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地球卫星遥感（卷1理论与仪器）
作者：（美）曲
出版社：清华大学出版社
出版时间：2006-09-01
ISBN：9787302128441
定价：¥128.00
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内容简介
木门共有两卷。此为第1卷，包含20章，主要提供了有关中分辨串成像光谱仪（MODIS）产品的信息和科学应用，介绍了美国国家极轨环境卫系统（NPOESS）和NPOESS预备计划（NPP），还探讨厂其他卫星遥感装备和应用。有关数据格式、数据处理、数据查询和订购等方面的内容在第2卷中给出。本卷论及美国宁航局（NSA）用于监测和探测地球变化的主要卫星系统——地球观测系统（EOS），EOS包括的卫星Terra、Aqua和Aura及其装载的MODIS、AIRS、AMSU、AMSR-E、OMI等遥感仪器，并讨论厂NPP将携带的4个NPOESS系统重要部件：可见光红外成像辐射组件（VIIRS），航线交叉红外探测器（CrIS），先进技术微波探测器（ATMS）以及臭氧成图和廓线仪装置（OMPS），可用于研究地球气候环境和天气变化。《地球卫星遥感（卷1）：理论与仪器（国内版）》作者均为相关领域具有权威性的专家与学者。图书内容既包括现代遥感技术的基础知识，义涉及卫星遥感的前沿领域，有广泛的实用性，可作为遥感、地学、环境、空间信息等地球科学领域的专业参考书。
作者简介
Prof. John J. Qu is a faculty member of the ESGS department at the school of Computational Sciences and is Technical Director of EastFIRE Lab at George Mason University. He is also with NASA Goddard Space Flight Center to support the NPOESS Preparatory Project （NPP） mission. His major research areas are satellite remote sensing， Earth systems science， fire science and GIS applications.
目录
List of Contributors xv
1 Introduction to Science and Instruments 1
References9
2 Introduction to MODIS and an Overview of Associated Activities 12
2.1 Introduction 12
2.2 Background12
2.3 MODIS History 14
2.4 MODIS Sensor 15
2.5 MODIS Science Team and Data Products 19
2.6 MODIS Data Processing 24
2.7 Status and Follow-On Systems 28
2.7.1 Status 28
2.7.2 Follow-On Systems 29
References 31
MODIS Level-lB Products 33
3.1 Introduction 33
3.2 L1B Data Product Description 34
3.3 L1B Calibration Algorithm 38
3.3.1 Thermal Emissive Bands Algorithm 39
3.3.2 Reflective Solar Bands Algorithm 40
3.4 Code Standards and Properties 45
3.5 Data Processing 46
3.6 Data Product Retrieval 47
3.7 Summary 48
References 48
4 MODIS Geolocation 50
4.1 Introduction 50
4.2 Background 50
4.3 Approach 51
4.3.1 Instrument Geometry 52
4.3.2 Exterior and Interior Orientation 56
4.3.3 Algorithm 57
4.3.4 Error Sources 59
4.3.5 Ground Control Points 60
4.3.6 Geolocation Error Analysis and Reduction Methodology 61
4.4 Results 62
4.4.1 MODIS/Terra Results 62
4.4.2 MODIS/Aqua Results 68
4.5 Conclusion and the Future 70
Acknowledgements 71
References 71
5 Introduction to MODIS Cloud Products 74
5.1 Introduction 74
5.2 MODIS Instrument and Calibration 75
5.3 Level-2 Cloud Products 76
5.3.1 Cloud Masking 77
5.3.2 Cloud Thermodynamic Phase 77
5.3.3 Cloud Top Pressure and Effective Cloud Amount 78
5.3.4 Cloud Optical and Microphysical Properties 79
5.3.5 Cirrus Reflectance Algorithm 84
5.4 Global Gridded (Level-3) Products 84
5.5 Future Algorithm Efforts86
5.5.1 Detection of Multilayered Clouds 86
5.5.2 Improved Ice Cloud Microphysical and Optical Models 87
5.5.3 Improved Land Spectral Albedo Maps 88
5.5.4 Clear-Sky Radiance Maps 88
5.6 Summary 90
References 90
6 MODIS Observation of Aerosol Loading from 2000 to 2004 92
6.1 Introduction92
6.2 Multi-Year Aerosol Datasets93
6.3 MODIS Aerosol Retrieval Algorithm and Expected Accuracy 94
6.4 Characterization of Aerosol Optical Depth Distribution 96
6.5 Global and Hemispheric Analysis 99
6.6 Regional Analysis 101
6.7 Terra vs Aqua 104
