Menghua Wang, UMBC, NASA/GSFC 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, 2003 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
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Menghua Wang, UMBC, NASA/GSFC The IOCCG Atmospheric Correction Working Group Status Report The Eighth IOCCG Committee Meeting Department of Animal Biology.
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Menghua Wang, UMBC, NASA/GSFC
The IOCCG Atmospheric Correction Working Group Status Report
The Eighth IOCCG Committee Meeting Department of Animal Biology and Genetics
University of Florence, Florence, ItalyFebruary 24-26, 2003
Menghua Wang
Contributors:MERIS D. Antoine, A. Morel, B. GentiliOCTS/GLI H. Fukushima, R. FrouinPOLDER P. Deschamps, J-M. NicolasMODIS H. GordonSeaWiFS M. Wang
Menghua Wang, UMBC, NASA/GSFC
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?
Menghua Wang, UMBC, NASA/GSFC
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.
Menghua Wang, UMBC, NASA/GSFC
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.
Menghua Wang, UMBC, NASA/GSFC
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.
Menghua Wang, UMBC, NASA/GSFC
Sensor Spectral Characterizations
SeaWiFSBand Center
(nm)
MODISBand Center
(nm)
OCTS/GLIBand Center
(nm)
POLDERBand Center
(nm)
MERISBand Center
(nm)
412 412 412 — 412
443 443 443 443 443
490 490 490 490 490
510 530 520 — 510
555 550 565 565 560
670 670 670/680 670† 665
765 750 765/749 765 708, 779
865 865 865 865† 870
† Polarized channel.
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.
Menghua Wang, UMBC, NASA/GSFC
The TOA Reflectance (testing data) Generation
• w is the water-leaving reflectance from model (Case-1) or
measurements (Case-2).
• r is the Rayleigh reflectance.
• A = a + ra 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.
The TOA reflectances were generated based on the following:
t r A t w , L 0 F0
Menghua Wang, UMBC, NASA/GSFC
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.
Menghua Wang, UMBC, NASA/GSFC
-0.2
-0.1
0
0.1
0.2
0 10 20 30 40 50 60 70
Monte Carlo Noise
412 nm865 nm
% D
iffe
ren
ce in
Lt(
)
Sensor Zenith Angle (Deg.)
M80 aerosol, a(865) = 0.2,
o = 78o, = 90o
Vector RTE run with 15 million photonscomparing with 30 million photons
Therefore, 15 million photons were used for each vector RTE simulation
Menghua Wang, UMBC, NASA/GSFC
Uncertainty is usually within ~0.5% at the blue
-1
-0.5
0
0.5
1
2 2.5 3 3.5 4 4.5 5 5.5
TOA Radiance Validation
412 nm443 nm490 nm510 nm
555 nm670 nm765 nm865 nm
% D
iffe
ren
ce o
f L t(
) (M
C v
s. S
OS
)
Air Mass
Vector RTE of Monte Carlo (MC) vs. Successive-Order-Scattering (SOS)
Two-Layer model, M80 aerosol
a(865) = 0.1,
o = 0o, 60o, 70o, = 5o, 25o, 45o, 65o, = 90o
Menghua Wang, UMBC, NASA/GSFC
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.
Menghua Wang, UMBC, NASA/GSFC
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*wn ].
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.