Multi-Angle Implementation of Atmospheric Correction for MODIS:
Algorithm MAIAC
MODIS Science Team Meeting
Alexei Lyapustin and Yujie Wang, GEST UMBC/NASA GSFC, mail code 614.4
January 26, 2010
Block-diagram of Processing(Backup: Standard Algorithm)
New Granule
1. Grid L1B Data and Split in Tiles
3. QB, LTP: Cloud Mask (Covariance-Based Algorithm)
2. LTP: RetrieveWater Vapor
(NIR Algorithm)
Queue of K days
Is B7 BRF known?
YesUse MODIS Dark Target Algorithm
7. QP: Retrieve BRF and albedo in reflective bands.
Backup: Lambertian retrieval
6. LTP: Retrieve “Angstrom exponent”
5. LTP: Retrieve B3 AOT using known surface BRF,
B=7bB
4. QB: Retrieve Spectral Regression Coefficients in
Blue band B3 (bB).
No
4a. Retrieve Snow sub-pixel Fraction and
Snow Grain Size
QB:Is Snow Detected?
MAIAC Products (1 km, gridded)Atmosphere: Surface:
• Cloud Mask; Parameters of RTLS BRF model;• Water Vapor; Surface Reflectance (BRF)/ Albedo;• AOT & fine mode fraction; Dynamic Land-Water-Snow Mask.
• Basis - covariance analysis (identifies reproducible surface pattern in the time series) & reference clear-sky image of surface (B. Rossow)
• High covariance - CLEAR. Ephemeral clouds disturb the pattern and reduce covariance.
• Because covariance removes the average component of signal and uses variation, it works well: 1) for bright surfaces and snow, 2) in high AOT conditions if the surface variability is still detectable.
• Algorithm maintains a dynamic clear-skies reference surface image (refcm), used as a comparison target in cloud masking.
• Internal Land-Water-Snow surface classification.
CM Legend:Blue (Clear), Red & Yellow (Cloudy). DOY 187
GSFC, USA
DOY 116Mongu, Zambia
DOY 139Solar Village
Cloud MaskLeft – MODIS TERRA RGB, 2003 (5050km2), Right – MAIAC CM
(BTij < BTG – 4) AND (r1ij >refcm.r1ij+0.05) PCLOUD Bright-Cold Cloud Test (@Pixel)
Approach:
- Accumulate gridded MODIS L1B data for K days;
- Process K days for area NN pixels simultaneously:
KN2 (measurements) > K + 3N2 (unknowns), if K>3- Derive shape of BRF from 2.1 m, and use spectral scaling to reduce DIM:
KN2 > K + N2
MISR heritage: MODIS heritage:-Using spatial and angular structure of imagery for aerosol - RTLS BRF retrieval algorithm (Schaaf et al., 2002)
retrievals (Martonchik et al., IEEE TGARS 1998); - Gridding algorithm (Wolfe et al., 1998)
-Using angular and spectral shape similarity constraints - Cloud Mask (Ackerman et al., 1998)
in aerosol retrievals over land (Diner et al., RSE, 2004).
Basis:- surface is spatially variable and stable in short time
intervals;- aerosols are variable in time and have a mesoscale
(60-100 km) range of global variability.
7Bijij
Blueij b
Generic Retrieval of SRCQueue of K
days
1
2
3
kN2
}),,({ ijijvgoLij bkkk
{k} {bij}
Prescribed vs Retrieved SRC(Example for Goddard Space Flight Center, 2000, DOY 84-93)
Initial MAIAC run Second run after initialization
TOACM
C5 AOT TOACM AOT
NBRF SRCBRF
1. In standard retrievals, AOT correlates with surface brightness (left).
2. MAIAC removes artificial correlation by means of SRC retrieval (right).
Aerosol Retrieval Algorithm• Compute AOTB and weight of coarse mode using Blue (B3), Red (B1),
SWIR (B7) bands.
• Surface BRF: use SRC in blue band, . BRF in B1 and B7
is known from previous retrieval with uncertainty at TOA.
