1. The IOCCG Atmospheric Correction Working Group Status Report The Eighth IOCCG Committee Meeting Department of Animal Biology and Genetics University of Florence, Florence, Italy February 24-26, Menghua Wang
Contributors:
MERIS D. Antoine, A. Morel, B. Gentili OCTS/GLI H. Fukushima, R. Frouin POLDER P. Deschamps, J-M. Nicolas MODIS H. Gordon SeaWiFS M. Wang

2. Goal of the Atmospheric Correction Working Group
The atmospheric correction working group activity was proposed by R. Frouin at the 5th IOCCG committee meeting in Hobart, Tasmania, and endorsed by committee and representatives of various space agencies participated at the meeting.
The main objective of the working group is to
quantify the performance of the various exiting atmospheric correction algorithms used in the various ocean color satellite sensors;
the derived products from various ocean color missions (projects) can be meaningfully compared and possibly merged.
how can derived ocean color products from one sensor be best compared with those from others?

3. Membership
The Working Group is composed of:
Antoine, Morel MERIS
Dechamps POLDER
Fukushima, Frouin OCTS/GLI
Gordon MODIS
Wang SeaWiFS
Others are welcome to participate. A general requirement for people to join the Working Group is that they can contribute a well documented algorithm and participate some of tests.

4. Atmospheric Correction Algorithms
The performance of the following atmospheric correction algorithms are intended to be tested and compared:
SeaWiFS/MODIS algorithm (Gordon and Wang, 1994)
POLDER algorithm (POLDER document, Feb. 1999)
OCTS/GLI algorithm (Fukushima et al, 1998)
MERIS algorithm (Antoine & Morel, 1999)
Testing of the above 4 operational algorithms is the necessary requirement for the objective of the Working Group.
Results from other algorithms for some special cases, e.g., Spectral Matching algorithm for absorbing aerosols, are also useful.

5. Parameters
The derived parameters to be compared and tested are:
the normalized water-leaving reflectances at the visible wavelength bands;
two-band ratio values of the derived normalized water-leaving reflectances, i.e., 443/555 and 490/555; and
the atmospheric parameter--the derived aerosol optical thickness at 865 nm.

6. Sensor Spectral Characterizations
All comparison algorithms are operated (some have been modified for this purpose) using the same spectral bands of 443, 490, 555, 765, and 865 nm.

7. The TOA Reflectance (testing data) Generation
The TOA reflectances were generated based on the following:
rw is the water-leaving reflectance from model (Case-1) or measurements (Case-2).
rr is the Rayleigh reflectance.
A = ra + rra is the aerosol and Rayleigh-aerosol contributions.
t is the atmospheric diffuse transmittance.
the sun glint and whitecap contributions are ignored.
gas absorption is ignored.

8. For the open ocean cases
Testing Data Sets
Simulated Data Sets:
For the open ocean cases
a polarized RTE (Monte Carlo method) was used for simulations with 15 million photons for each vector RTE run (within ~0.5% at blue);
TOA reflectances for spectral bands at 412, 443, 490, 510, 555, 670, 708, 765, 779, and 865 nm (total 10 spectral bands) were generated;
a two-layer plane-parallel atmospheric model (78% of molecules at the top layer);
aerosols (Maritime with RH=80%, M80) located at the bottom layer mixed with 22% of molecules (Rayleigh scattering);
aerosol optical thicknesses at 865 nm: 0.05, 0.1, and 0.2;
a Fresnel reflecting ocean surface with pigment concentrations of 0.03, 0.1, 0.3, and 1.0 (mg/m3) from Gordon et al. (1988) model;
no gas absorption, no whitecap contributions;
the solar zenith angles: 0o, 45o, 60o, 65o, 70o, and 78o; sensor viewing angles: 5o, 25o, 45o, 55o, and 65o; and relative azimuth angle of 90o.

