1. 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

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

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. 1. Standard algorithm of the NASA (STD) (Bailey et al. 2010) Gordon & Wang 94 including a bio-optical model with hypotheses of L w at 670 nm 2. NIR similarity spectrum algorithm (SIMIL) (Ruddick et al. 2000) Gordon & Wang 94 including hypotheses of spatial homogeneity of L w (NIR) and L a (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) METHODS 3. METHODS: Selection of 4 algorithms for MODIS Aqua 5/23

6. 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 3. METHODS: In-Situ data 6/23

7. 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 nL w (667) > 0.183 mW. cm -2 um -1 sr -1 ) Excluded matchups: RESULTS Reduced to 187 for inter-comparison (matchup has an estimation for each algorithm) 4. RESULTS: Matchup pairs 7/23

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

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

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

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

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

13. 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 5. CONCLUSION 13/23

14. 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) 6. PERSPECTIVES → data from the Management Unit of the North Sea Mathematical Models (MUMM), PI Kevin Ruddick) 14/23

15. 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 6. PERSPECTIVES → data from the Management Unit of the North Sea Mathematical Models (MUMM), PI Kevin Ruddick) Ruddick et al., 2000 Lw (748) Lw (869) 15/23

16. 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 6. PERSPECTIVES Rrs 670 = 0.23*Rrs 550 *(Rrs 520 /Rrs 550 ) -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)