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Evaluation of atmospheric correction algorithms for MODIS Aqua in coastal regions

Evaluation of atmospheric correction algorithms for MODIS Aqua in coastal regions. Goyens, C., Jamet, C., and Loisel, H. Atmospheric correction workshop – June 13 th 2012 – Wimereux. 1. CONTEXT: Why do we need atmospheric correction algorithms ?. In the NIR:. Unknown.

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Evaluation of atmospheric correction algorithms for MODIS Aqua in coastal regions

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  1. Evaluation of atmospheric correction algorithms for MODIS Aqua in coastal regions Goyens, C., Jamet, C., and Loisel, H. Atmospheric correction workshop – June 13th 2012 – Wimereux

  2. 1. CONTEXT: Why do we need atmospheric correction algorithms ? In the NIR: Unknown LTOA= Lpath+T*Lg+t*Lwc+t*Lw Atmosphere Scattering by aerosols & molecules Absorption by aerosols & molecules Absorption by pure water & constituents Scattering from suspended matter !!!! Ocean Absorption by phyto and constituents For turbid waters !! 2/23

  3. 2. OBJECTIVES To improve the atmospheric correction algorithms in turbid waters Prepare future missions (e.g. Sentinel 3, ACE, OCAPI, GOCI-2) 3/23

  4. 2. OBJECTIVES To improve the atmospheric correction algorithms in turbid waters Prepare future missions (e.g. Sentinel 3, ACE, OCAPI, GOCI-2) Global evaluation of atmospheric correction algorithms for turbid waters for MODIS Aqua 4/23

  5. METHODS 3. METHODS: Selection of 4 algorithms for MODIS Aqua 1. Standard algorithm of the NASA (STD) (Bailey et al. 2010) • Gordon & Wang 94 • including a bio-optical model with hypotheses of Lw at 670 nm 2. NIR similarity spectrum algorithm (SIMIL) (Ruddick et al. 2000) • Gordon & Wang 94 • including hypotheses of spatial homogeneity of Lw(NIR) and La(NIR) 3. NIR-SWIR algorithm (SWIR) (Wang & Shi, 2007) • Gordon & Wang 94 • uses SWIR for the selection of aerosol models in turbid water and the STD algorithm for non-turbid waters 4. Direct inversion by Neural Network (NN) (Schroeder et al. 2007) 5/23

  6. 3. METHODS: In-Situ data 1. AERONET-OC data: = global network of above-water autonomous radiometers located in coastal regions - AAOT: 2002-2007 - COVE: 2006-2009 - MVCO: 2004-2005 - Gustav Dalen: 2005-2009 - Helsinki: 2006-2009 2. Cruise data from LOG: = in-water measurements with TriOS - Optical Sensors • North Sea and English Channel 2009/05-2009/09 • French Guiana 2009/10-2009/10 6/23

  7. RESULTS 4. RESULTS: Matchup pairs Matching satellite images with in-situ data: - 3 by 3 pixel window around the station - Median of at least 6 « valid » pixels within the window - Spatial homogeneity within the window - Focus on turbid waters only (in-situ nLw (667) > 0.183 mW. cm-2 um-1 sr -1) Excluded matchups: Reduced to 187 for inter-comparison (matchup has an estimation for each algorithm) 7/23

  8. RESULTS 4. RESULTS: Global evaluation of the algorithms 8/23

  9. RESULTS 4. RESULTS: Evaluation of the algorithms as a function of the water types Classification of in-situ Lw spectra in 4 water type classes defined by Vantrepotte et al. (2012) Distinguish classes based on normalized reflectance spectra Focus on turbid waters only ! 9/23

  10. RESULTS 4. RESULTS: Evaluation of the algorithms as a function of the water types Mainly phytoplankton Detrital & mineral material High concentrations of CDOM & phytoplankton 10/23

  11. RESULTS 4. RESULTS: Evaluation of the algorithms as a function of the water types CLASS 1 CLASS 2 CLASS 4

  12. RESULTS RESULTS 4. RESULTS: Evaluation of the algorithms as a function of the water types CLASS 1 CLASS 4 CLASS 2 12/23

  13. 5. CONCLUSION 1. Overall best algorithm = Standard algorithm from NASA 2. Overall atmospheric correction algorithms performs • well for water masses mainly influenced by • high concentrations of phytoplankton • less for water masses mainly influenced by • detrital & mineral material • high concentration of CDOM 3. Validation of the algorithms depends on water type! • The NN algorithm performs the best for water masses influenced by detrital and mineral material • The NIR SIMILARITY algorithm performs better for water masses influenced by detrital and mineral material compared to SWIR and STD 13/23

  14. PERSPECTIVES 6. PERSPECTIVES 1. Further improving the STD and SIMIL algorithms: • Modify the hypotheses within the bio-optical model of the STD algorithm (Bailey et al., 2012) → data from the Management Unit of the North Sea Mathematical Models (MUMM), PI Kevin Ruddick) 14/23

  15. PERSPECTIVES 6. PERSPECTIVES 1. Further improving the STD and SIMIL algorithms: • Modify the hypotheses within the bio-optical model of the STD algorithm (Bailey et al., 2012) • Constrain algorithms with new relationships Ruddick et al., 2000 Lw (869) Lw (748) → data from the Management Unit of the North Sea Mathematical Models (MUMM), PI Kevin Ruddick) 15/23

  16. PERSPECTIVES 6. PERSPECTIVES 1. Further improving the STD and SIMIL algorithms: • Modify the hypotheses within the bio-optical model of the STD algorithm (Bailey et al., 2012) • Constrain algorithms with new relationships Rrs670= 0.23*Rrs550*(Rrs520/Rrs550)-2 16/23

  17. THANK YOU FOR YOUR ATTENTION And many thanks to .... - CNES for their funding provided through the TOSCA program - The Ministère Français de l'Enseignement for providing my scholarship - GSFC NASA for the access to the MODIS-aqua images and for their support - Hui Feng, Brent Holben and Giuseppe Zibordi, PI's from the AERONET-OC stations used in this study - The MUMM team and Kevin Ruddick for sharing their in-situ database - Colleagues from LOG for collecting the in-situ data

  18. ANN model P132 versus P134

  19. Classification of Lw spectra per water type: - 4 water type classes defined by Vantrepotte et al. (in press) • Input = normalized reflectance spectra • Classification= unsupervised clustering method of Ward (minimizing the sum of squares of any pairs of clusters at each step) • Remove outliers using Silhouette Width - Novelty detection technique: • Each class is associated to a log normal distribution with µ and Σ • Assigns the spectra to the water type class with the smallest Mahalanobis distance • If the Mahalanobis distance > then theoretical threshold (from Chi-Square distribution), matchup is defined as unclassified D'Alimonte et al. (2003)

  20. Vicarious Calibration ALL GAINS = 1 DEFAULT GAINS FROM SEADAS

  21. Vicarious Calibration

  22. Classification of Lw spectra per water type: - 4 water type classes defined by Vantrepotte et al. (in press) • Input = normalized reflectance spectra • Classification= unsupervised clustering method of Ward (minimizing the sum of squares of any pairs of clusters at each step) • Remove outliers using Silhouette Width - Novelty detection technique: • Each class is associated to a log normal distribution with µ and Σ • Assigns the spectra to the water type class with the smallest Mahalanobis distance • If the Mahalanobis distance > then theoretical threshold (from Chi-Square distribution), matchup is defined as unclassified D'Alimonte et al. (2003)

  23. Selection of aerosols models following Gordon and Wang (1994)

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