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

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Fig. 7.8 shows that the NI value varies with the reflectance of two bands at a given reflectance change of band b. The x and y axes represent the reflectances of bands a and b, and z axis (vertical axis) stands for the variation of index value. The change of NI value decreases with the increasing difference between Ra and Rb. The maximum variation of NI value is up to 0.0527 if Rb has a negative ten percent response shift. The NI value change could reach to 0.1779 if there is a negative 30% response shift of Rb. Figure 7.8: NI change with negative 10% response change of band b 7.2.2.2 Impact on detection of dust aerosol The NDDI is an index proposed for the identification of dust aerosol. It is the normalized ratio of band 3 and band 7, in which the former band is located on the warm FPA and the latter one is located on the cold FPA. With the results of the SRCA on-orbit 115
spatial characterization, the biggest mis-registration is observed between the warm FPA and the cold FPA. The existence of spatial shift between these two bands, up to 320 m along-track and 265 m along scan, will produce undesired effects, and consequently, lead to less accurate NDDI value and dust aerosol detection product. If the NDDI is calculated with 500 m resolution measurements, the mis-registration between two bands is up to 0.64 pixels in track direction and 0.53 pixels in scan direction. The tentative correction is performed by shifting one pixel in track direction while keep unchanged in scan direction since the mis-registration is more serious along-track. A dust storm is displayed in Fig. 7.9 (only part of dust storm) by MODIS true color image. The dust storm is prevalent in the left and right sides of the image, divided by the cloud in the middle. Fig. 7.10 and Fig. 7.11 present the difference of NDDI values and dust aerosol detection. The three subplots in Figs. 7.10 and 7.11, from left to right, are the results with and without correction and their comparison difference. In Fig. 7.11, the total number of dust aerosol pixels detected is 15,622 in the sampled area, listed in Table 7.3. After correction, the number of detected dust aerosol pixels decrease to 15,455. There are 15,270 pixels are labeled as dust aerosol in two results, equaling 97.747%. About 185 non-dust aerosol pixels (cloud and other pixels) are labeled as dust aerosol pixels after correction, and correspondingly 352 dust aerosol pixels are identified as non-dust pixels in the corrected results. The difference is up to 3%. The correlation coefficients, r 2 , of all three surface scenes increase after spatial correction. This increase demonstrates that the quality of science data products can be improved by spatial correction. 116
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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
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0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2
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7 a Dust pixel 0.5 b 6 Cloud pixel
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4.3 Results Three dust events occur
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c c Figure 4.6: Dust storm over TAK
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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
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: 7.2.2.1 Theoretical impact analysis
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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