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A class-based approach for mapping the uncertainty of empirical chlorophyll algorithms

A class-based approach for mapping the uncertainty of empirical chlorophyll algorithms. Timothy S. Moore University of New Hampshire NASA OCRT Meeting May 3-5, 2009 NYC. …in collaboration with…. Mark Dowell, JRC Janet Campbell, UNH. What’s the problem?.

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A class-based approach for mapping the uncertainty of empirical chlorophyll algorithms

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  1. A class-based approach for mapping the uncertainty of empirical chlorophyll algorithms Timothy S. Moore University of New Hampshire NASA OCRT Meeting May 3-5, 2009 NYC …in collaboration with… Mark Dowell, JRC Janet Campbell, UNH

  2. What’s the problem? • Current single, bulk estimates of chlorophyll error (50-78%) for the empirical algorithms exceed the desired goal of 35%. • This is misleading, as algorithms do not perform to the same level of accuracy in different optical environments. • Product error is relevant to higher-order algorithms that use OC products, and understanding changes in CDRs. • Question: How can we more accurately assess OC product error and geographically map them?

  3. OC3/OC4 Algorithms Relative error Range of uncertainty log Rrs(blue):Rrs(green) Average absolute error: 50% based on NOMAD V2

  4. NOMAD V2

  5. Approach • Previously, we have implemented a fuzzy logic methodology for distinguishing different optical water types based on remote sensing reflectance. • The same techniques can be adapted for characterizing chlorophyll error (uncertainty) for empirical algorithms. • The advantage gained is that different parts of the empirical algorithm can be 1) discretely characterized for error and 2) individually mapped using satellite reflectance data.

  6. Rrs • In situ Chl • Algorithm Chl NOMAD V2 SeaWiFS Validation Set Aqua Validation Set

  7. In-situ Database (NOMAD V2) Rrs() OC3/4 Rel. Error 8 classes Cluster analysis station data sorted by class class-based average relative error Class Mi, Si Satellite Measurements Rrs() Individual class error Calculate membership Merged Image Product

  8. NOMAD V2 Clustering Results • Cluster analysis on SeaWiFS Rrs bands • 8 clusters optimal based on cluster validity functions N=2372

  9. Class Mean Reflectance Spectra Type 1 • Rrs mean spectra behave as endmembers • Rrs class statistics form the fuzzy membership function. 2 3 4 5 6 7 Rrs(0-) 8 wavelength (nm) Class Means

  10. What is fuzzy logic? Fuzzy Hard Fuzzy graded membership Traditional minimum-distance criteria Water Water Reflectance Band 2 Reflectance Band 2 Wetland Wetland Forest Forest 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 • Designed to handle data imprecision and ambiguity • Allows for multiple outcomes using a fuzzy membership

  11. Chi-square PDF The Membership Function Vrs y1 y2 Z2 = (Vrs - yj)tj -1(Vrs - yj) Vrs – satellite pixel vector yj – jth class mean vector j– jth class covariance matrix Result: A number between 0 and 1 that is a measure of the vector’s membership to that class.

  12. Characterizing class uncertainty chlor a uncertainty chlorophyll mg/m3 Type 1 2 3 4 5 6 7 8 Log10(max(Rrs443,Rrs488)/Rrs551) NOMAD V2 N=1543 Aqua validation set SeaWiFS validation set N=464 N=1576

  13. Relative Error - %

  14. Aqua GAC - May 2005 Membership 0 1

  15. Producing the Uncertainty Map For each pixel, Sfi* = Uncertainty image i = 1…8

  16. May 2005 Relative Error (%) 125 100 75 SeaWiFS OC4 50 25 0 Aqua OC3

  17. Aqua OC3 Error Relative Error (%) 125 100 Jan 2005 Apr 2005 75 50 25 0 Jul 2005 Oct 2005

  18. SeaWiFS OC4 Error

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

  20. Conclusions • Single, bulk estimates of algorithm performance do not realistically describe the spatial distribution of error. • The class-based method is a way to characterize product uncertainty for different optical environments and to dynamically map them. • Basing OC3/OC4 error statistics with the Aqua and SeaWiFS validation data set is recommended because it reflects product error. • Class-based approach provides a common framework that can be applied to different satellites and different algorithms at multiple spatial scales. • We envision the error maps as separate, companion products to the existing suite of NASA OC products.

  21. MERIS image - Aug. 22, 2008

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