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

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(a) (b) ENVI RGB image (500m spatial resolution) (d) Figure 7.4: The color images of selected site for spatial characterization. (a) The MODIS RGB image (Latitude: 26.5 0 N-28.0 0 N, Longitude: 21.5 0 E-22.5 0 E) with 500 m resolution measurement; (b), (c), and (d) are Google images with difference enlargements for the same site (Courtesy of Google). The consecutive measurements over the selected targets with high contrast features can be used to track the BBR shifts for MODIS spectral bands during the entire mission. Only daytime measurements are used in this spatial characterization study for both RSB and TEB. Not all the measurements, however, are qualified for the sensor spatial 103 (c)
characterization. Only the measurements that satisfy two basic requirements are selected in this study for calculating the band-to-band spatial deviation. One requirement is that the measurements are from a clear scene free of clouds, dust storms, and snow in order to improve the accuracy. The other requirement is that the selected site must be located at the nadir area (sensor scan angle less than ±15 degrees) in the images. Otherwise there will be large distortion of the shape of circular dark targets due to the bowtie effect (Wolfe et al., 2002). 7.1.3.3 Algorithm The same target will be mapped by each band at different positions in the MODIS images because of the mis-registration between bands. The location difference of the same target between two bands is equivalent to the spatial deviation. Therefore, the core process of this ground target approach is to find the discrepancy of centroid locations of the same dark target observed by different spectral bands. When the scan mirror scans across the selected dark targets, the response of each band will change rapidly and form a series of signal valleys. Only those valleys with high signal contrast are collected for the determination of the spatial shift in order to improve the accuracy of the algorithm. For a given valley, the centroid position difference between band a and b, ΔPab, can be computed using Eq. 7.1. k k pb ( i) Rb ( vb , pb ( i)) pa ( i) Ra ( va, pa ( i)) Pab i1 k i1 k R ( v , p ( i)) R ( v , p ( i)) b b b a a a i1 i1 104 (7.1)
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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
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1 0 /2 b ( 0 0, 0; , ) k1 e
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3.2.3 Training data collection Surf
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Reflectance Reflectance Response cu
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Figure 3.6: The normalized ratio of
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all pixels over both land and ocean
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NDDI 1 0.8 0.6 0.4 0.2 0 -0.2 Figur
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MODIS L1B measurements Pixels over
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is clear that most of smoke plumes
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a b c b Figure 3.10: Smoke images o
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3.4.2 California 2007 fire The 2007
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The multi-spectral approach is test
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4.1 Physical Principle of Dust Aero
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Aqua 2006 102-04/12 04:15 Terra 200
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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 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
Page 115: 7.1.3.2 Date Source MODIS L1B data
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
Page 139 and 140: REFERENCES Ackerman, S. A., 1989, U
Page 141 and 142: imagery, Atmospheric Environment, v
Page 143 and 144: Salomonson, V. V., Privette, J. L.,
Page 145 and 146: Martinez, A., and Gros, G., 2007-10
Page 147 and 148: Wang W., Qu, J. J., Liu, Y., Hao, X
Page 149: CURRICULUM VITAE Yong Xie is a Ph.D
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