Land Remote Sensing and Global Environmental Change (eBook)

Bhaskar Ramachandran is a senior scientist who supports the NASA Earth Observing System (EOS) science mission at the Earth Resources and Observation Science (EROS) Center at the U.S. Geological Survey in Sioux Falls, South Dakota. He currently supports the MODIS science mission, and has performed similar roles for the Landsat-7 and ASTER missions in the past. His current research interests include the use of semantic web capabilities, and building ontologies to represent geospatial science knowledge domains. Chris Justice is a Professor and Research Director at the Geography Department of the University of Maryland. He is the land discipline chair for the NASA MODIS Science Team and is responsible for the MODIS Fire Product. He is a member of the NASA NPOESS Preparatory Project (NPP) Science Team. He is the NASA Land Cover Land Use Change Program Scientist. His current research is on land cover and land use change, the extent and impacts of global fire, global agricultural monitoring, and their associated information technology and decision support systems. Michael Abrams received his degrees in Biology and Geology from the California Institute of Technology. Since 1973 he has worked at NASA's Jet Propulsion Laboratory in geologic remote sensing. He served on the science team for many instruments, including Skylab, HCMM, Landsat, and EO-1. Areas of specialization are mineral exploration, natural hazards, volcanology, and instrument validation. He has been on the US/Japan ASTER Science Team since 1988, and became the ASTER Science Team Leader in 2003.

Part I: The Earth Observing System and the Evolution of ASTER and MODIS 43
Chapter 1: Evolution of NASA’s Earth Observing System and Development of the Moderate-Resolution Imaging Spectroradiometer and 45
1.1 Introduction 45
1.2 Evolution of NASA’s Earth Observing System 45
1.3 Development, Characterization, and Performance of the Earth Observing System Moderate Resolution Imaging Spectroradiomet 51
1.3.1 Background 51
1.3.2 Sensor Concepts 54
1.3.3 Performance 56
1.3.4 The Post-MODIS Future 62
1.4 History of the Advanced Spaceborne Thermal Emission and Reflection Radiometer 64
References 74
Chapter 2: Philosophy and Architecture of the EOS Data and Information System 77
2.1 Introduction 77
2.2 Inception and Early History 77
2.3 Early Architecture 80
2.4 An EOSDIS to Support the EOS Missions 81
2.5 Science Investigator-Led Processing Systems 83
2.6 Data Policy 84
2.7 EOSDIS and Participating Communities 85
2.8 Metrics 87
2.9 Lessons Learned and Best Practices Today 87
2.10 Next Generation Challenges 89
Reference 89
Chapter 3: Lessons Learned from the EOSDIS Engineering Experience 90
3.1 EOSDIS Core System Background 90
3.1.1 Incremental Method of Requirements Elaboration Proved Effective 90
3.1.2 Heavy Dependence on COTS Technology Increases the System’s Complexity in the Long Run 91
3.1.3 Incremental Releases Are a Much Better Way to Deploy Capability than Big-Bang Releases 92