6.8 Conclusions 107
References 107
7 MODIS Land Products and Data Processing 110
7.1 Introduction 110
7.2 Land Products and Characteristics 111
7.3 Data Production 114
7.3.1 Data Flows 115
7.3.2 Algorithm Improvements l17
7.3.3 Quality Assurance Approach 119
7.3.4 Validation Approach 119
7.4 Conclusion 120
Acknowledgements 120
References 121
8 Operational Atmospheric Correction of MODIS Visible to Middle Infrared Land Surface Data in the Case of an Infinite ambertian Target 123
8.1 Introduction 123
8.2 Theoretical Background 124
8.3 Operational Implementation 126
8.3.1 Simplification to Account for Surface Pressure 126
8.3.2 Detailed Computations 127
8.4 Input and Ancillary Data 129
8.4.1 Surface Pressure 130
8.4.2 Ozone 130
8.4.3 Water Vapor 131
8.4.4 Aerosol Optical Thickness 131
8.5 Application to MODIS Data and Error Budget 132
8.5.1 Calibration Uncertainties 135
8.5.2 Uncertainties on Ancillary Data Pressure 137
8.5.3 Uncertainties on Ancillary Ozone Amount 139
8.5.4 Uncertainties on the Water Vapor Amount 141
8.5.5 Uncertainties on Empirical Relationship used to Determine the Surface Reflectance at 470 nm and 645 nm 143
8.5.6 Uncertainties on the Aerosol Model 145
8.5.7 Overall Uncertainties 151
8.5.8 Validation of the Atmospheric Correction Algorithm 152
8.6 Conclusions 152
References 152
9 MODIS Snow and Sea Ice Products 154
9.1 Introduction 154
9.2 Snow Products 157
9.2.1 Introduction 157
9.2.2 MODIS Snow-Mapping Approaches 158
9.2.3 Snow Swath Product 160
9.2.4 Daily and 8-Day Composite Gridded Snow (Tile Products) 162
9.2.5 Daily and 8-Day Composite Global Climate-Modeling Grid Products 163
9.2.6 Monthly Snow Products 165
9.2.7 Validation 165
9.3 Sea Ice Products 168
9.3.1 Introduction and Algorithm Description 168
9.3.2 Calculation of Sea Ice-Surface Temperature 170
9.3.3 Swath Products171
9.3.4 Daily and 8-Day Composite Gridded Sea Ice Products (Tile Products) 171
9.3.5 Global-Scale Daily, 8-Day Composite and Monthly Gridded Products 171
9.3.6 Validation 172
9.4 Limitations Inherent in the Snow and Sea Ice Products 174
9.4.1 Land Masking in the Snow and Sea Ice Data Products 174
9.4.2 Cloud Masking 175
9.5 Discussion and Conclusion 176
Acknowledgements 177
References 177
10 The NPOESS Preparatory Project 182
10.1 Introduction 182
10.1.1 Origins of NPP 182
10.1.2 Program Philosophies 183
10.2 Sensor Payload--Providing Continuity and Evolution 184
10.2.1 VIIRS and Terra MODIS Continuity 184
10.2.2 VIIRS and Continuity of Operational Imagers 185
10.2.3 Aqua and Aura Continuity 186
10.2.4 CrlS andAIRS Continuity 186
10.2.5 CrlS and Continuity of Operational Sounders 186
10.2.6 ATMS and Continuity of Operational Sounders 187
10.2.7 OMPS and Continuity of Research Sounders 187
10.3 Spacecraft and Launch Vehicle 188
10.4 Orbit 189
10.5 Ground Segment 190
10.5.1 Data Downlink 190
10.5.2 IDPS 190
10.5.3 SDS 191
10.6 Measurement Requirements 192
10.6.1 lORD 192
10.6.2 NASA Science Requirement 192
10.6.3 Stratification 194
10.6.4 CDR's andEDR's 195
10.7 Science Guidance 197
10.8 Summary 197
References 198
11 The Visible Infrared Imaging Radiometer Suite 199
11.1 Introduction 199
