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

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measurements. 3) Finally, the uncertainty from sensor spatial characterization on dust storm detection result is assessed. With the on-orbit spatial characterization, mis-registration has been observed between MODIS bands and FPAs. The mis-registration affects the quality of not only L1B measurements but also dust aerosol detection results. The impacts of mis-registration on L1B measurements are relative small, but large on dust aerosol detection, about 3% in select cases. This assessment is very valuable for sensors without on-orbit spatial characterization capability. Table 1.1: Datasets and Sources Dataset Data Source Link MODIS L1A geolocation LAADS 1 MODIS L1B calibrated radiance LAADS 1 MODIS L2 Aerosol EDG 2 MODIS fire product EDG 2 Hyperion data USGS 3 OMI AI GES DAAC 4 CALIPSO dataset ASDC 5 1 LAADS: Level 1 and Atmosphere Archive and Distribution System 2 EDG: Earth Observing System Data Gateway 3 USGS: U.S. Geological Survey 4 GES DAAC: Goddard Earth Science Distributed Active Archive Center 5 ASDC: Atmospheric Science Data Center 9 http://ladsweb.nascom.nasa.gov/ http://ladsweb.nascom.nasa.gov/ http://edcimswww.cr.usgs.gov/pub/imswelcome/ http://edcimswww.cr.usgs.gov/pub/imswelcome/ http://eo1.usgs.gov/samplefire.php http://daac.gsfc.nasa.gov/Aura/OMI/index.shtml http://eosweb.larc.nasa.gov/JORDER/ceres.html
CHAPTER 2 REVIEW OF APPROACHES FOR DETECTING SMOKE AND DUST AEROSOLS In the past, several approaches for detecting smoke and dust aerosols using satellite remote sensing measurements have been developed, tested, and validated based on the different techniques. This chapter focuses on the review of different approaches so as to find their physical principles, and their advantages and limitations, which are beneficial for the development of a new robust algorithm. 2.1 Approaches of Smoke Aerosol Detection Smoke plume from biomass burning can generate forcing of climate by changing cloud microphysics and reflectance of sunlight (Kaufman and Fraser, 1997). The smoke particle is a good scatter of sunlight in visible spectrum and therefore reduces the visual visibility. There are two kinds of climatic impacts by smoke: one is warming effect on the atmosphere because smoke acts like greenhouse gases, and the other is cooling effect because smoke particulate is an excellent absorber and scatter of incoming solar radiation (Li, 1998). Satellite remote sensing has been demonstrated as a feasible tool for the 10
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 and 14: are validated not only visually wit
Page 15 and 16: 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: 1.6 Principal Results The principle
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 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
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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
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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