3.1.4 Mode Management Approach to Software Development and Testing 93
3.1.5 Development of DAAC-Unique Extensions 93
3.1.6 Data Migration in Multi-petabyte ECS Archives Is a Continuous Operational Function 94
3.1.7 Silent Data Corruption Will Occur if Data Volumes Are Large Enough 94
3.1.8 Providing Access to Online Data 95
References 95
Part II: ASTER and MODIS: Instrument Design, Radiometry, and Geometry 96
Chapter 4: Terra ASTER Instrument Design and Geometry 99
4.1 Overview 99
4.2 Baseline Performance 99
4.3 System Layout 101
4.4 System Components 101
4.5 Spectral Performance 102
4.6 Radiometric Performance 105
4.7 Geometric Performance 111
4.8 Modulation Transfer Function 114
4.9 Level-1A Data Product 115
4.10 Level-1B Data Product 117
References 122
Chapter 5: ASTER VNIR and SWIR Radiometric Calibration and Atmospheric Correction 123
5.1 Introduction 123
5.2 Conversion to At-Sensor, Spectral Radiance 124
5.2.1 Unit Conversion Coefficients 124
5.3 Determination of Radiometric Calibration Coefficients 125
5.3.1 Preflight Determination of Radiometric Calibration Coefficients 126
5.3.2 Onboard Calibration 126
5.3.3 Vicarious Calibration 128
5.3.3.1 Reflectance-Based Approach 129
5.3.4 Cross Calibration 133
5.3.5 OBC RCC Trends and Comparison to the Vicarious Calibration 133
5.3.5.1 RCC Trends 133
5.3.5.2 Comparison to the Vicarious Calibration 136
5.3.5.3 Radiometric Calibration Coefficients for VNIR and SWIR 139
5.4 Other Radiometric Performance Issues 140
5.4.1 Offset 141
5.4.2 Noise Equivalent Reflectance and Temperature 142
5.4.3 Modulation Transfer Function 142
5.4.4 SWIR Crosstalk 144
5.5 Atmospheric Correction 150
5.5.1 Method Description 150
5.5.2 LUT Resolution 151
5.5.3 Uncertainty Estimates 153
References 154
Chapter 6: ASTER TIR Radiometric Calibration and Atmospheric Correction 157
6.1 ASTER TIR Radiometry 157
6.1.1 Onboard Calibration 157
6.1.2 Responsivity Trend 158
6.1.3 Radiometric Calibration for Products 159
6.1.4 Vicarious Calibration 162
6.1.5 Stray Light 163
6.1.6 Other Radiometric Performances 164
6.2 ASTER TIR Atmospheric Correction 165
6.2.1 Theoretical Basis 165
6.2.2 Standard Atmospheric Correction for ASTER/TIR 166
6.2.2.1 Algorithm Overview 166
6.2.2.2 Implementation 166
6.2.2.3 Input Parameters to MODTRAN 167
6.2.2.4 Validation 168
6.2.3 Alternative Atmospheric Correction: Water Vapor Scaling Method 168
6.2.3.1 Algorithm Overview 168
6.2.3.2 Validation 170
References 170
Chapter 7: Terra and Aqua MODIS Design, Radiometry, and Geometry in Support of Land Remote Sensing 173
7.1 Introduction 173
7.2 MODIS Sensor Design 176
7.2.1 MODIS Optics 176
7.2.2 Focal Plane Assemblies 177
7.2.3 Onboard Calibrators 178
7.3 Radiometric Calibration 181
7.3.1 Reflective Solar Band Calibration 181
7.3.2 Thermal Emissive Band Calibration 183
7.3.3 On-Orbit Performance 185
7.4 MODIS Geometry 192
7.5 Cross-Calibration of Terra and Aqua MODIS 196
7.6 Summary 200
References 202
Part III: ASTER and MODIS: Data Systems 205
Chapter 8: ASTER and MODIS Land Data Management at the Land Processes, and National Snow and Ice Data Centers 207