11.1.1 Spectral Band Compliment 200
l 1.2 Design Philosophy 202
11.2.1 Spatial/Temporal Design Drivers 202
11.2.2 Spectral/Radiometric Design Drivers 205
11.3 Follow the Photons 208
11.3.1 Rotating Telescope Assembly 208
11.3.2 HalfAngle Mirror 210
11.3.3 Aft Optics 211
11.3.4 Focal Planes and Dewar 212
11.3.5 On-Board Calibrators 215
ll.4 Opto-Mechanical Systems 217
11.4.1 Structures 217
11.4.2 Cryoradiator 217
11.4.3 Thermal Control and Stray Light 218
11.5 Electronics 219
11.5.1 Signal Processing and Transmission 219
11.5.2 Power Supplies and Control Systems 221
11.5.3 Operational Modes 222
Acknowledgements 222
References 223
12 Conically Scanned Microwave Imager Sounder 224
12.1 Introduction 224
12.2 Instrument Overview 225
12.3 CMIS Risk Reduction Studies with Heritage Sensors,and Proxy Data 234
12.3.1 DMSP-SSMIS 237
12.3.2 Coriolis/WindSat 237
12.3.3 NOAA-15, 16 and 17 AMSU 238
12.4 Discussions 240
Acknowledgements 241
References 241
13 Advanced Technology Microwave Sounder 243
13.1 Introduction 243
13.2 Instrument Overview 243
13.3 ATMS Studies with a Heritage Sensor: AMSU 246
13.3.1 AMSU-A Temperature Profiles for Climate 247
13.3.2 AMSU-A Weather Application 248
13.4 Discussions 252
Acknowledgements 252
References 252
14 Introduction to AIRS and CrIS 254
14.1 Introduction and Overview 254
14.2 The Radiative Transfer Equation 257
14.3 Results usingAIRS/AMSU Data 262
14.4 Forecast Impact Experiments 269
14.5 Comparison ofCrlS andAIRS 273
14.6 Summary 277
References 278
15 The Ozone Mapping and Profiler Suite 279
15.1 Introduction 279
15.2 Nadir Sensors 280
15.3 Nadir Retrieval Algorithms 282
15.3.1 Total Column Ozone Algorithm 282
15.3.2 Nadir Profile Ozone Algorithm 284
15.4 Limb Profiler Sensor 285
15.5 Limb Profiler Ozone Algorithm 287
15.6 Limb Retrieval Challenges 292
Acknowledgements and Disclaimer 294
References 295
16 Estimating Solar UV-B Irradiance at the Earth's Surface Using Multi-Satellite Remote Sensing Measurements 297
16.1 Introduction 297
16.2 Satellite Remote Sensing Measurements 298
16.2.1 Satellite TOMS Ozone and Backscatter Ultraviolet Measurements 298
16.2.2 Shuttle Solar Backscatter Ultraviolet Measurements 299
16.2.3 Satellite Cloud Observations 300
16.2.4 Satellite Aerosol Observations 300
16.3 Ultraviolet Radiative Transfer Models 301
16.3.1 Scheme of UV-B Radiation Model 301
16.3.2 Two-Stream UV-B Radiation Transfer Models 303
16.4 Sensitivity Study 304
16.4.1 Sensitivity to Solar Zenith Angle 304
16.4.2 Sensitivity to Atmospheric Ozone 304
16.4.3 Sensitivity to Surface Reflectivity 305
16.4.4 Sensitivity to Cloud Optical Depth 306
16.4.5 Sensitivity to Atmospheric Aerosols 307
16.5 The Effects of Clouds and Aerosols on UV-B Irradiance 309
16.5.1 The Effects of Cloud on the Surface UV-B h-radiance 309
16.5.2 The Effects of Aerosol on the Surface UV-B Irradiances 310
16.5.3 Model Calibration 310
16.6 Summary and Conclusions 312
Acknowledgements 313
References 313
17 Surface Rain Rates from Tropical Rainfall Measuring Mission Satellite Algorithms 317
17.1 Introduction 317
17.2 Satellite Algorithms and Data 318
17.2.1 V5 Algorithms 319
17.2.2 V6 Algorithms 320
