A Method for MERIS Aerosol Correction : Principles and validation. David Béal, Frédéric Baret, Cédric Bacour, Kathy Pavageau

Transcription

1 A Method for MERIS Aerosol Correction : Principles and validation David Béal, Frédéric Baret, Cédric Bacour, Kathy Pavageau

2 Outlook Objectives Principles Training neural networks Validation Comparison with AERONET Comparison with MODIS products Consequences on atmospheric correction Conclusion

3 Objectives Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites Demonstrate the capacity of producing operationally consistent global fields of biophysical variables over long and continuous time series Biophysical variables: Albedo fapar fcover LAI Resolution: 1km - 8km Temporal sampling: 10 days Coverage: Global Duration: Sensors: VEGETATION (1-8km) AVHRR (8km) POLDER (8km) MERIS (RR) (1-8km?)

4 Principles of CYCLOPES products Temporal compositing Fusion between sensors Consistent algorithms between sensors R TOA Cloud Atm-corr R TOC Bio-algo VAR Little possibility for Fusion Possibility to fuse For single date Spectral & directional Normalization? Possible ingestion of several configurations Possible fusion (filtering) Problem: atmospheric correction climatology (current version) cooperation between sensors: some providing atmosphere characteristics autonomous correction

5 Atmospheric correction principles for MERIS Dense dark pixels not everywhere. Make use of the spectral features of MERIS Radiative transfer in the atmosphere (F=MODTRAN, 6S, SMAC, ) R TOA = F (H 2 0, 0 3, P, Aerosols, R TOC ) known? Algo AOT Estimates SMAC R TOA R TOC

6 Principles of the algorithm based on neural networks Why ANN? efficient (already working for ocean) fast in operational mode

7 Generation of the training data base Distribution of the radiative transfer model variables Leaves Canopy Atmosphere

8 Simulation of TOA reflectances N, C ab, C w, C dm, C s LAI, ALA, Hot, vcover Geometry θo, θs, φ Atmosphere Radiative transfer SMAC τ 550, P atm,c H20, C O3 R TOA (λ) Leaf optical properties PROSPECT Background reflectance Data base Soil type, B s ρ(λ) τ(λ) R s (λ) Canopy reflectance SAIL simulations R TOC (λ) fapar fcover # Centre (nm) Width (nm) Potential Applications Yellow substance and detrital pigments Chlorophyll absorption maximum Chlorophyll and other pigments Suspended sediment, red tides Chlorophyll absorption minimum Suspended sediment Chlorophyll absorption and fluo. reference Chlorophyll fluorescence peak Fluo. Reference, atmospheric corrections Vegetation, cloud Oxygen absorption R-branch Atmosphere corrections Vegetation, water vapour reference Atmosphere corrections Water vapour, land

9 Neural networks architecture Observation geometry Top Of Atmosphere reflectance (MERIS) The 13 bands used 412, 442, 490, 510, 560, 620, 665, 681, 708, 753, 760, 778, 865, 885, 900 H 2 O O 3

10 Theoretical performances evaluated over the test data set Mean:

11 Theoretical performances for the correction with the retrieved AOT No biases, some scattering (reasonable)

12 Validation over AERONET 10 sites located at different places with a range of surface conditions Several dates for each site A total of 61 pairs of clear MERIS RR L1B images and AERONET data available

13 Aerosol types Possibly biased by the location of the AERONET stations

14 Representativity of the learning data base Reflectance mismatch: Computed over 102 MERIS scenes (June 2002 october 2004)

15 Application of the algorithm: the spatial problem aerosol layer height normal sun zenith angle Sunphotometer zone width zone length Typical lengths of aerosol Footprint of the AERONET data Environmental effects Removing possible outliers Values computed over single RR pixels Median computed over a 30x30km² window

16 Validation over 61 AERONET sites No biases but some scattering

17 Comparison with MODIS products Same overpass time (10:00 am); 20 pairs of MODIS/MERIS estimates MERIS MODIS Comparable results with MODIS extended DDV algo.

18 Consequences for R TOC estimation Possible problems with aerosol types

19 Conclusion Moderate accuracy in AOT retrieval however similar to MODIS, MISR, but yielding to relatively accurate R TOC estimates Assumptions Continental atmosphere Adjacency effects neglected Not explicitly accounting for P Future improvements Account for Aerosol type Explicitly P, O3, H20 Exploitation of the spatial variability of the surface