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

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2.3 Chapter Summary This chapter reviewed the approaches for detecting smoke and dust aerosols in past and present. Although visual identification of aerosol can provide quick identification, this technique only gives very limit information about smoke and dust aerosols. And this approach cannot offer automated detection of smoke and dust aerosols, which is not suitable for processing large amount of remote sensing measurements timely. Although current MODIS operational product provides aerosol classification based on dark target approach and deep blue approach, it misclassified most smoke/dust events near or mixed with cloud since it adopted cloud mask product directly which has strict standard to filter out all suspicious pixels. Moreover, the 10 km 2 spatial resolution is also relatively coarse. The physical principle of multi-spectral method is to find the spectral feature difference between several major kinds of scene types in VIS/NIR/IR even in microwave spectra. As discussed above, most multi-spectral approaches use the combination of the bands either in the solar spectrum or thermal spectrum. Similarly, most approaches can detect smoke or dust only at those areas away from cloud. Based on the review of the physical bases, advantages, and limitations of above approaches, the multi-spectral approach is selected for smoke and dust aerosol detection in this dissertation by combining measurements of both MODIS RSBs and TEBs. The major goal of this algorithm is to detect smoke and dust aerosols over both dark and bright surfaces. Moreover, the algorithm is aim to avoid the misclassification over the areas close to or mixed with cloud. 15
CHAPTER 3 SMOKE AEROSOL DETECTION WITH MODIS MEASUREMENTS Smoke aerosol from wildfires is a mixture of gases, organic compounds and particles, including carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and so on (Austin and Goyer, 2007). The mixing level of these components varies with the types of burning wood and vegetation so that smoke aerosol has no stable spectral characteristic. Therefore, a multi-spectral method combining both MODIS RSBs and TEBs is developed in this chapter (Xie et al., 2007). The smoke is identified by filtering out other scene types step by step, according to their reflectance differences in solar spectrum and their BTD in thermal spectrum. The bands are selected and thresholds are decided on the basis of spectral and statistical analyses, as well as basic radiative transfer model. The algorithm works well on detection of smoke plumes occurred in the United States. The validation of results with MODIS true color images is also presented in this chapter. 16
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 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: more information about dust charact
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 77 and 78: operated in the A-train orbit with
Page 79 and 80:
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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