17.3 Results 322
17.3.l Annual Means and Paired t-Tests 322
17.3.2 Seasonal Differences 327
17.3.3 Interannual Variations 329
17.4 Summary and Discussion 332
Acknowledgements 334
References 334
18 Use of Satellite Remote Sensing Data for Modeling Carbon Emissions from Fires: A Perspective in North America 337
18.1 Introduction 337
18.2 Carbon Emission Estimation 338
18.3 Fire Emission Parameters and Modeling 339
18.3.1 Burned Area 339
18.3.2 Spatial Fragmentation and Temporal Expansion of Burned Area 344
18.3.3 Fuel Loading 346
18.3.4 Fuel Type 349
18.3.5 Fraction of Fuels Consumed 350
18.3.6 Emission Factor 353
18.3.7 Fuel Moisture Content 355
18.4 Summary 355
References 356
19 TRMM Fire Algorithm, Product and Applications 363
19.1 Introduction 363
19.1.1 Satellite Fire Products 363
19.1.2 Satellite Aerosol Product 365
19.2 TSDIS Fire Algorithms 366
19.2.1 Nighttime Algorithm 367
19.2.2 Daytime Algorithm 368
19.3 TSDIS Fire Products 370
19.4 Seasonal and Interannual Variability 373
19.4.1 Fire and Aerosol Comparison 373
19.4.2 Statistical EOF Analysis 377
19.5 Diurnal Cycle and Intraseasonal Variability 381
19.5.1 Diurnal CycleAliasing 382
19.5.2 Single Spectrum Analysis 384
19.6 Interaction between Fire and Rainfall 386
19.7 Summary 388
Acknowledgements 388
References 389
20 China's Current and Future Meteorological Satellite Systems 392
20.1 Introduction 392
20.2 The Polar Orbiting Meteorological Satellites of China 393
20.2.1 The First Generation of Polar Orbiting Operational Meteorological Satellites of China 393
20.2.2 The Second Generation of Polar Orbiting Operational Environmental Satellites of China: FY-3 Series 395
20.2.3 Payloads Onboard FY-3A 397
20.2.4 Complementary Mission 403
20.3 The First Generation Geostationary Meteorological Satellites of China 406
20.3.1 The FY-2A and FY-2B Satellites 406
20.3.2 The First Generation of Chinese Geostationary Operational Satellite: FY-2C Series 409
20.4 The Planning of the Second Generation Geostationary Meteorological Satellites of China: FY-4 412
20.5 Summary 413
References 413
Index 414
1 Introduction to Science and Instruments 1
References9
2 Introduction to MODIS and an Overview of Associated Activities 12
2.1 Introduction 12
2.2 Background12
2.3 MODIS History 14
2.4 MODIS Sensor 15
2.5 MODIS Science Team and Data Products 19
2.6 MODIS Data Processing 24
2.7 Status and Follow-On Systems 28
2.7.1 Status 28
2.7.2 Follow-On Systems 29
References 31
MODIS Level-lB Products 33
3.1 Introduction 33
3.2 L1B Data Product Description 34
3.3 L1B Calibration Algorithm 38
3.3.1 Thermal Emissive Bands Algorithm 39
3.3.2 Reflective Solar Bands Algorithm 40
3.4 Code Standards and Properties 45
3.5 Data Processing 46
3.6 Data Product Retrieval 47
3.7 Summary 48
References 48
4 MODIS Geolocation 50
4.1 Introduction 50
4.2 Background 50
4.3 Approach 51
4.3.1 Instrument Geometry 52
4.3.2 Exterior and Interior Orientation 56
4.3.3 Algorithm 57
4.3.4 Error Sources 59
4.3.5 Ground Control Points 60
4.3.6 Geolocation Error Analysis and Reduction Methodology 61
4.4 Results 62
4.4.1 MODIS/Terra Results 62
4.4.2 MODIS/Aqua Results 68
