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

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7.3 Chapter Summary The mis-registration, or the BBR shift, has been observed between the bands and FPAs by the ground target approach and by the Spectro-Radiometric Calibration Assembly (SRCA). The impact analysis of MODIS BBR shift on MODIS L1B data and science data products is performed based on the SRCA on-orbit spatial characterization results. The quality of L1B data could be lessened by executing the SZA correction using mismatch SZA due to mis-registration. The impacts are different between two directions due to the strong bow-tie effect in scan direction. The practically largest relative errors of all RSBs for Aqua MODIS are less than 0.1% in the select case, which are negligible in actual applications. The spatial correction of L1B data is necessary if the BBR shift is too large to reduce the data quality significantly. The theoretical analysis shows that the influence of mis-registration on science data products is generally larger than that on L1B data. The NDDI, an index for dust aerosol identification, is selected to estimate the impact of mis-registration on science data product since. The influence on dust aerosol detection results is small at the homogenous or semi-homogenous areas but relative large at the mixed areas. The increase of correlation coefficient demonstrates that the quality of science data product can be improved by shifting a pixel in track direction. This study provides valuable information for sensors without spatial characterization capability and also for the specification design of future sensors. 119
8.1 Conclusions CHAPTER 8 CONCLUSIONS AND DISSCUSIONS In this dissertation, the multi-spectral algorithm for observing and monitoring smoke and dust aerosols over both bright and dark surfaces is developed by combining measurements of MODIS solar and thermal bands. Since the algorithm uses its own cloud module to separate the cloud, the algorithm can detect the smoke/dust at the areas close to or mixed with clouds. In the algorithm, the spectral curves of several major scene types in both solar and thermal spectrum, such as smoke, dust, cloud, and vegetated surface and non-vegetated surface, are derived statistically from large quantity of training data. According to spectral analysis, the algorithm is divided into land and ocean branches for smoke detection, and into bright surface and dark surface branches for dust detection. In the algorithm, the thermal bands are mainly used for filter out cloud, as well as a water vapor band, while the RSBs are selected for separating smoke/dust from other scene types. Some indices well developed in others previous studies are adopted directly. And some tests are proposed first time, including the pseudo-NDVI, the reflectance of blue band, and the normalized of two blue bands used for smoke detection; and the reflectance of red band used for dust detection. The spatial resolution of smoke/dust detection results 120
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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
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
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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-
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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
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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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