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

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Figure 5.8: The total attenuated backscatter signal profile at 532 nm in two locations. Table 5.3: Vertical and horizontal information about dust aerosols occurred in 2008 spring season. Case Date (UTC time) Height Width Average depth Coverage (km) 75 (km) (km) (km 2 ) 1 Mar 29, 2008, 7:05 1.6 ~ 4.7 200 1.2 239113 2 Mar 30, 2008, 7:45 1.4 ~ 3.2 330 1.2 184081 3 Apr 19, 2008, 7:20 3.2 ~ 5.0 161 1.8 240236 4 Apr 20, 2008, 8:05 1.7 ~ 4.7 400 0.9 301729 5 Apr 28, 2008, 7:15 1.4 ~ 5.0 313 1.4 206002 6 May 5, 2008, 7:25 2.9 ~ 6.1 280 1.3 117019 5.3 Chapter Summary The dust aerosol detection with multi-sensor measurements, such as MODIS and CALIPSO, is performed in this chapter. Since both CALIPSO and Aqua MODIS operated
in the same A-train orbit with similar local passing time, the mis-registration of temporal is quite small and ignored in the algorithm. A simple approach is developed combining the CALIPSO VFM product and MODIS BTD (12, 11) measurements. With the CALIPSO VFM product, the aerosol and cloud layer are easily separated from ground scene types. Then dust aerosol is further separated from cloud with MODIS BTD (12, 11) values, since dust aerosol and cloud have opposite values. After spatial registration, those layers labeled as cloud in CALIPSO VFM but having positive BTD (12, 11) values are identified as heavy dust aerosol. Several cases are selected to test the algorithm; the accuracy is quite good by compare with true color images. Based on this approach, several dust storms occurred during spring season in northwest China is summarized. A few important parameters of dust aerosol are retrieved, including the altitude, thickness, location, and spatial coverage and distribution. 76
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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
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LIST OF TABLES Table Page Table 1.1
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Figure 6.2 The UVAI images and the
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VFM Vertical Feature Mask VIS Visib
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are validated not only visually wit
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1.1 Importance of Studying Smoke an
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1.3 Objectives and Scopes The main
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originalities, and discussions of f
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1.6 Principal Results The principle
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CHAPTER 2 REVIEW OF APPROACHES FOR
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cooperatively: the reflectance at 0
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more information about dust charact
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CHAPTER 3 SMOKE AEROSOL DETECTION W
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with a two-side scan mirror which r
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3.1.2 MODIS data The 55 0 scan angl
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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 64 and 65: The dust has a high reflectivity at
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: Altitude (km) BTD (K) 20 15 10 15 4
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
Page 115 and 116: 7.1.3.2 Date Source MODIS L1B data
Page 117 and 118: characterization. Only the measurem
Page 119 and 120: these bands, including reflective s
Page 121 and 122: different spectral bands are slight
Page 123 and 124: SZA (Degree) SZA (Degree) 65 60 55
Page 125 and 126: 7.2.1.2 Real Impact on L1B Measurem
Page 127 and 128: 7.2.2.1 Theoretical impact analysis
Page 129 and 130: spatial characterization, the bigge
Page 131 and 132: Difference NDDI with spatial correc
Page 133 and 134: 8.1 Conclusions CHAPTER 8 CONCLUSIO
Page 135 and 136: Based on the detection results, the
Page 137 and 138: 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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