1. Figure 3. TERRA MODIS AOD biases (left column), MODIS land cover classification (middle column), and polynomial
fits for SRR-MVI relationships are shown for CCNY (top), Billerica (middle), and Osaka (bottom) sites.
Adam Atia1
Barry Gross2
1
Earth System Science and Environmental Engineering, The City College of New York
2
Department of Electrical Engineering, The City College of New York
Figure 2. Simplified flowchart of improved regional algorithm for MODIS AOD retrievals over urban areasPM 2.5 air quality monitoring depends on aerosol optical depth (AOD)
satellite retrievals to model air quality regionally. However, the
Moderate Resolution Imaging Spectroradiometer (MODIS) Collection
5 algorithm tends to overestimate AOD retrievals specifically over
urban areas. A primary driver for this overestimation is suspected to be
due to the underestimation of visible-shortwave infrared surface
reflection ratios by the MODIS Collection 5 retrieval . While using the
Aerosol Robotic NETwork (AERONET) AOD dataset to validate
MODIS AOD retrievals, this study focuses on augmenting the
Collection 5 retrieval algorithm by incorporating land surface
classification to differentiate between urban and non-urban pixels.
Applying a second-order polynomial fit to predict surface reflectance
values based on the modified vegetation index (MVI) for urban pixels
yields improved AOD retrievals. A MODIS AOD case study over the
CCNY (NYC) area is shown to illustrate this bias-correction; for this
particular case, the modified algorithm results in a reduction from 93%
to 25% AOD error at the CCNY AERONET pixel.
Abstract
Methodology
Analysis
Improving MODIS Aerosol Optical Depth Retrievals Over Urban AreasImproving MODIS Aerosol Optical Depth Retrievals Over Urban Areas
The MODIS Collection 5 (C005) aerosol over land algorithm1
that
produces the MODIS AOD Level 2 product is illustrated in Figure 1
as a simplified flowchart where only the main processes related to
surface reflectance estimations are shown. This algorithm is largely
dependent on “dark pixels” and works relatively well for densely
vegetated sites.
Figure 1. Simplified flowchart of improved regional algorithm for
MODIS AOD C005 algorithm.
To improve MODIS AOD retrievals over urban areas, using
MATLAB, the MODIS land cover product (MCD12Q1)2
and
MODIS AERONET Surface Reflectance Validation Network
(ASRVN)3
data were implemented. Since the C005 algorithm does not
account for land classification, pixels in a given MODIS granule were
identified as either urban or non-urban, based on the MCD12Q1
dataset. Instead of using the C005 surface reflectance estimations for
urban pixels, the instantaneous bidirectional reflectance factor (IBRF)
values from the ASRVN dataset were used for increased accuracy.
Plotting the time-averaged 660-2120 and 470-2120 surface reflectance
ratios (SRR) against MVI for a site that is predominantly classified as
urban, surface reflectance was estimated by fitting a second-order
polynomial curve to the plot, with MVI as the only independent
variable.
Collection 5 Algorithm for
AOD over Land
Collection 5 Algorithm for AOD over Land
NO YES
Land Classification Data
MVI = 1.24
ρ −
2.12
ρ
1.24
ρ +
2.12
ρ
Methodology Continued
Refined AODOperational C005 AOD
Results
CCNYBillericaOsaka
SRRSRRSRR
MVI
MVI
MVI
 Underestimation of visible-shortwave infrared
surface reflectance ratios does play a role in
the over-bias in MODIS AOD retrievals.
 Criteria must be further defined to determine
when the regional algorithm can be applied.
 There are other factors that seem to contribute
to AOD overestimation, which are being
investigated in relation to AOD error.
 Currently our regional algorithm yields 10 km
resolution results; work is being done to
produce 3 km resolution granules as well.
Conclusion & Future Work
Figure 4. A MODIS AOD case study over NYC on October 3,
2006 is shown below. The NYC AOD hotspot (left) produced
by the C005 algorithm is reduced and brought closer to
AERONET AOD levels by the improved regional algorithm
(right). At the center pixel (where the AERONET station is
located), the C005 algorithm and the regional algorithm yield
AOD values of 0.54 and 0.35, respectively. Since the
AERONET AOD value is 0.28, the percent errors for the C005
and regional algorithms are approximately 93% and 25%,
respectively.
1] Levy, R., Remer, L., Mattoo, S., Vermote, E., and Kaufman,Y. J.: Second-
generation operational algorithm: Retrieval of aerosol properties over land from
inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, J.
Geophys. Res.-Atmos., J Geophys Res. 2007;112:13211. doi:
10.1029/2006JD007811. [Online 13 July 2007].
2] Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D.,
Strahler, A. H., ... & Schaaf, C. (2002). Global land cover mapping from MODIS:
algorithms and early results. Remote Sensing of Environment, 83(1), 287-302.
3] Wang, Y., Lyapustin, A. I., Privette, J. L., Morisette, J. T., & Holben, B. (2009).
Atmospheric correction at AERONET locations: A new science and validation data
set. Geoscience and Remote Sensing, IEEE Transactions on, 47(8), 2450-2466.
References
This poster was made possible by the National Oceanic and
Atmospheric Administration, Office of Education Educational
Partnership Program award NA11SEC4810004. Its contents are
solely the responsibility of the award recipient and do not
necessarily represent the official views of the U.S. Department of
Commerce, National Oceanic and Atmospheric Administration.
Acknowledgments
ρ0.66
s
= f (MVI,Θ,ρ2.12
s
)
ρ0.47
s
= f (ρ0.66
s
)