Optical Water Type Classification in GIOP

1. The role of Optical Water Type classification in the context of GIOP Timothy S. Moore University of New Hampshire, Durham NH Mark D. Dowell Joint Research Centre, Ispra Italy September 25, 2010

2. 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 coastal & shelf seas. This is unlikely to be achieved in the foreseeable future, with a single representation of IOP subcomponents. BEAM – Case2R, GIOP The proposed approach is an algorithm framework more than a specific algorithm.

3. Practical uses of a classification approach based on Optical Water Types (OWT) • Describe variance and co-variance of optically active constituents • Parameterizing IOP subcomponent models (or fit coefficients for empirical relationships) • Selecting different inversions methods for different optical waters • Avenue to spatial uncertainty estimates for remotely-sensed products • Value-added products (directing new Cal/Val field work, data collection)

4. Our OWT method uses a fuzzy logic approach for optical classification of in situ and satellite data based on remote sensing reflectance. Advantages of fuzzy logic defined provinces • They allow for spatial and temporal 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.

5. Conceptual Framework for class-based algorithms In-situ Database (NOMAD) IOPs Sgd, aph*,……. Rrs(l) 8 classes Cluster Analysis Station data sorted by class Class based relationships Class Mi, Σi IOP model parameterization Satellite Measurements Rrs(l) IOP model/algorithm selection Individual class derived products Calculate membership Merged Product

6. Base OWT Definition • 2407 data points (NOMAD v2) • 8 clusters ‘optimal’ • representations of different optical water types (OWT) • mean and covariance matrix form the basis of the • fuzzy membership function.

7. Mapping of the OWTs in ocean color data - example OWT 1 OWT 2 OWT 3 OWT 4 OWT 5 OWT 6 OWT 7 OWT 8

8. Possible coefficients to parameterize on an OWT-basis in a standard semi-analytic algorithm configuration. a(l) = aw(l)+ aph(l,Chl) + ad(l,TSS)+ acdom(l,CDOM) a(l) = aw(l)+ Ac(l)[Chl]Bc(l) + [acdm(440)] exp(-Sdg(l-440)) bb(l) = bbw(l)+ bbp(l,Chl,TSS) bb(l) = bbw(l) +[bbp(555)] [555/l]Y Red - variables Yellow - parameters that need to be set (possible OWT dependency

9. One could imagine applying a tuning algorithm (e.g. simulated annealing) to each class to determine optimal class based model coefficients.

10. What follows is a look at the distribution and relationships of optical properties in the context of semi (quasi)-analytic algorithms from an OWT perspective based on the NOMAD v2 and IOCCG simulated data set.

11. Distribution of OWTs in Data sets vs. Ocean Obsverations (numbers are in percent)

12. Sg v. ag443 Points are color coded by degree of membership to the OWT (based on Rrs).

13. OWT 1 ag slope IOCCG NOMAD ag 443

14. (Bricaud et al, 2009) Sg ag440 -NOMAD

15. OWT 1 2 3 4 5 6 7 8 µ=0.086 µ=7.39 OWT 1 OWT 5 aph aph µ=0.148 OWT 2 µ=7.87 OWT 6 µ=0.331 OWT 3 µ=3.22 OWT 7 aph* µ=1.01 OWT 4 µ=3.04 OWT 8 log10 Chl

16. OWT 1 log ag443 log aph443 ag443/at443 bbp slope IOCCG NOMAD

17. OWT 2 log ag443 log aph443 ag443/at443 bbp slope IOCCG NOMAD

18. OWT 3 bbp slope log ag443 log aph443 ag443/at443 IOCCG NOMAD

19. OWT 4 bbp slope log ag443 log aph443 ag443/at443 IOCCG NOMAD

20. OWT 5 bbp slope log ag443 log aph443 ag443/at443 IOCCG NOMAD

21. OWT 6 bbp slope log ag443 log aph443 ag443/at443 IOCCG NOMAD

22. OWT 7 log ag443 log aph443 ag443/at443 bbp slope IOCCG NOMAD

23. For what its worth…

24. Averages

25. OWT OWT 1 1 2 2 3 3 4 4 2.5 5 5 (QAA) 6 6 2.0 7 7 8 8 1.5 1.0 Y 0.5 0.0 0.5 1 5 rrs443/rrs555 Miscellaneous bio-optical empirical functions Bricaud aph* function LAS Kd function Kd443 aph* Chl rrs443/rrs555 QAA a555 rho

26. Frequency of ‘low membership’ areas 100 % 75 50 25 0 “Blue Hole”

27. Summary • There are some inconsistencies in the OWT-based distributions of IOPs between NOMAD and the IOCCG simulated data set. • Both data sets are skewed towards coastal/case 2 waters. • If a new simulated data set is being considered, the generation of IOPs and IOP pairs could be further constrained by the variance and co-variance as seen in NOMAD within different OWTs. • In addition, the representation of data points could be guided by the global distribution of naturally occurring OWTs.

28. Summary (continued) • OWT code is currently in Seadas, but has yet to receive the final green light for public usage (we see no problem here). • Preliminary OWT-based IOP parameters now exist and can be used in the GIOP framework. • Potential for further use in parameterizing empirical models within GIOP is being explored. • OWTs themselves may change over time, which could effect some of the OWT-based parameters (don’t think this to be major). • Sensitivity and performance analysis remains to be assessed for GIOP-related products.

29. OWT 1 2 3 4 5 6 7 8 OWT 5 OWT 1 log ag443 OWT 2 OWT 6 ag411 OWT 3 OWT 7 NOMAD ag443 OWT distributions OWT 4 OWT 8

30. (Bricaud et al, 2009) Sg ag440 -NOMAD

31. Sd v. ad443

32. Sdg v. adg443

33. Effects of aph to aph* conversion OWT 5 aph aph* • There are some issues with data quality that might be revealed.

34. OWT 1 OWT 5 bbp slope estimation OWT 2 OWT 6 bbp OWT 3 OWT 7 OWT 4 0 0.5 1 1.5 2.0 Y

35. OWT 1 OWT 5 bbp slope estimation OWT 2 OWT 6 OWT 7 OWT 3 OWT 4 All * Negative values excluded Y