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

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7 a Dust pixel 0.5 b 6 Cloud pixel BT12 - BT11 5 4 3 2 1 0 -1 -2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 NDDI 80 c d BT3.7 - BT11 70 60 50 40 30 20 10 Dust pixel over bright surface Clear pixel of bright surface 0 -1.4 -1.3 -1.2 -1.1 -1 -0.9 -0.8 -0.7 Ln (R1) 70 e Clear pixel of dark surface -0.6 f 60 Dust pixel over dark surface -0.8 -1 BT3.7 - BT11 50 40 30 20 10 0 -2.5 -2 -1.5 -1 -0.5 Ln (R1) NDDI Ln (R1) Ln (R1) Figure 4.4 Statistical analyses of training data for deciding thresholds. (a)BTD (12, 11) values for dust and cloud; (b) NDDI values for dust and cloud; (c) BTD (3.7, 11) values for dust and clear pixels over bright surface; (d) Ln (R1) values for dust and clear pixels over bright surface; (e) BTD (3.7, 11) values for dust and clear pixels over dark surface; (f) Ln (R1) values for dust and dark pixels over dark surface. 55 0 -0.5 Dust pixel Cloud pixel -1 -2 -1 0 1 2 3 4 5 6 BT12 - BT11 -0.6 -0.7 -0.8 -0.9 -1 -1.1 -1.2 -1.3 -1.4 Dust pixel over bright surface Clear pixel of bright surface -1.5 10 20 30 40 50 60 70 BT3.7 - BT11 -1.2 -1.4 -1.6 -1.8 -2 -2.2 -2.4 Clear pixel of dark surface Dust pixel over dark surface -2.6 0 10 20 30 40 50 60 70 BT3.7 - BT11
MODIS L1B measurements Dust aerosol over bright surface Ln(R1)≤-1.2 & BT20-BT31≥25K Dust storm pixels Figure 4.5: The flowchart of the dust storm detection Table 4.4: The tests and thresholds used for detecting dust storm; and sensitivity analysis based on the selected dust storm events in China during years 2001-2007. Bright surface Dark surface Class Type Threshold test Value Error (%) Value Error (%) Dust over land Cloud Total Y N N BT 3.7 –BT 11 & Ln (R1) (B7-B3) / (B3+B7) & BT 12 –BT 11 (R7-R3) / (R3+R7) ≥ 0.0 & BT32-BT31 ≥ 0.0 Y Dust & surface scenes Land pixels Dust & Noise Discrete pixel? 56 25 K -1.2 0.0 0 K Dust aerosol over dark surface 2.843 2.294 1.886 0.334 1 5.976 20 K -1.6 0.0 0 K 2.623 4.416 12.484 0.845 1 17.717 1 The total errors of both over bright and dark surface are not equal to the summation of errors from each test because the overlap exists among tests. N Y Y N Cloud pixels Pre-store surface types Ln(R1)≤-1.6 & BT20-BT31≥20K Noise pixels
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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
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 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 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 and 116: 7.1.3.2 Date Source MODIS L1B data
Page 117 and 118: 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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