8.1 Introduction 207
8.2 ASTER Data Management at LP DAAC 209
8.2.1 History of ASTER Data Management 209
8.2.2 ASTER Data Archival, Production, and Distribution Statistics 211
8.2.3 Contemporary ASTER Data Management 211
8.3 MODIS Land Data Management at LP DAAC 213
8.3.1 MODIS Land Data Archival and Distribution Statistics 216
8.4 MODIS Snow and Sea Ice Data Management at NSIDC DAAC 216
8.4.1 MODIS Snow and Sea Ice Data Archival and Distribution Statistics 220
8.4.2 MODIS Metadata Management and Quality Assurance Updates at Both Data Centers 220
8.4.3 ECS Evolution-Related Changes at Both Data Centers 221
8.5 Closing Thoughts 221
References 222
Chapter 9: An Overview of the EOS Data Distribution Systems 223
9.1 Introduction 223
9.2 History and Evolution 226
9.2.1 Version-0 Information Management Subsystem 226
9.2.2 Overview of EDG and WIST 228
9.3 Key User Interfaces for EOS MODIS and ASTER Data Products 228
9.3.1 EDG and the WIST 228
9.3.1.1 Searching for Data 229
9.3.1.2 Navigating Search Results 230
9.3.1.3 Browse Images 231
9.3.1.4 Data Access 231
9.3.1.5 Ordering 231
9.3.1.6 Subsetting 231
9.3.2 Data Pools 232
9.3.2.1 LP DAAC’s Data Pool 233
9.3.2.2 NSIDC DAAC’s Data Pool 233
9.3.3 EOS Clearinghouse 234
9.3.4 Global Change Master Directory 234
9.3.5 Interfaces Specializing in Land Processes Data 235
9.3.5.1 United States Geological Survey: Global Visualization Viewer 235
9.3.5.2 Oak Ridge National Laboratory’s Mercury System 235
9.3.5.3 MODIS Search ‘N Order Web Interface 236
9.4 Future 236
9.5 Appendix: Acronyms 241
References 242
Chapter 10: The Language of EOS Data: Hierarchical Data Format 243
10.1 Introduction 243
10.2 Brief History and Evolution of HDF 244
10.2.1 Early History of Scientific Data Formats 244
10.2.2 Origins of HDF 245
10.2.2.1 Features of HDF 245
10.2.2.2 The HDF Data Model 245
10.2.3 Why NASA Chose HDF for EOS What Other SDFs Were Considered?
10.2.4 Pros and Cons of HDF (With Respect to Earth Science Data) 247
10.3 Overview of the HDF Data Model 247
10.3.1 HDF4 247
10.3.2 HDF5 248
10.3.3 HDF-EOS 248
10.3.3.1 Swath Data Model 249
10.3.3.2 GRID Data Model 251
10.3.4 The EOS Metadata Standard 253
10.3.4.1 Metadata Data Model 253
10.3.4.2 Attribute Storage in HDF-EOS Files 254
10.4 How MODIS Uses HDF 255
10.4.1 MODIS 255
10.4.2 MODIS L1B as an Example of Hybrid HDF and HDF-EOS 255
10.4.3 HDF Objects 256
10.4.4 Higher-Level Products 257
10.4.5 MODIS Metadata 258
10.4.5.1 Collection Description 258
10.4.5.2 Spatial Domain Container 259
10.4.5.3 Range Date Time 259
10.4.5.4 Orbital Spatial Domain Container 261
10.4.5.5 Additional Attributes 261
10.5 How ASTER Uses HDF-EOS 261
10.5.1 ASTER 261
10.5.1.1 ASTER Level-1A Data 262
10.5.1.2 ASTER Level-1B Data 262
10.5.1.3 ASTER Level-2 and Level-3 Products 262
10.5.2 ASTER Metadata 263
10.6 Software Support for HDF and HDF-EOS 263
References 267
Part IV: ASTER Science and Applications 268
Chapter 11: The ASTER Data System: An Overview of the Data Products in Japan and in the United States 271
11.1 Introduction 272
11.2 ASTER Data Flow Overview 273
11.3 The Role of ASTER GDS 274
11.3.1 Mission Operations 274
11.3.2 Data Processing 275
11.4 The Role of the Land Processes DAAC 277
11.5 ASTER Standard Data Products 278
11.6 Access to ASTER Data and Products 279
11.6.1 Aster Gds 280
11.6.2 LP DAAC 281
11.7 Conclusions 282
References 282
Chapter 12: ASTER Applications in Volcanology 283
12.1 Introduction 283
12.2 Surface Temperature Mapping 283
12.3 Volcano Observations with ASTER 285
12.3.1 Sulfur Dioxide Flux Estimation at Miyakejima Volcano, Japan 285
12.3.1.1 Introduction 285
12.3.1.2 The SO2 Flux Estimation Using a Thermal Infrared Multispectral Scanner 285
12.3.2 Thermal Monitoring of the 2006 Merapi Volcano Eruption, Indonesia 287
12.3.2.1 Introduction 287
12.3.2.2 Volcanic Activity of Merapi Volcano 287
12.3.2.3 Analysis of Daytime ASTER Images of the Merapi 2006 Eruption 288
