Projet PNTS SPACTO. SPatial Atmospheric Correction over Turbid Ocean. Julien Brajard

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1 Projet PNTS SPACTO SPatial Atmospheric Correction over Turbid Ocean Julien Brajard

2 Outline Context Achievements Perpectives

3 Context

4 Signal measured by the radiometer water-leaving reflectance Atmospheric correction Objective : remove the atmospheric signal to retrieve the oceanic signal. MERIS 26 Jul. 2006

5 Standard atmospheric correction proccess 1 2 reflectance spectrum in the near-infrared aerosol optical properties Top-of-atmosphere reflectance atmosphere + ocean atmosphere wavelength black-pixel assumption aerosol optical thickness and Angström exponent 3 reflectance in the visible atmosphere wavelength 4 water-leaving reflectance Signal measured by the radiometer ocean wavelength

6 Where is it suitable? Flags level 2 (MERIS) water-leaving reflectance 490nm coastal open ocean MERIS 11 May MERIS 11 May Not suitable for coastal/turbid waters

7 Which assumption can be done? Open Ocean black/dark-pixel assumption «Ocean is black/dark for wavelengths higher than 700 nm» Coastal waters bright pixel assumption Gordon 1994, Antoine and Morel 1999 «Spectral dependency of ocean is known for wavelength higer than 700 nm» Moore et al. 1999, Bailey et al spatial assumption «aerosols properties are spatially correleted» the spatial algorithm Hu et al. 2000, Ruddick et al. 2000

8 Achievements

9 The spatial algorithm 1 Identify open ocean /coastal water Open Ocean Coastal waters MERIS flags Level 2

10 The spatial algorithm 2 Perform a standard atmospheric correction Use of NeuroVaria algorithm over open ocean Aerosol optical thickness Angström exponent NeuroVaria algorithm : Brajard et al., remote sensing of environement, 2012

11 The spatial algorithm 3 Evaluate the spatial correlation Variogram (h) = 1 2 Var[ Z(x) Z(y) 2 ] with h = x y (h) (h) fit h (in lat. degree) Aerosol optical thickness h (in lat. degree) Angström exponent

12 The spatial algorithm 4 Extrapolate the aerosol optical properties Using a ordinary krigging algorithm Aerosol optical thickness before krigging Aerosol optical thickness after krigging

13 The spatial algorithm 5 Perform the atmospheric correction Water-leaving reflectance (490nm) standard MERIS algorithm Water-leaving reflectance (490nm) spatial algorithm

14 Validation 5 transects (from Sept to July 2006) measurement of water-leaving reflectance downloaded from MERMAID (P.I. R. Doerffer)

15 Transect comparison 11 May 2006 water-leaving reflectance (490nm) measurement transect coastal water open ocean ocean coast in-situ standard MERIS spatial algorithm

16 Scatter plots water leaving reflectance (490nm) All valid matchups are selected (822 points) MERIS standard algorithm 1:1 line linear regression spatial algorithm in-situ measurement MERIS standard algorithm correlation (r 2 ) : 0.70 Root mean square error : 2.9E-3 in-situ measurement spatial algorithm correlation (r 2 ) : 0.79 Root mean square error : 2.7E-3

17 Other case study : AAOT Test on a moderate turbid water site

18 Tools used Atmospheric correction using NeuroVaria (implemented using the YAO software) Variogram/krigging using gstat noise removal : morphological opening median filter Matlab image toolbox

19 Summary The spatial algorithm performs an accurate atmospheric correction with making no assumption on the oceanic signal Outlook : Use this algorithm to extend other atmospheric correction scheme Adress the problems of specific signals near the coast (adjacency effect, fog) Explore the sensitivity of this algorithm to other coastal regions or other parametrization of the algorithm (flagging procedure, krigging algorithm,...)

20 Perspectives

21 Les perspectives A court terme : Généralisation de la méthode à d autres schémas de correction atmosphérique Insertion dans SeaDAS / ODESA (verrou technique) Validation globale Peu de mesures en eau très turbide Plus long terme : Proposer une méthode mixte spatiale / spectrale Evaluation précise des erreurs de chacune des approches.