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

2. Elements for Discussion (1/2) 1.Do we need “regional” SAA parameterization to make further progress ? 2.Do we need regional or optical water class based parameterizations ? – how do we make this decision ? 3.Should effort be made not only for the IOP subcomponent models but also for the bulk AOP-IOP relationships (e.g. VSF variability)? 4.Should such methods be applicable independent of the inversion method (i.e. different inversion methods can be used for different optical water types)? 5.Will class-based approaches improve “ambiguity” issues associated with SAAs?

3. Elements for Discussion (2/2) 6.Are existing global datasets “clean” enough to parameterize sub-component models at the scale of a class? 7.What would be acceptable as far as increased computation time, for a class based approach to be included in operational processing ? 8.Do we need additional statistics for assessing class based approaches ? 9.If we base our classifications on in-situ AOPs how do we incorporate the uncertainties associated with satellite derived nLw when mapping province distributions?

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

5. 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.

6. 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

7. 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. The proposed approach is an algorithm framework more than a specific algorithm.

8. 0102030 Forest Wetland Water Reflectance Band 1 Reflectance Band 2 Mean class vector Unknown measurement vector Traditional minimum-distance criteria Hard 0102030 Forest Wetland Water Reflectance Band 1 Reflectance Band 2 Fuzzy graded membership Water = 0.05 Wetland = 0.65 Forest = 0.30 Fuzzy 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.

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

10. 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.

11. 8 objectively identified classes in radiance space

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

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

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

15. Chlorophyllagd(443) Rrs(665)

16. 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

17. Class-based NLO (e.g. GSM) Sgd varies based on class [0.0180,0.0164,0.0139,0.0147,0.0153,0.01 28,0.0138,0.0121] 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.

19. Amoeba - NLO

21. Class-based version of QAA Sgd variable based on class a t (443) versus r rs (443)/r rs (555) class based a t (555) versus a t (443) class based a ph (443) versus Chl class based a ph *(443)

23. Hard classes Aug. 31, 2006 MERISMODIS 1 2345678

24. Class 2 MODISMERIS

25. a555 images – MODIS Fuzzy QAAQAA 0.01 0.04 0.15 0.55 2.11 8.00

26. MERIS/MODIS a555 – coastal MERIS MODIS 0.01 0.04 0.15 0.55 2.11 8.00

27. Amoeba - NLO Spectral Unmixing

28. 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. Conclusions