1. Algorithm Theoretical Basis Document GlobAlbedo Aerosol Retrieval
Peter North, Andreas Heckel
Swansea University
L. Guanter, R. Preusker, J. Fischer
Free University of Berlin

2. Aims
AOT retrieval for all sensors based on common set of aerosol models and RTM
Avoid a priori assumptions on surface albedo for AOT retrieval
Development of separate inversion constraints to exploit the characteristics of the instruments
Per pixel uncertainty estimates for error propagation

3. Spatial interpolation
Algorithm Scheme
(A)ATSR RTOA
(8 channels)
MERIS RTOA
(13 channels)
VGT RTOA
Iterative inversion
(image subsets)
Atmospheric correction using LUT:
F(RTOA, Ma , t550 , O3, H2O, H,S, V, R, )
-> RSURF(,S, V, R, )
Optimise t550 and Ma to minimise EMOD
Test fit with surface model: N Y
RTOA
(Land, cloud-free)
Initial estimate:
Ma , t550
Auxiliary data:
O3,H2O, DEM,
Ma, t550 and Dt550
(Sparse grid)
Water/cloud masking
RTOA
(Land, cloud-free)
Initial estimate:
Ma , t550
Auxiliary data:
O3,H2O, DEM,
Aerosol
Retrieval
Ma, t550 and Dt550
(Sparse grid)
Spatial interpolation
(A)ATSR
Ma, t550 Dt550
MERIS
Ma, t550 Dt550
VGT
Ma, t550 Dt550

4. TOA - SDR Inversion LUT
Approximation of the radiative transfer problem
Inversion of TOA reflectance to SDRs based on lookup tables
Ratm, gl and rl tabulated in lookup tables

5. Aerosol Models
LUTs provide atmospheric parameters for a set of 7 different aerosol models
Potential to retrieve aerosol type
Option to set fixed aerosol model to increase performance and consistency

6. Trace Gas Absorption
Fitting of surface models requires correction of trace gas absorptions
Additional LUT provide trace gas transmittance tg(AMFgeo)

7. Surface Reflectance Models
Angular Model (ATSR-2,AATSR)
Spectral Model (MERIS, VGT)
Mixed Model (AATSR)
Apply spectral model to AATSR channels
Linear combination of angular and spectral models

8. Optimal AOT and Uncertainty
Minimum of error metric determines optimal AOT
Aim: tuning of constraints to retrieve same AOT with the different instruments
Per-pixel uncertainty is defined as:
Emin : Minimum value of error metric
a : Curvature at minimum
k : scaling (to be defined validation)

9. Tomsk

10. TOA Reflectance

11. Pixel classification
Removal of cloudy and non-land pixel

12. Spatial Binning Assumption of smooth AOT field 9x9 pixel bins
“Improves” AATSR co-registration
Enables better surface model fits

13. Retrieved AOT Low resolution AOT field with gaps
Gaps larger than in original resolution due to binning
Needs gap filling

14. Spatial Interpolation
Gaps interpolated and rescaled to original input resolution

15. Final AOT product
Final AOT map with high resolution valid pixel mask applied

16. Preliminary AOT Test Data-Set
AATSR TOA RGB
AATSR AOT
AATSR DAOT
VGT TOA RGB
VGT AOT
VGT DAOT

17. AATSR Tomsk 15.5.2003 False colour RGB AOT AOT uncertainty
Improved AOT
AOT uncertainty

18. Tomsk: Synergy, MERIS Lv2, MODIS

19. Correlation TOA Reflectance VGT - AATSR

20. AOT Validation Quantitative comparison vs. Aeronet measurements
Spatial average 15x15km
Temporal overlap 30min
Qualitative comparison vs. other satellite products
(MERIS L2, MODIS)

21. Summary
Draft processor for AOT retrieval of MERIS, VGT and AATSR implemented in BEAM
1 year test data-set of AOT and per-pixel uncertainty of AATSR and VGT processed
Known issues / Next steps
Refinement of constraints necessary to retrieve homogenous aerosol for all three sensors
Single aerosol model only
Optimisations of implementation to increase performance