i Detection of Smoke and Dust Aerosols Using Multi-sensor Satellite ...

more information about dust characteristics. Qu et al. (2006) used Normalized Difference
Dust Index (NDDI), a normalized ratio of 2.1µm band and blue band, to detect dust
storms and monitor the moisture change of dust. NDDI has advantages because of the
high sensitivity of MODIS 2.1µm band to moisture content. Recently, several studies
show that it is feasible to detect Saharan dust over land using BT in thermal infrared
spectra (Cavtenet et al., 1992; Legrand et al., 1987; Legrand et al., 1989; Legrand et al.,
1992; Legrand et al.,2001) Hao et al, (2007) proposed a thermal infrared index to detect
Saharan dust storms by combining fours MODIS TEBs. A correlation of the BT between
11 µm and 3.7 µm bands for dust outbreaks was proposed by Ackerman (1989). He
developed a tri-spectral (8, 11 and 12 µm) technique later for detecting dust over water
and for distinguishing dust plumes from water/ice clouds (Ackerman, 1997). An
automatic multi-spectral approach for detection of dust storm in the northwest of China is
developed by Han et al. (2005). In his approach, a set of indices were used to separate
dust from cloud, snow, and land with several RSB measurements. However, most of
approaches for dust (also for smoke) detection chooses either RSB measurements or TEB
measurements.
For retrieval of dust parameters such as aerosol optical thickness, current algorithm
works only over dark surface. The reason is that the algorithm is based on the empirical
relationship between MODIS VIS bands and Small Wavelength Infrared (SWIR) bands
(Kaufman 1997).
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