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

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CHAPTER 5 AEROSOL DETECTION BY COMBINING CALIPSO AND MODIS MEASUREMENTS Multi-sensor remote sensing, by its global and repetitive measurement capability, provides a large amount of valuable information for solving problems in various Earth observation applications. This massive amount of data allows the same features on the Earth to be observed by the several different sensors, yielding complementary information for more accurate measurements. CALIPSO mission studies the role of aerosols and cloud in the Earth’s climate system (Winker et al., 2003). It provides the vertical information about aerosols and cloud. The CALIPSO level 2 Vertical Feature Mask (VFM) (Vaughan et al., 2004) product provides detail vertical information about aerosol and cloud layer. It is easily used to separate dust from ground surface. However, limited by its spectral coverage, the misclassification may appear by labeling heavy dust aerosols as cloud in VFM product and reported by recent data quality statement (CALIPOS Lidar Level 2 Vertical Feature Mask products Version 2.01, January 25, 2008). The possible reason is that the intrinsic scattering properties of dust layers are similar to those of cloud in certain conditions. The Aqua MODIS has good spatial resolution and spectral coverage for detection large scale aerosol. It can provide the planar information about the dust storm. Both sensors are 63
operated in the A-train orbit with similar local equatorial crossing time. To fully take advantages of multi-sensor system, these two sensors, MODIS and CALIPSO, are combined together for Asian dust detection. 5.1 CALIPSO Instrument and Measurements CALIOP is a lidar (LIght Detection And Ranging) sensor, namely a laser radar, providing information on the vertical distribution of aerosols and cloud as well as their optical and physical properties (Winker et al., 2003). Current remote sensing capabilities can be enhanced by the CALIPSO by providing global, vertically-resolved measurements of aerosol distribution. Cloud and aerosol layers can be discriminated easily using the magnitude and spectral variation of the lidar backscatter (Liu et al., 2004). 5.1.1 CALIPSO instrument The CALIPSO mission was launched on April 28, 2006. It is flying at the same orbit as Aqua spacecraft with 1 minute and 15 seconds behind. The CALIPSO payload consists of three instruments: a two-wavelength, polarization-sensitive lidar (CALIOP), the Image Infrared Radiometer (IIR) and the Wide Field Camera (WFC) (Winker et al., 2003). The CALIPO is a two-wavelength polarization-sensitive lidar. It is designed to acquire the vertical profiles of aerosols with backscatter signal at 532 nm and 1064 nm in both daytime and nighttime, as well as profiles of linear depolarization at 532 nm. The IIR with three channels in the infrared window region is optimized for retrievals of cirrus particle size. The WFC has one visible channel that provides meteorological context and a means to accurately register CALIPSO observations to those from MODIS on the Aqua 64
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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
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
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
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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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