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

4.1.2 Spectral curves
According to selected cases, most Asian dust storms originate and dissipate within
the inland areas. In this manner, the dust events selected as training data are roughly
divided into two categories using land surface types dataset: dust storm over bright
surface and over dark surface. The spectral curves of these two categories of dust are
shown in Fig. 4.1, as well as those of clouds and clear scenes. The spectral response at
each band is the statistical mean value of all training data, and the standard deviation as
well. In Fig. 4.1, cloud shows a high reflectivity in band 3 and low reflectivity in band 7,
while dust displays a reverse trend. In thermal spectrum, cloud has much lower BT than
dust. This difference assists their separation significantly. In Figs. 4.2 and 4.3, the
relatively large response difference between dust and clear scene over both dark and
bright surfaces is found at the band 1 (red channel). Additionally, the BTD (3.7, 11) value
of dust is larger than that of clear scenes.
4.1.3 Band selection
The BT in LWIR between dust and cloud is obviously different. Ackerman (1997)
proposed an “IR split windows” technique to discriminate the dust storm layer from
cloud using the brightness temperature difference between the 11 and the 12μm regions
of the spectrum. As illustrated in smoke detection section, the BTD (12, 11) value is
negative for cloud. The BTD (12, 11) value is positive for dust based on the Ackerman’s
conclusion and also spectral curves in Fig. 4.1.
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