4.5 Conclusion and the Future 70
Acknowledgements 71
References 71
5 Introduction to MODIS Cloud Products 74
5.1 Introduction 74
5.2 MODIS Instrument and Calibration 75
5.3 Level-2 Cloud Products 76
5.3.1 Cloud Masking 77
5.3.2 Cloud Thermodynamic Phase 77
5.3.3 Cloud Top Pressure and Effective Cloud Amount 78
5.3.4 Cloud Optical and Microphysical Properties 79
5.3.5 Cirrus Reflectance Algorithm 84
5.4 Global Gridded (Level-3) Products 84
5.5 Future Algorithm Efforts86
5.5.1 Detection of Multilayered Clouds 86
5.5.2 Improved Ice Cloud Microphysical and Optical Models 87
5.5.3 Improved Land Spectral Albedo Maps 88
5.5.4 Clear-Sky Radiance Maps 88
5.6 Summary 90
References 90
6 MODIS Observation of Aerosol Loading from 2000 to 2004 92
6.1 Introduction92
6.2 Multi-Year Aerosol Datasets93
6.3 MODIS Aerosol Retrieval Algorithm and Expected Accuracy 94
6.4 Characterization of Aerosol Optical Depth Distribution 96
6.5 Global and Hemispheric Analysis 99
6.6 Regional Analysis 101
6.7 Terra vs Aqua 104
6.8 Conclusions 107
References 107
7 MODIS Land Products and Data Processing 110
7.1 Introduction 110
7.2 Land Products and Characteristics 111
7.3 Data Production 114
7.3.1 Data Flows 115
7.3.2 Algorithm Improvements l17
7.3.3 Quality Assurance Approach 119
7.3.4 Validation Approach 119
7.4 Conclusion 120
Acknowledgements 120
References 121
8 Operational Atmospheric Correction of MODIS Visible to Middle Infrared Land Surface Data in the Case of an Infinite ambertian Target 123
8.1 Introduction 123
8.2 Theoretical Background 124
8.3 Operational Implementation 126
8.3.1 Simplification to Account for Surface Pressure 126
8.3.2 Detailed Computations 127
8.4 Input and Ancillary Data 129
8.4.1 Surface Pressure 130
8.4.2 Ozone 130
8.4.3 Water Vapor 131
8.4.4 Aerosol Optical Thickness 131
8.5 Application to MODIS Data and Error Budget 132
8.5.1 Calibration Uncertainties 135
8.5.2 Uncertainties on Ancillary Data Pressure 137
8.5.3 Uncertainties on Ancillary Ozone Amount 139
8.5.4 Uncertainties on the Water Vapor Amount 141
8.5.5 Uncertainties on Empirical Relationship used to Determine the Surface Reflectance at 470 nm and 645 nm 143
8.5.6 Uncertainties on the Aerosol Model 145
8.5.7 Overall Uncertainties 151
8.5.8 Validation of the Atmospheric Correction Algorithm 152
8.6 Conclusions 152
References 152
9 MODIS Snow and Sea Ice Products 154
9.1 Introduction 154
9.2 Snow Products 157
9.2.1 Introduction 157
9.2.2 MODIS Snow-Mapping Approaches 158
9.2.3 Snow Swath Product 160
9.2.4 Daily and 8-Day Composite Gridded Snow (Tile Products) 162
9.2.5 Daily and 8-Day Composite Global Climate-Modeling Grid Products 163
9.2.6 Monthly Snow Products 165
9.2.7 Validation 165
9.3 Sea Ice Products 168
9.3.1 Introduction and Algorithm Description 168
9.3.2 Calculation of Sea Ice-Surface Temperature 170
9.3.3 Swath Products171
9.3.4 Daily and 8-Day Composite Gridded Sea Ice Products (Tile Products) 171
9.3.5 Global-Scale Daily, 8-Day Composite and Monthly Gridded Products 171
9.3.6 Validation 172
9.4 Limitations Inherent in the Snow and Sea Ice Products 174
9.4.1 Land Masking in the Snow and Sea Ice Data Products 174