• Algorithm: Fit Blue band to find AOTB for given , and find by minimizing 2
),(
B
TheorMeas AOTRRrmse
7Bijij
Blueij b
TO
A R
efl
ect
an
ce
II
, mBlue Red SWIR
=0, Fine mode
=0.2, Low % of Coarse mode
=0.8, High % of Coarse mode
Water Cloud model
)( ij
… examplesIllustration of AOT and coarse mode fraction
for GSFC in June 2002:
1. Fine mode aerosol does not affect B7 (measured and modeled reflectance agree).
2. Coarse mode aerosol affects B7 (measured reflectance is higher than modeled reflectance)
Resolving thin clouds using Cloud Model(yellow color)
TOA CM NBRFBRFSRC AOT TOA7RTLS7
1
1
1
1
2
2
2
classification: Fine – Coarse - Cloud
• Compute 3 parameters of Ross-Thick Li-Sparse (RTLS) model by fitting
4-16 days of MODIS data at TOA:
Quality Control
- Detect seasonal and rapid surface change from measurements (green-up or senescence shows as large-scale correlated changes in NDVI and NIR and SWIR reflectance as compared to theoretical RTLS values). If surface is stable, use 16-day Queue. If change is detected, use last 4 days for faster response.
- In stable conditions, require consistency with previous solution.
- Check shape of BRDF, rmse etc. (including sufficient angular sampling,
filtering of high AOT …).
Surface Retrieval Algorithm
),(),,(),,(),(),,(),,( 000000 nlGGVVLLD RFkFkFkRR
DOY 233-248 DOY 249-270, 2000
Response to Rapid Surface Change
TOA RGB NBRFBRF
SRCAOT
TOA RGB NBRFBRF
SRCAOT
Example: MODIS PRI analysisHilker, T. et al. (2009). An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance. RSE, doi:10.1016/j.rse.2009.07.012.
Photochemical Reflectance Index (PRI):
The “ocean fluorescence” band 531 nm is sensitive todown-regulation of plant photosynthesis changing by several tenths of 1%, while reference band is stable. The ground measurements showed a good correlation of PRI with light use efficiency ().
554531
554531
Ground PRI (AMSPEC) vs MODIS PRI generated with 6S and MAIAC(Vancouver Island, BC, Canada, March-October 2006)
6S, all angles 6S, backscattering angles MAIAC
Problem of Brighter Surfaces
0
0.2
0.4
0.6
0.8
1
-60 -40 -20 0 20 40 60VZA
Sp
ec
tra
l R
ati
o
NBRF_Red = 0.36NBRF_Blue = 0.16NBRF_B7 = 0.52
135o45o
SZA=30o
SZA=60o
• MAIAC overestimates AOT in the backscattering directions, and underestimates it at forward scattering angles.
• Reason: spectral invariance assumption
is not very accurate for soils and sands when difference in brightness is significant.
• Analysis shows that
the Blue band BRF is more anisotropic than at 2.1 m. The bottom Figure shows Spectral Ratio for the blue (B3/B7) and red (B1/B7). The BRF was computed using LSRT parameters of ASRVN.
7Bijijij b
Desert Pixel
Wang, Y., A. Lyapustin, J. L. Privette, J. T. Morisette, B. Holben, Atmospheric Correction at AERONET Locations: A New Science and Validation Data Set, . IEEE Trans. Geosci. Remote Sens., in press.
