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

cooperatively: the reflectance at 0.63 µm and 3.7 µm channel, the Bright Temperature
(BT) difference between 3.7 µm and 11 µm channel, and the BT difference between
anticipated clear-sky value and measured value at 11 µm channel. Li et al. (2001)
presented a multi-threshold method for automated smoke plume detection using AVHRR
measurements based on the neural networks. The shortcoming of using AVHRR is that
AVHRR has only five channels, which is inefficient for smoke detection.
Chrysoulakis et al. (2007) proposed a multi-temporal change detection approach
using two images at the same target area on the different time. By comparing two images
(one is acquired during the fire event and the other is acquired before fire event), the
important anomalies in NDVI and infrared radiances were detected hence to detect the
core of plume. Then the plume core was enlarged to include the complete smoke area
through identifying a pixel as plume pixel if it located spatially and spectrally at the
neighborhood of the initial plume core. The new developed one offers a novel ways for
smoke monitoring. However, this approach need find a clear day before the occurrence of
smoke. The Bidirectional Reflectance Distribution Function (BRDF) issue is also need to
be taken into account.
The current MODIS aerosol retrieval algorithm uses the dark target approach
(Kaufman et al. 1997, Kaufman et al. 1997) with two assumptions: 1) the aerosol is
transparent to most aerosol types (except dust) at 2.1 µm so that this channel can be used
to detect dark surface targets; 2) the surface reflectance at 0.47 µm and 0.64 µm channel
could be retrieved by that at 2.1 µm with the ratio 0.25 and 0.5. With the assumptions, the
Aerosol Optical Thickness (AOT) and particle size parameters were retrieved at 0.47 µm
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