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

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The dust has a high reflectivity at band 7 but a low reflectivity at band 3 (blue band). Contrarily, blue band is sensitive to cloud but band 7 is insensitive to cloud. This inverse spectral feature helps greatly for distinguishing dust and cloud. Qu et al. (2006) raised NDDI to detect dust, with the formula NDDI = (R3 − R7) / (R3 + R7). The BTD (3.7, 11) is selected to separate dust from surface scenes. Both dust and surface scenes have a similar BT at the band 20 (3.7μm) but dust has much lower BT at the LWIR than surface scenes. Among bands in LWIR, the BTD of band 31 (11μm) relative to band 20 has the largest statistical difference between dust and surface scenes. Moreover, in reflective solar spectrum the relative large reflectance difference between dust and surface scenes (both bright and dark surfaces) is found at band 1. Therefore, the single reflectance of band 1 is picked up to differentiate dust from surface scenes. Overall, the BTD (12, 11) and NDDI are chosen for discriminating dust and cloud, while BTD (3.7, 11) and the single reflectance of band 1 are chosen for identify dust over both dark and bright surfaces. In each branch, both RSBs and TEBs are used. 51
Reflectance Reflectance 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 240 Dust over bright surface 0.1 Dust over dark surface Cloud 0 B8 B3 B4 B1 B2 B7 B20 B29 B31 B32 220 412 469 555 645 858 2130nm; 3.75 8.55 11.03 12.02μm 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 Response curve of dust and cloud by statistical analysis Figure 4.1: Response curves of dust and cloud. Response curve of dust and clear dark pixels by statistical analysis 0.55 330 Dust over dark surface 0.5 Clear dark surface 320 B8 B3 B4 B1 B2 B7 B20 B29 B31 B32 260 412 469 555 645 858 2130nm; 3.75 8.55 11.03 12.02μm Figure 4.2: Response curves of dust and clear dark pixels. 52 340 320 300 280 260 310 300 290 280 270 BT (K) BT (K)
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Detection of Smoke and Dust Aerosol
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DEDICATION This is dedicated to my
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TABLE OF CONTENTS Page LIST OF TABL
Page 7 and 8:
LIST OF TABLES Table Page Table 1.1
Page 9 and 10:
Figure 6.2 The UVAI images and the
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VFM Vertical Feature Mask VIS Visib
Page 13 and 14: are validated not only visually wit
Page 15 and 16: 1.1 Importance of Studying Smoke an
Page 17 and 18: 1.3 Objectives and Scopes The main
Page 19 and 20: originalities, and discussions of f
Page 21 and 22: 1.6 Principal Results The principle
Page 23 and 24: CHAPTER 2 REVIEW OF APPROACHES FOR
Page 25 and 26: cooperatively: the reflectance at 0
Page 27 and 28: more information about dust charact
Page 29 and 30: CHAPTER 3 SMOKE AEROSOL DETECTION W
Page 31 and 32: with a two-side scan mirror which r
Page 33 and 34: 3.1.2 MODIS data The 55 0 scan angl
Page 35 and 36: Sun Satellite Figure 3.2: Basic rad
Page 37 and 38: 1 0 /2 b ( 0 0, 0; , ) k1 e
Page 39 and 40: 3.2.3 Training data collection Surf
Page 41 and 42: Reflectance Reflectance Response cu
Page 43 and 44: Figure 3.6: The normalized ratio of
Page 45 and 46: all pixels over both land and ocean
Page 47 and 48: NDDI 1 0.8 0.6 0.4 0.2 0 -0.2 Figur
Page 49 and 50: MODIS L1B measurements Pixels over
Page 51 and 52: is clear that most of smoke plumes
Page 53 and 54: a b c b Figure 3.10: Smoke images o
Page 55: 3.4.2 California 2007 fire The 2007
Page 58 and 59: The multi-spectral approach is test
Page 60 and 61: 4.1 Physical Principle of Dust Aero
Page 62 and 63: Aqua 2006 102-04/12 04:15 Terra 200
Page 66 and 67: 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2
Page 68 and 69: 7 a Dust pixel 0.5 b 6 Cloud pixel
Page 70 and 71: 4.3 Results Three dust events occur
Page 72 and 73: c c Figure 4.6: Dust storm over TAK
Page 74: 4.4 Chapter Summary In this chapter
Page 77 and 78: operated in the A-train orbit with
Page 79 and 80: 5.1.2 CALIPSO measurements and VFM
Page 81 and 82: Altitude (km) 20 15 10 5 UTC: 2006-
Page 83 and 84: After spatial registration, the ove
Page 85 and 86: Figure 5.4(a) The MODIS true color
Page 87 and 88: Altitude (km) BTD (K) 20 15 10 15 4
Page 89 and 90: in the same A-train orbit with simi
Page 91 and 92: Aqua spacecrafts are operated in th
Page 93 and 94: the Fig. 6.1a. For easy comparison,
Page 95 and 96: esolutions, the number of smoke pix
Page 97 and 98: 6.1.4 Validation of dust monitoring
Page 99 and 100: Figure 6.3 Validation of dust image
Page 101 and 102: eflectance in red band. Therefore,
Page 103 and 104: Altitude (km) 20 15 10 5 UTC: 2006-
Page 105 and 106: image at the same location. The rea
Page 107 and 108: accuracy could be increased further
Page 109 and 110: FPAs and cold FPAs (because of diff
Page 111 and 112: development of future remote sensin
Page 113 and 114: Figure 7.3: SRCA spatial mode retic
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7.1.3.2 Date Source MODIS L1B data
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characterization. Only the measurem
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these bands, including reflective s
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different spectral bands are slight
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SZA (Degree) SZA (Degree) 65 60 55
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7.2.1.2 Real Impact on L1B Measurem
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7.2.2.1 Theoretical impact analysis
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spatial characterization, the bigge
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Difference NDDI with spatial correc
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8.1 Conclusions CHAPTER 8 CONCLUSIO
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Based on the detection results, the
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3) Validating aerosol detection qua
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REFERENCES Ackerman, S. A., 1989, U
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imagery, Atmospheric Environment, v
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Salomonson, V. V., Privette, J. L.,
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Martinez, A., and Gros, G., 2007-10
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Wang W., Qu, J. J., Liu, Y., Hao, X
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CURRICULUM VITAE Yong Xie is a Ph.D
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