9. Therefore, 15 million photons were used for each vector RTE simulation

10. Uncertainty is usually within ~0.5% at the blue

11. Testing Data Sets (cont.)
Some cases for sensitivity studies (simulated data sets)
absorbing aerosols: Urban aerosols with two type vertical distributions, i.e., two-layer and uniformly mixed one-layer cases;
case 2 water—although algorithms are mostly intended for case 1 water, a quantitative estimation of atmospheric correction error over case 2 water is needed.
Data from SeaWiFS measurements (this is still open….):
open ocean cases (with various locations and seasons);
coastal region ocean waters;
some trouble cases, e.g., nLw<0, dust contamination, etc.
For testing and comparison, SeaWiFS data sets are usually co-located with in situ measurements. It was agreed that SeaDAS will be used.

12. Diffuse Transmittance Issue
It was realized that there were two fundamentally different approaches in computing the atmospheric diffuse transmittance and effect the atmospheric correction:
the SeaWiFS/MODIS algorithm assumes that the water-leaving radiance just BENEATH the sea surface is uniform.
the POLDER algorithm (University of Lille) assumes that the water-leaving radiance just ABOVE the sea surface is uniform.
in addition, the POLDER team includes a factor of the multiple surface reflection contribution, i.e., 1/[1-S*rwn ].
However, the t difference is usually within ~2%, while difference from the multiple surface reflection factor is within ~1%. Therefore, a simple correction to the POLDER results was proposed and agreed within the group. The correction has been applied to the POLDER results.

14. Atmospheric Contributions: Maritime Aerosol (2-layer)

15. Atmospheric Contributions: Absorbing Aerosol (2-layer)

17. NOTE: Significant different contribution in magnitude from these two type waters !!

18. Maritime Aerosol (2-layer) Cases

19. Maritime Aerosol (2-layer) Cases

20. Absorbing Aerosol (2-layer) Cases

21. Absorbing Aerosol (1-layer) Cases

22. NIR reflectances are not enough to retrieve absorbing aerosol properties

24. ALL COMPARISON RESULTS ARE PRELIMINARY!

25. WORK IS IN PROGRESS …….

26. IOCCG Report Outline Introduction Algorithm Description
Atmospheric correction working group: objectives, members, procedures, etc.
Overview of the atmospheric correction for ocean color sensors
Algorithm Description
MERIS
POLDER
OCTS/GLI
SeaWiFS/MODIS
Others, e.g., spectral-match algorithm for absorbing aerosols, etc.
Simulated Data Set
Brief description of the vector Monte-Carlo RTE for the data set
Uncertainty of the data set, e.g., noise, accuracy, etc.
Atmospheric model, e.g., two-layer, one-layer, aerosols: M80, U80, surface, etc.
Ocean data set: Case-1 and Case-2
Diffuse transmittance: assumptions, computations, and two approaches
Generating TOA data from atmosphere and ocean data set

27. IOCCG Report Outline (cont.)
Comparison Results
Open ocean (Case-1) with Maritime aerosols
Case-1 water with absorbing (Urban) aerosols
Case-2 water with Maritime aerosols
Case-2 water with absorbing (Urban) aerosols
Vertical effects for the absorbing aerosols
Discussions
Errors from various algorithms: radiance, ratio, aerosol thickness
Influence of errors in the ratio values (the normalized water-leaving radiance) to the bio-optical algorithm, e.g., the chlorophyll retrievals
Cases for absorbing aerosols, Case-2 waters, etc.
Vicarious calibration
Others
Recommendations and Conclusions
Future Work
Algorithm comparison with real satellite measured data, e.g., SeaWiFS data

28. Status/Time Schedule
Setting up working group (done).
Draft a proposal for discussing in the 1st working group meeting in May 16-18, 2000 (done).
Revise working plan based on discussions (done).
Generate the testing data sets: ~3-4 months (done).
The 2nd working group meeting was held on 1/18/2002 (done).
Diffuse transmittance issue was resolved: ~5 months (done).
Algorithm testing and results analyses: (on going).
Write up an IOCCG report: (on going).
Workshop for the working group: (planned).
A journal paper: (planned).
Algorithm comparison with real satellite data (e.g., SeaWiFS, data)??? (open).