Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA...
If you can't read please download the document
Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore
Dust Detection in MODIS Image Spectral Thresholds based on Zhao
et al., 2010 Pawan Gupta NASA Goddard Space Flight Center
GEST/University of Maryland Baltimore County NASA ARSET Air Quality
Training Salt Lake City, April 22-25, 2013
Slide 2
Global Dust Sources
Slide 3
Dust Source Strength Global dust emission strength is estimated
using data on dust loading in the atmosphere, surface material
characteristics, marine sediments, and numerical dust transport
models. Recent studies estimate that global dust-emission rates
falls within a range from less than 1000 to 3000 Tg yr 1 Estimates
show a wide range of values, reflecting differences in modeling
procedures, model resolution, the considered time scale, and
specification of the source areas. About 80% of the dust is from
the Northern Hemisphere. Seven to twenty percent of the dust
emissions are less than 1 m in diameter (Cakmur et al., 2006;
Schulz et al., 1998) The world's largest source of dust is the
Sahara Desert - 160 to 760 Tg yr 1 Removal of dust from atmosphere
is by direct contact with the surface without precipitation (dry
deposition), or through scavenging by cloud droplet and
precipitation (wet deposition).. Current estimates of the
atmospheric dust loading range from less than 10 Tg to 35 Tg, an
uncertainty factor of about 34.
Slide 4
Available Satellite Aerosol Products for Dust Detection Work
well over ocean but not over land Good for qualitative purpose only
Only available over dark land targets (not available over desert s)
Lack of information on scattering aerosols Sensitive to absorbing
aerosols (fine dust) more sensitive to elevated aerosols Large
footprint cloud contamination Sensitive to small mode aerosols Can
be used with MODIS data to separate dust from non-dust aerosols
Available over both dark and bright targets Narrow swath (almost
point measurement) Very limited global coverage, Larger
uncertainties in retrieved data sets Very good to estimated
vertical distribution of aerosols, Limited swath width (360km) It
can separate dust and non-dust aerosols NO daily observations for
air quality Sensor Product Comment Available over land bright
targets Angstrom coefficient & Single Scattering Albedo also
can be used to detect dust (Ginoux et al., 2010) s)
Slide 5
Spectral Signature Qu et al., 2006
Slide 6
Spectral Response of Dust and Clouds Xie et al., 2009 BT11-BT12
>0.0 for clouds BT11-BT12
Dust Detection Test # 1 Pixel should pass these two conditions
- R 0.47m, R 0.64m, R 0.86m, R 1.38m > 0 BT 3.9m, BT 11m, BT 12m
> 0K If pixel deos not pass these tests Flaged as bad data
Pixels in RED Remove bad pixels points from the image
Slide 15
Dust Detection Test # 2 Remove cloudy pixels from the image BT
11m BT 12m 0.5K & BT 3.9m BT 11m 20K & R 1.38m < 0.055
If pixel failed any of these test..then flag is set as other class
Pixels in BLUE
Slide 16
Dust Detection Test # 3 Dust Detection - If MNDVI 0.005 then
DUST If BT 3.9m - BT 11m >= 25K - then DUST DUST PIXELS are in
YELLOW
Slide 17
Dust Detection Test # 4 Thick Dust Detection -- If MNDVI