mixing algorithm many opportunities at intermediate steps. • Also Kd & Zeu models etc.

7. Benefits • Flexible framework for updating algorithms based on availability of new “regional” datasets. • Feedback mechanism in prioritizing fieldwork for validation and parameterizations. • Avenue into spatial uncertainty analysis • Also of benefit for parameterizing new products from IOP e.g. POC, DOC

8. Rationale • There is necessity to describe a considerable amount of variability in Inherent Optical Property (IOP) subcomponent models. • This is particularly true, if inversion algorithms are to be applicable at global scale yet remain quantitatively accurate in both the open ocean and coastal/shelf seas. • This is unlikely to be achieved in the foreseeable future, with a single representation of IOP subcomponents. • BEAM – Case2R • The proposed approach is an algorithm framework more than a specific algorithm.

9. The approach undertaken adopts fuzzy logic to define and identify, in radiance space, distinct bio-optical provinces that implicitly reduce the variance in the IOP subcomponent models. Fuzzy Hard Fuzzy graded membership Traditional minimum-distance criteria Forest Forest Reflectance Band 2 Reflectance Band 2 Wetland Wetland Water Water Water = 0.05 Wetland = 0.65 Forest = 0.30 Unknown measurement vector Mean class vector 0 10 20 30 0 10 20 30 Reflectance Band 1 Reflectance Band 1

10. c Satellite Measurements c Rrs() Individual class derived products Calculate membership c c Merged Product In-situ Database Sgd, aph*,……. Rrs() c c 8 classes Cluster analysis Station data sorted by class c c Class based relationships Class Mi, Si

11. Advantages of fuzzy logic defined provinces • They allow for dynamics both seasonal and inter-annual in the optical properties of a given region. • They address the issue of transitions at the boundaries of provinces (through the fuzzy membership function of each class) thus resulting finally in the seamless reconstruction of a single geophysical product.

12. 8 objectively identified classes in radiance space

13. May 2004 SeaWiFS Composite Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

14. High resolution provinces for European Seas Med May 2004 Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

15. Channel 1-5 Channel 2,3,5 MERIS MODIS/Aqua May 2004 SeaWiFS Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

16. Chlorophyll agd(443) Rrs(665)

17. Inversion Methods • Non-linear optimization (Amoeba) - a la GSM • Spectral Unmixing - a la Lee et. al. QAA • ……Neural Network , PCI Note: there is no need for the same inversion method to be applied to each class

18. Class-based NLO (e.g. GSM) • Sgd varies based on class • [0.0180,0.0164,0.0139,0.0147,0.0153,0.0128,0.0138,0.0121] • aph*() varies dependent on class • Y (i.e. slope of bbp) using Carder’s relationship One could imagine applying a tuning algorithm (e.g. simulated annealing) to each class to determine optimimal class based model coefficients.

20. Amoeba - NLO

22. Class-based version of QAA • Sgd variable based on class • at(443) versus rrs(443)/rrs(555) class based • at(555) versus at(443) class based • aph(443) versus Chl class based aph*(443)

24. a555 class parameterization N=41 N=59 N=110 N=169 N=71 Class-specific a555-rho parameterization: QAA

25. RMS Error – NOMAD in situ data Relative Error (%) N=391

26. Chlorophyll a – Aug. 31, 2006

27. Chlorophyll a – Aug. 31, 2006

28. Hard classes Aug. 31, 2006 MERIS MODIS

29. MERIS fuzzy classes – March 23, 2008

30. Spectral Unmixing Amoeba - NLO

31. Conclusions • Fuzzy logic based dynamic provinces provide a powerful tool for describing the optical variability of the world oceans. • Statistically rigorous means of parameterizing bio-optical models. • Allow to produce algorithms capable of describing the strong non-linearity of optical variability across many decades of variability • Various inversion methods have been/ are being tested - not all classes need to adopt the same inversion scheme. • Uncertainty estimate with the approach can benefit from the availability of the membership functions. • Ongoing work to identify bio-optical “end-member” locations for use in identifying cal/val sites, as well as identifying “under-sampled” optical water types.