12.3.2.4 Analysis of Nighttime ASTER Images of the Merapi 2006 Eruption 288
12.3.3 The 2005 Sierra Negra Eruption, Galapagos Islands 289
12.3.3.1 Introduction 289
12.3.3.2 ASTER Image Analyses 291
12.3.4 Discolored Seawater Observation at Satsuma-Iwojima, Japan 291
12.3.4.1 Introduction 291
12.3.4.2 Geologic Setting of Satsuma-Iwojima 293
12.3.4.3 Satellite Image Analyses 294
12.3.5 The 2005 Fukutoku-Okanoba Submarine Volcano Eruption, Japan 295
12.3.5.1 Introduction 295
12.3.5.2 Discolored Seawater and Floating Objects Analysis with ASTER VNIR 296
12.3.6 The 2006 Home Reef Submarine Volcano Eruption in Tonga 298
12.3.6.1 Introduction 298
12.3.6.2 ASTER Images of Home Reef 299
12.3.7 Detection of Flank and Summit Thermal Anomalies in Advance of the Chikurachki Volcano’s 2003 Eruption 300
12.3.7.1 Introduction 300
12.3.7.2 Analysis 301
12.4 Global Volcano Observation Plan, the ASTER Image Database for Volcanoes, and the ASTER Volcano Archive 304
12.4.1 Introduction 304
12.4.2 The Global Volcano Observation Plan with ASTER 305
12.4.3 ASTER Image Database for Volcanoes 306
12.4.4 The ASTER Volcano Archive 306
12.5 Conclusions 308
References 309
Chapter 13: Issues Affecting Geological Mapping with ASTER Data: A Case Study of the Mt Fitton Area, South Australia 311
13.1 Introduction 311
13.1.1 Sensor Resolution Issues 311
13.1.2 Atmospheric Effects and SWIR Crosstalk Issues 314
13.2 Mt Fitton Test Site, South Australia 315
13.3 Previous Remote Sensing Studies at Mt Fitton 316
13.4 ASTER Pre-processing and Mineral Map Generation 319
13.4.1 ASTER Data Levels and Products 319
13.4.2 ASTER SWIR Crosstalk Correction 322
13.4.3 Geological and Mineral Information Extraction Techniques for Multitemporal Mapping 322
13.5 Results 324
13.5.1 AlOH, MgOH/Carbonate, and Ferrous Iron Mapping Using ASTER SWIR Radiance-at-Sensor Data 324
13.5.2 Seasonal and Pre-processing Effects on ASTER SWIR Mapping Results 325
13.5.3 The Significance of Topographic Illumination Effects on ASTER SWIR Results 330
13.5.4 Estimation and Correction of ASTER SWIR Radiance Offsets 332
13.5.5 Geological Mapping Results with ASTER Thermal Infrared Data 335
13.6 Conclusions 336
References 337
Chapter 14: ASTER Data Use in Mining Applications 339
14.1 Introduction 339
14.2 Regional Reconnaissance and Mineral Assessment 341
14.2.1 High Zagros and Jebal Barez Mountains, Zagros Magmatic Arc, Iran 341
14.3 District-Scale Alteration Mapping 347
14.3.1 Chimborazo-Zaldivar Mining District, Northern Chile 347
14.4 Localized Fieldwork and Logistics 354
14.4.1 Oyu Tolgoi Mining District, Mongolia 354
14.5 Summary and Conclusions 359
References 360
Chapter 15: ASTER Imaging and Analysis of Glacier Hazards 362
15.1 Introduction 362
15.2 Why Are Glaciers Dangerous, and How Can Satellite Imaging Assist? 364
15.3 Types and Case Examples of Glacier Hazards and Disasters 366
15.4 Satellite Vision: Capabilities and Limitations 373
15.4.1 Palcacocha: What Is Visible from the Ground and Space? 373
15.4.2 The 2003 Palcacocha Crisis 384
15.5 Alpine Glacier Hazards Evolution in this Century of Global Warming 385
15.5.1 List of Causes of Evolution of Glacier Hazards 387
15.6 A Future Technological Approach to Hazards Detection and Predictive Modeling 389
15.6.1 Fuzzy Logic for the Autonomous Assessment of Glacier Hazards 393
15.6.2 Fuzzy Expert Systems for Glacier-Induced Hazards Assessment 395
15.7 Toward a System and Protocol for Glacier Hazards Research and Communications 402
References 406
Chapter 16: ASTER Application in Urban Heat Balance Analysis: A Case Study of Nagoya 411
16.1 Introduction 411
16.2 Study Area and Data Used 412
16.2.1 Description of the Study Area 412
16.2.2 Satellite Data and Preprocessing 413
16.2.3 Meteorological Data and Preprocessing 415
16.3 Methodology of Heat Flux Calculation 417
16.4 Results and Discussion 421
16.4.1 Spatial Pattern and Temporal Variation of Artificial Increase in Sensible Heat Flux 423