AERONET
MA
IAC
Assessment of Lambertian Biasesfrom ASRVN Data (GSFC site)
y = 0.939x + 0.0028
R2 = 0.9936
0
0.04
0.08
0.12
0.16
0 0.04 0.08 0.12 0.16
ASRVN IBRF
RedAOT<0.3SZA<45VZA<45
y = 0.8992x + 0.0058
R2 = 0.9853
0
0.05
0.1
0.15
0.2
0 0.05 0.1 0.15 0.2
ASRVN IBRF
RedAOT<0.3SZA>45VZA>45
y = 0.8514x + 0.007
R2 = 0.9638
0
0.04
0.08
0.12
0.16
0 0.04 0.08 0.12 0.16
ASRVN IBRF
RedAOT>0.3SZA<45VZA<45
y = 0.8306x + 0.0089
R2 = 0.9517
0
0.05
0.1
0.15
0.2
0 0.05 0.1 0.15 0.2
ASRVN BRF
RedAOT>0.3SZA>45VZA>45
y = 0.848x + 0.0085
R2 = 0.9812
0
0.04
0.08
0.12
0.16
0 0.04 0.08 0.12 0.16
ASRVN IBRF
GreenAOT<0.3SZA<45VZA<45
y = 0.7683x + 0.014
R2 = 0.9639
0
0.05
0.1
0.15
0.2
0 0.05 0.1 0.15 0.2
ASRVN IBRF
GreenAOT<0.3SZA>45VZA>45
y = 0.6807x + 0.0191
R2 = 0.9127
0
0.04
0.08
0.12
0.16
0 0.04 0.08 0.12 0.16
ASRVN IBRF
GreenAOT>0.3SZA<45VZA<45
y = 0.6383x + 0.0219
R2 = 0.9121
0
0.05
0.1
0.15
0.2
0 0.05 0.1 0.15 0.2
ASRVN BRF
GreenAOT>0.3SZA>45VZA>45
ASRVN BRF
AS
RV
N L
ambe
rt R
efle
ctan
ce
Slope of regression decreases by up to ~15% in red and 35% in greenfrom consistent comparison of ASRVN-based BRF and RL (Wang, Lyapustin, Privette, Vermote, Schaaf, Wolfe, R. Cook et al.)
y = 0.9734x + 0.0071
R2 = 0.9932
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5
ASRVN IBRF
MO
D0
9 R
efle
cta
nce
(D
aily
)
NIR
y = 0.9119x + 0.005
R2 = 0.9572
0
0.05
0.1
0.15
0.2
0 0.05 0.1 0.15 0.2
ASRVN IBRF
MO
D0
9 R
efle
cta
nce
(D
aily
)
Red
y = 0.6212x + 0.0279
R2 = 0.8727
0
0.05
0.1
0.15
0.2
0 0.05 0.1 0.15 0.2
ASRVN BRF
MO
D0
9 R
efle
cta
nce
(D
aily
)
Green
y = 1.0061x + 0.0058
R2 = 0.985
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
ASRVN NDVI
MO
D0
9 N
DV
I (D
aily
)
NDVI
Assessment of Lambertian Biasesfrom MODIS Data
ASRVN BRF ASRVN NDVI
Konza EDC
y = 0.9374x + 0.0112
R2 = 0.9926
0.1
0.2
0.3
0.4
0.5
0.1 0.2 0.3 0.4 0.5
ASRVN IBRF
MO
D0
9 R
efle
cta
nce
(D
aily
)
NIR
y = 0.8653x + 0.0039
R2 = 0.94
0
0.03
0.06
0.09
0.12
0.15
0 0.03 0.06 0.09 0.12 0.15
ASRVN IBRF
MO
D0
9 R
efle
cta
nce
(D
aily
)
Red
y = 0.5903x + 0.0187
R2 = 0.8426
0
0.03
0.06
0.09
0.12
0 0.03 0.06 0.09 0.12
ASRVN BRF
MO
D0
9 R
efle
cta
nce
(D
aily
)
Green
y = 0.974x + 0.0284
R2 = 0.9792
0.2
0.4
0.6
0.8
1
0.2 0.4 0.6 0.8 1
ASRVN NDVI
MO
D0
9 N
DV
I (D
aily
)
NDVI
Walker Branch
ASRVN BRFASRVN BRF
Observed biases over vegetated sites are similar to results of ASRVN-based comparison of BRF and RL which indicates that they are mostly due to Lambertian assumption.
AERONET Validation, 7+ yrs. (old Version)