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Mark Dowell mark.dowell@jrc.it & Stewart Bernard sbernard@csir.co.za PowerPoint Presentation
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Mark Dowell mark.dowell@jrc.it & Stewart Bernard sbernard@csir.co.za

Mark Dowell mark.dowell@jrc.it & Stewart Bernard sbernard@csir.co.za

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Mark Dowell mark.dowell@jrc.it & Stewart Bernard sbernard@csir.co.za

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  1. Tailored Algorithms for Ocean Colour applications at regional scales: geographic and optical approaches Mark Dowell mark.dowell@jrc.it & Stewart Bernard sbernard@csir.co.za

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

  3. Defintitons • Geographic • Class-based

  4. Regional vs. Class-Basedmake table! Regional Class-based Advantages Generic, “global”, can be generalized Can be used as a tool to identify “black holes” Seamless transitions Continuous improvements through additional on in-situ data Disadvantages More complicated to implement Computational more expensive – not much! • Advantages • Explicitly link to locally measured in-situ data • May be “simpler” • Accounts for physiological differences • Disadvantages • Explicitly link to locally measured in-situ data – not generalized • May result in regional discontinuities

  5. Stewart’s regional algorithmBackground

  6. The Southern Benguela A highly productive upwelling system, frequently suffering from harmful algal blooms, with considerable atmospheric variability. Both empirical and analytical algorithms have been developed for these high biomass waters, with buoy based validation of water leaving radiances. The next few years should see more buoy – and AERONET based validation of ocean and atmospheric algorithms Figure 7. Satellite-derived images of 1-km resolution from the Medium Resolution Imaging Spectrometer (MERIS) sensor, detailing the development of the Gonyaulax polygramma bloom in the False Bay area in February–March 2007 and its transport along the shelf edge of the West Coast. The simultaneous development of a bloom dominated by the toxic Alexandrium catenella in the St. Helena Bay region is also evident. Chlorophyll a (Chl) products were calculated using merged data from the standard MERIS Algal 1 algorithm for Chl < 25 mg m-3 and a locally derived red-band empirical algorithm for Chl > 25 mg m-3. No flags were applied to the data so as to allow bloom transport to be observed in images where absolute Chl is relatively less important (except where high sun glint caused the failure of the Algal 1 product). [Pitcher et al 2008]

  7. MERIS Sample Products Mozambique Channel 27th October 2008 Algal 1 Chlorophyll a Case 1 algorithm Algal 2 Chlorophyll a Case 2 algorithm Total Suspended Matter Case 2 algorithm Yellow Substance Case 2 algorithm South African East Coast: Natal and Delagoa Bights, Sofala Banks Dynamic coastal systems subject to extensive riverine influence and the oceanic influence of the Agulhas current; these are extremely good subjects regional algorithms dealing with high Case 2 variability. The next few years should see extensive radiometric and geophysical validation efforts, focusing initially on the Natal Bight.

  8. Stewart’s regional algorithmExample Products

  9. What to parameterize? • Variance and Co-variance of Optically Active Constituents • Parameterising IOP subcomponent models (or fit coefficients – for empirical algorithms) • Different OWT different inversions method • Avenue to spatial uncertainty estimates • Regional value-added products

  10. 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

  11. 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

  12. 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.

  13. 8 objectively identified classes in radiance space

  14. May 2004 MERIS Global Composite Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

  15. Benguela example I’m using the 2006 MODIS image (A2006082123500.L2_LAC) it would be good to have your regional algorithm for the same product and a RGB image

  16. Benguela example Mark’s image of classified optical water types+ membership maps

  17. MERIS October 8th 2008

  18. Relation to current understanding turbid water flag After Morel and Bélanger 2006

  19. Relation to current understanding turbid water flag Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

  20. Class persistence36 month Time-series Class 5 Class 6 Class 7 & 8 0 1

  21. Class Persistencedistribution of classes dominant for more than 70% of observations Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8

  22. Spatial Uncertainty Analysisderived from class based assessment of OC3M with NOMAD2

  23. Why Not !?

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

  25. Spectral Unmixing Amoeba - NLO

  26. Conclusions

  27. Future initiatives • MERIS specific class-based model parameterization • Investigate feasibility of implementing BEAM module for class distribution • Eventual link to Regional C2R processor • Consider the possibility of a proposal to G-POD to systematically process class distribution at basin and global scales. • GSFC to implement code for mapping of Optical Water Type distributions in SEADAS

  28. Proposed ROI WG • Initial proposal for a Regional bio-Optical algorithm Initiative (ROI) presented at 2008 IOCCG meeting • Establish central portal for information on regional OC algorithms, main focus: • Capacity building • Maps of water types • Geographically located bibliography • Protocols • Round robin • Expected to start some time in 2009 • If interested contact Mark Dowell (mark.dowell@jrc.it) or Stewart Bernard (sbernard@csir.co.za) to be added to mailing list

  29. ChloroGIN • ChloroGIN is the Chlorophyll Globally Integrated Network • Conceived during a IOC/GOOS/POGO/GEO sponsored workshop in Plymouth, Sept 2006 • Addresses GEO task EC-06-07 • “Build upon existing initiatives e.g. ANTARES in South America … to develop a global network of organization-networks for ecosystems, and coordinate activities to strengthen observing capacity in developing countries. “ • ChloroGIN aims to promote in situ measurement of chlorophyll in combination with satellite derived estimates and associated products.