i Detection of Smoke and Dust Aerosols Using Multi-sensor Satellite ...
i Detection of Smoke and Dust Aerosols Using Multi-sensor Satellite ...
i Detection of Smoke and Dust Aerosols Using Multi-sensor Satellite ...
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Total 4.656% 2<br />
35<br />
34.577% 2<br />
1<br />
The threshold <strong>of</strong> BTD (3.7, 12) is 15 K for pixels over l<strong>and</strong> <strong>and</strong> 9 K for pixels over<br />
ocean.<br />
2<br />
The total errors <strong>of</strong> both over l<strong>and</strong> <strong>and</strong> over ocean are not equal to the summation <strong>of</strong><br />
errors from each test because the overlap exists among tests.<br />
3<br />
The pseudo-NDVI <strong>and</strong> NDDI is only applied over l<strong>and</strong>.<br />
3.3.2 Flowchart<br />
Fig. 3.8 is the flowchart <strong>of</strong> smoke detection algorithm. The whole swath <strong>of</strong> L1B<br />
measurements is input <strong>and</strong> divided into two branches with l<strong>and</strong>/sea mask product stored<br />
in L1A file: smoke over l<strong>and</strong> <strong>and</strong> smoke over ocean. In l<strong>and</strong> branch, the process is<br />
comprised with four tests, orderly for classifying vegetation, soil, cloud, <strong>and</strong> noise<br />
(including water). The first test employs a pseudo-NDVI to examine vegetated pixel. The<br />
NDDI is applied in the second test for differentiating smoke from bright surface. In the<br />
third test, the BT11 <strong>and</strong> BTD (3.7, 11) are used to discriminate smoke from cloud, as well<br />
as the reflectance at the 1.38μm b<strong>and</strong>. The goal <strong>of</strong> last module is to filter out the noise<br />
pixels (mainly water pixels) with the normalized Ratio (R3, R8) <strong>and</strong> single <strong>of</strong> reflectance<br />
<strong>of</strong> b<strong>and</strong> 8. In ocean branch, the process is relatively easy, which is comprised <strong>of</strong> two steps<br />
only. At the end <strong>of</strong> process, an additional test named “noise-elimination test” is executed<br />
to further filter noise pixels. In view <strong>of</strong> the smoke continuity, although a pixel is<br />
identified as smoke pixel but it is not close to other smoke pixels, this pixel will be<br />
considered as noise pixels <strong>and</strong> deleted from smoke images.