9.4.2 Cloud Masking 175
9.5 Discussion and Conclusion 176
Acknowledgements 177
References 177
10 The NPOESS Preparatory Project 182
10.1 Introduction 182
10.1.1 Origins of NPP 182
10.1.2 Program Philosophies 183
10.2 Sensor Payload--Providing Continuity and Evolution 184
10.2.1 VIIRS and Terra MODIS Continuity 184
10.2.2 VIIRS and Continuity of Operational Imagers 185
10.2.3 Aqua and Aura Continuity 186
10.2.4 CrlS andAIRS Continuity 186
10.2.5 CrlS and Continuity of Operational Sounders 186
10.2.6 ATMS and Continuity of Operational Sounders 187
10.2.7 OMPS and Continuity of Research Sounders 187
10.3 Spacecraft and Launch Vehicle 188
10.4 Orbit 189
10.5 Ground Segment 190
10.5.1 Data Downlink 190
10.5.2 IDPS 190
10.5.3 SDS 191
10.6 Measurement Requirements 192
10.6.1 lORD 192
10.6.2 NASA Science Requirement 192
10.6.3 Stratification 194
10.6.4 CDR's andEDR's 195
10.7 Science Guidance 197
10.8 Summary 197
References 198
11 The Visible Infrared Imaging Radiometer Suite 199
11.1 Introduction 199
11.1.1 Spectral Band Compliment 200
l 1.2 Design Philosophy 202
11.2.1 Spatial/Temporal Design Drivers 202
11.2.2 Spectral/Radiometric Design Drivers 205
11.3 Follow the Photons 208
11.3.1 Rotating Telescope Assembly 208
11.3.2 HalfAngle Mirror 210
11.3.3 Aft Optics 211
11.3.4 Focal Planes and Dewar 212
11.3.5 On-Board Calibrators 215
ll.4 Opto-Mechanical Systems 217
11.4.1 Structures 217
11.4.2 Cryoradiator 217
11.4.3 Thermal Control and Stray Light 218
11.5 Electronics 219
11.5.1 Signal Processing and Transmission 219
11.5.2 Power Supplies and Control Systems 221
11.5.3 Operational Modes 222
Acknowledgements 222
References 223
12 Conically Scanned Microwave Imager Sounder 224
12.1 Introduction 224
12.2 Instrument Overview 225
12.3 CMIS Risk Reduction Studies with Heritage Sensors,and Proxy Data 234
12.3.1 DMSP-SSMIS 237
12.3.2 Coriolis/WindSat 237
12.3.3 NOAA-15, 16 and 17 AMSU 238
12.4 Discussions 240
Acknowledgements 241
References 241
13 Advanced Technology Microwave Sounder 243
13.1 Introduction 243
13.2 Instrument Overview 243
13.3 ATMS Studies with a Heritage Sensor: AMSU 246
13.3.1 AMSU-A Temperature Profiles for Climate 247
13.3.2 AMSU-A Weather Application 248
13.4 Discussions 252
Acknowledgements 252
References 252
14 Introduction to AIRS and CrIS 254
14.1 Introduction and Overview 254
14.2 The Radiative Transfer Equation 257
14.3 Results usingAIRS/AMSU Data 262
14.4 Forecast Impact Experiments 269
14.5 Comparison ofCrlS andAIRS 273
14.6 Summary 277
References 278
15 The Ozone Mapping and Profiler Suite 279
15.1 Introduction 279
15.2 Nadir Sensors 280
15.3 Nadir Retrieval Algorithms 282
15.3.1 Total Column Ozone Algorithm 282
15.3.2 Nadir Profile Ozone Algorithm 284
15.4 Limb Profiler Sensor 285
15.5 Limb Profiler Ozone Algorithm 287
15.6 Limb Retrieval Challenges 292
Acknowledgements and Disclaimer 294
References 295
16 Estimating Solar UV-B Irradiance at the Earth's Surface Using Multi-Satellite Remote Sensing Measurements 297
16.1 Introduction 297
16.2 Satellite Remote Sensing Measurements 298
16.2.1 Satellite TOMS Ozone and Backscatter Ultraviolet Measurements 298
