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

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CHAPTER 1 INTRODUCTION This dissertation is aiming to develop a multi-spectral algorithm for detecting smoke and dust aerosols in a timely manner with MODoderate resolution Imaging Spectroradiometer (MODIS) measurements. According to the spectral and statistical analyses, a new combination of multiple visible (VIS), near infrared (NIR), and short- and long-wave infrared bands is selected for smoke and dust detections. The results are validated not only visually with MODIS RGB (Red, Green, and Blue) true color images, but also quantitatively with multi-sensor measurements, such as Ozone Monitoring Instrument (OMI) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). On the other hand, detection of dust and smoke with multi-sensor measurements, MODIS and CALIPSO, is also executed in this dissertation. The three dimensional information of dust aerosol is obtained by combining both sensors’ measurements. Additionally, sensitivity analysis is performed to estimate the impact of MODIS spatial characterization change on Level 1B (L1B) measurements and dust detection results. 1
1.1 Importance of Studying Smoke and Dust Aerosols The study of atmospheric aerosols has become a very interesting topic in recent years because of the evidence of their impacts on climate change (Estelles et al., 2007). The aerosol, deserving the same consideration as greenhouse gases, plays important roles in atmospheric chemistry, cloud microphysics, temperature, and radiation balance in the lower atmosphere (Li et al., 2001). Smoke from wildfires and dust storm are two kinds of frequently occurred natural phenomena over the continents. They may have potential impacts on the climate, environment, and air quality. Particulate matter from smoke is the major pollutant of concern, which is a general term for a mixture of solid particles and liquid droplets found in the air. Dust storm, usually aroused in arid and semi-arid regions, can carry large quantity of dust and move forward like an overwhelming tide to destroy crop plants, ruin the mining and communication facilities, weather vestiges, damage small villages, reduce visibility and human’s daily activity, and impact the aircraft and road transportation. It pollutes the atmosphere and air quality, influences cloud formation (James et al., 2007), obscures the sunlight, and reduces the temperature. Some dust storm can suspend on the air for several days and travel by the wind far from the place where it originated. Recently, several researches observed that the heavy dust storm can impact the formation and evolution of hurricane (Dunion and Velden, 2004; Wu et al., 2006). 1.2 Statement of Problems Due to the importance of smoke and dust aerosols in climate, environment, air 2
Page 1 and 2: Detection of Smoke and Dust Aerosol
Page 3 and 4: DEDICATION This is dedicated to my
Page 5 and 6: TABLE OF CONTENTS Page LIST OF TABL
Page 7 and 8: LIST OF TABLES Table Page Table 1.1
Page 9 and 10: Figure 6.2 The UVAI images and the
Page 11 and 12: VFM Vertical Feature Mask VIS Visib
Page 13: are validated not only visually wit
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
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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
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Figure 5.4(a) The MODIS true color
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Altitude (km) BTD (K) 20 15 10 15 4
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in the same A-train orbit with simi
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Aqua spacecrafts are operated in th
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the Fig. 6.1a. For easy comparison,
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esolutions, the number of smoke pix
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6.1.4 Validation of dust monitoring
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Figure 6.3 Validation of dust image
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eflectance in red band. Therefore,
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Altitude (km) 20 15 10 5 UTC: 2006-
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image at the same location. The rea
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accuracy could be increased further
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FPAs and cold FPAs (because of diff
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development of future remote sensin
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Figure 7.3: SRCA spatial mode retic
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7.1.3.2 Date Source MODIS L1B data
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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
Page 149:
CURRICULUM VITAE Yong Xie is a Ph.D
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