Simultaneous Retrieval
of Aerosol and Land
Bidirectional Reflectance
from MODIS
GEST UMBC/NASA GSFC, July 2, 2010
MAIAC Products (1 km, gridded)
Atmosphere
• Cloud Mask
• Water Vapor
• AOT & fine mode
fraction
Surface
MAIAC Products (1 km, gridded)
• Parameters of RTLS BRF
model
• Surface Reflectance (BRF)
• Dynamic Land-Water-
Sn...
Multi-Angle Implementation of
Atmospheric Correction (MAIAC)
Queue of
K days

New
Granule
2. LTP: Retrieve
Water Vapor
(...
Aerosol Retrieval Algorithm
2
),(






 




B
TheorMeas
AOTRR
rmse
 Compute AOTB and coarse mode fraction...
Aerosol Retrieval Algorithm



TOAReflectance
I
I
, mBlue Red SWIR
=0, Fine mode
=0.2, Low % of Coarse mode
=0.8, ...
Dust Storms – UAE 2004
Desert Dashti MargoMAIAC RGB NBRF (VZA=0, SZA=45 )
AERONET Validation, 9 yrs. of TERRA Data
The accuracy of MAIAC and MOD04 is generally similar over green
and relatively da...
Summary
• Developed a new algorithm MAIAC based on a
time series and imagery processing.
• The algorithm is generic and wo...
Upcoming SlideShare
Loading in …5
×

Modis 2010

509 views

Published on

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
509
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Modis 2010

  1. 1. Simultaneous Retrieval of Aerosol and Land Bidirectional Reflectance from MODIS GEST UMBC/NASA GSFC, July 2, 2010
  2. 2. MAIAC Products (1 km, gridded) Atmosphere • Cloud Mask • Water Vapor • AOT & fine mode fraction
  3. 3. Surface MAIAC Products (1 km, gridded) • Parameters of RTLS BRF model • Surface Reflectance (BRF) • Dynamic Land-Water- Snow Mask
  4. 4. Multi-Angle Implementation of Atmospheric Correction (MAIAC) Queue of K days  New Granule 2. LTP: Retrieve Water Vapor (NIR Algorithm) 3. QB, LTP: Cloud Mask (Covariance- Based Algorithm) 1. Grid L1B Data and Split in Tiles NO Use MODIS Dark Target Algorithm Is B7 BRF known? YES 7. QP: Retrieve BRF and albedo in reflective bands. Backup: Lambertian retrieval 6. LTP: Retrieve Fine Mode Fraction 5. LTP: Retrieve B3 AOT using known surface BRF, B=7bB 4. QB: Retrieve Spectral Regression Coefficients in Blue band B3 (bB). QB:Is Snow Detected? 4a. Retrieve Snow sub-pixel Fraction and Snow Grain Size
  5. 5. Aerosol Retrieval Algorithm 2 ),(             B TheorMeas AOTRR rmse  Compute AOTB and coarse mode fraction  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 .  Algorithm: Fit Blue band to find AOTB for given , and find  by minimizing 7B ijij Blue ij b   )(ij
  6. 6. Aerosol Retrieval Algorithm    TOAReflectance I I , mBlue Red SWIR =0, Fine mode =0.2, Low % of Coarse mode =0.8, High % of Coarse mode Water Cloud model AOT
  7. 7. Dust Storms – UAE 2004 Desert Dashti MargoMAIAC RGB NBRF (VZA=0, SZA=45 )
  8. 8. AERONET Validation, 9 yrs. of TERRA Data The accuracy of MAIAC and MOD04 is generally similar over green and relatively dark parts of the world. MAIACMOD04 AERONET
  9. 9. Summary • Developed a new algorithm MAIAC based on a time series and imagery processing. • The algorithm is generic and works over both dark and bright surfaces. • The suite of products includes 1 km AOT/coarse mode fraction and BRF/albedo. • MAIAC successfully passed NASA HQ ATBD review and will be tested operationally on MODAPS.

×