16.2.2 Shuttle Solar Backscatter Ultraviolet Measurements 299
16.2.3 Satellite Cloud Observations 300
16.2.4 Satellite Aerosol Observations 300
16.3 Ultraviolet Radiative Transfer Models 301
16.3.1 Scheme of UV-B Radiation Model 301
16.3.2 Two-Stream UV-B Radiation Transfer Models 303
16.4 Sensitivity Study 304
16.4.1 Sensitivity to Solar Zenith Angle 304
16.4.2 Sensitivity to Atmospheric Ozone 304
16.4.3 Sensitivity to Surface Reflectivity 305
16.4.4 Sensitivity to Cloud Optical Depth 306
16.4.5 Sensitivity to Atmospheric Aerosols 307
16.5 The Effects of Clouds and Aerosols on UV-B Irradiance 309
16.5.1 The Effects of Cloud on the Surface UV-B h-radiance 309
16.5.2 The Effects of Aerosol on the Surface UV-B Irradiances 310
16.5.3 Model Calibration 310
16.6 Summary and Conclusions 312
Acknowledgements 313
References 313
17 Surface Rain Rates from Tropical Rainfall Measuring Mission Satellite Algorithms 317
17.1 Introduction 317
17.2 Satellite Algorithms and Data 318
17.2.1 V5 Algorithms 319
17.2.2 V6 Algorithms 320
17.3 Results 322
17.3.l Annual Means and Paired t-Tests 322
17.3.2 Seasonal Differences 327
17.3.3 Interannual Variations 329
17.4 Summary and Discussion 332
Acknowledgements 334
References 334
18 Use of Satellite Remote Sensing Data for Modeling Carbon Emissions from Fires: A Perspective in North America 337
18.1 Introduction 337
18.2 Carbon Emission Estimation 338
18.3 Fire Emission Parameters and Modeling 339
18.3.1 Burned Area 339
18.3.2 Spatial Fragmentation and Temporal Expansion of Burned Area 344
18.3.3 Fuel Loading 346
18.3.4 Fuel Type 349
18.3.5 Fraction of Fuels Consumed 350
18.3.6 Emission Factor 353
18.3.7 Fuel Moisture Content 355
18.4 Summary 355
References 356
19 TRMM Fire Algorithm, Product and Applications 363
19.1 Introduction 363
19.1.1 Satellite Fire Products 363
19.1.2 Satellite Aerosol Product 365
19.2 TSDIS Fire Algorithms 366
19.2.1 Nighttime Algorithm 367
19.2.2 Daytime Algorithm 368
19.3 TSDIS Fire Products 370
19.4 Seasonal and Interannual Variability 373
19.4.1 Fire and Aerosol Comparison 373
19.4.2 Statistical EOF Analysis 377
19.5 Diurnal Cycle and Intraseasonal Variability 381
19.5.1 Diurnal CycleAliasing 382
19.5.2 Single Spectrum Analysis 384
19.6 Interaction between Fire and Rainfall 386
19.7 Summary 388
Acknowledgements 388
References 389
20 China's Current and Future Meteorological Satellite Systems 392
20.1 Introduction 392
20.2 The Polar Orbiting Meteorological Satellites of China 393
20.2.1 The First Generation of Polar Orbiting Operational Meteorological Satellites of China 393
20.2.2 The Second Generation of Polar Orbiting Operational Environmental Satellites of China: FY-3 Series 395
20.2.3 Payloads Onboard FY-3A 397
20.2.4 Complementary Mission 403
20.3 The First Generation Geostationary Meteorological Satellites of China 406
20.3.1 The FY-2A and FY-2B Satellites 406
20.3.2 The First Generation of Chinese Geostationary Operational Satellite: FY-2C Series 409
20.4 The Planning of the Second Generation Geostationary Meteorological Satellites of China: FY-4 412
20.5 Summary 413
References 413
Index 414
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