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

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statistical can provide reasonable results, its accuracy depends on the number of training data. Moreover, the thresholds used in the algorithm are usually site-specific, which limit the application of the algorithm. One of the significant achievements of this dissertation is to assess the impacts on the L1B measurements and corresponding science data products caused by the instrument itself. However, only the influence from the spatial characterization change is performed in this dissertation. It is very beneficial to estimate the impacts from all three kinds of characterization changes. 8.3 Future Works The approach for detecting smoke and dust aerosol with satellite remote sensing could be enhanced with further studies. Recommendations for future researches fall into four categories, showing as follow: 1) Adding more precise site-specific information. Currently, the surface is only separated into land and ocean for smoke detection, and divided into dark and bright surface for dust storm monitoring. The more strict classification of surface features and site-specific thresholds can enhance the accuracy of algorithm significantly. It is very valuable to build up a lookup table to store the site-specific thresholds for whole global. 2) Enhancing the algorithm by using multi-sensor measurements. In this dissertation, only CALIPSO and MODIS are combined for dust detection. Integrating more sensors can enhance the detection approach and improve the detection accuracy. 123
3) Validating aerosol detection quantitatively with ASTER measurements and ground measurements. Since ASTER instrument is boarded on the Terra spacecraft, same platform as MODIS, it can provide more rigorously quantitative validation. Additionally, the sunphotometer offers trustable ground measurements for validation. 4) Assessing the impact of sensor radiometric and spectral characterization on aerosol detection. After a long term operation in its orbit, the radiometric and spectral characterization of instrument may have small changes which could cause uncertainties on L1B data and consequently on science data products. Sensitivity analysis helps us understand more about the impact from instrument itself. On the other hand, some useful researches need to be implemented based on this dissertation in near future include: 1) tentative retrieving the AOD based on the detection results; 2) gathering the smoke/dust information in a long time range. With long term aerosol information, it is feasible to analyzing their distribution, the motion, height, and width, even the seasonal variation. 124
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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
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operated in the A-train orbit with
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5.1.2 CALIPSO measurements and VFM
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Altitude (km) 20 15 10 5 UTC: 2006-
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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,
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Page 97 and 98: 6.1.4 Validation of dust monitoring
Page 99 and 100: Figure 6.3 Validation of dust image
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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: Based on the detection results, the
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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