16.4.2 Assessment of the Accuracy of Has Estimation 424
16.4.3 Comparison of Seasonal and Temporal Variations in Has at Specific Sites 425
16.4.4 Contributions of Has and Hn as Causes of the Heat-Island Effect 426
16.4.5 Sensitivity Study of Has Calculation 426
16.4.6 Comparison of Heat Flux Ratio with in Situ Observations 427
16.5 Summary 429
References 430
Chapter 17: Monitoring Urban Change with ASTER Data 432
17.1 Introduction 432
17.1.1 Scale and Resolution 433
17.1.2 Spectral and Radiometric Properties for Urban Monitoring 435
17.1.3 Height Extraction 438
17.1.4 ASTER Science Team Acquisition Request (City STAR) for Urban Areas and Urban Environmental Monitoring Project at Arizo 438
17.2 Technical Specification and Applications for Urban Analysis 440
17.2.1 Urban Vegetation and Open Space Detection Versus Developed and Impervious Surfaces 440
17.2.2 Urban Landscape Structure 441
17.2.3 Thermal Analysis and Pattern 443
17.3 Monitoring Urban Areas: Latest Urban Environmental Monitoring Project Research Endeavors 446
17.3.1 Object-Oriented Land Use/Land Cover Classification: Phoenix Versus Las Vegas 447
17.3.2 Urban Land Cover Mapping Using ASTER: A Concept for Designing Practical Classification Schemes for “100 Cities” 448
17.4 Conclusions and Outlook 450
References 451
Chapter 18: Estimation of Methane Emission from West Siberian Lowland with Subpixel Land Cover Characterization Between MODIS a 455
18.1 Introduction 456
18.2 Methodology 457
18.2.1 Outline of this Research 457
18.2.2 Study Area 457
18.2.3 CH4 Flux Measurement 459
18.2.4 ASTER and MODIS Data Sets Used in the Study 460
18.2.5 Spectral Mixture Analysis 463
18.3 Results and Discussions 464
18.3.1 Spectral Mixture Analysis 464
18.3.2 Accuracy Assessment 466
18.3.3 Estimation of CH4 Emission 468
18.4 Discussions 469
18.5 Concluding Remarks 470
References 470
Chapter 19: ASTER Stereoscopic Data and Digital Elevation Models* 472
19.1 Introduction 472
19.2 ASTER, Stereoscopy, and DEMs 473
19.2.1 Basic Aspects of ASTER Stereoscopic Data 473
19.2.2 Basic Aspects of DEM Stereoscopy1 476
19.3 ASTER DEM Production at the LP DAAC 479
19.3.1 DEM Generation Algorithms 480
19.3.2 DEM Products and Validation 483
19.4 ASTER DEM Production at ERSDAC, Japan 485
19.4.1 DEM Generation Algorithms 486
19.4.2 Products Description and Validation 488
19.5 Concluding Remarks 492
References 492
Chapter 20: Using ASTER Stereo Images to Quantify Surface Roughness 495
20.1 Introduction 496
20.2 Approach 497
20.2.1 Relative SR Estimates 497
20.2.2 Calibration 499
20.2.3 ASTER Stereo Data 500
20.3 Methods 501
20.3.1 Field Work 501
20.3.2 Reflection Model 502
20.3.3 Atmospheric Corrections 502
20.4 Results 502
20.5 Discussion 508
20.6 Summary and Conclusion 510
Acknowledgments 512
References 512
Chapter 21: Technoscientific Diplomacy: The Practice of International Politics in the ASTER Collaboration 514
21.1 Introduction 514
21.2 Trying to Share Separately 516
21.3 Enacting Japan–U.S. Partnership 527
21.4 Conclusion 530
References 532
Part V: MODIS Science and Applications 535
Chapter 22: MODIS Land Data Products: Generation, Quality Assurance and Validation 538
22.1 Introduction 538
22.2 Land Products 539
22.3 MODIS Data Production 540
22.3.1 Data Flows 542
22.3.2 The MODIS Adaptive Processing System 544
22.3.3 Software Integration and Testing 546
22.3.4 Algorithm Improvements 546
22.4 MODIS Reprocessing: Collections 547
22.5 Quality Assessment 549
22.5.1 Rationale for Quality Assessment 549
22.5.2 MODIS Land Quality Assessment Roles 550
22.5.3 Product Quality Documentation 550
22.5.4 LDOPE Web Site 552
22.5.4.1 Known Issues 552
22.5.4.2 Global Browse 553
22.5.4.3 Metadata Database 553
22.5.4.4 Time-Series Analysis 554
22.6 Validation Approach 555
22.7 Conclusion 558
References 558
Chapter 23: MODIS Directional Surface Reflectance Product: Method, Error Estimates and Validation 561
23.1 Introduction 561
23.2 Theoretical Basis 562
23.3 MODIS AC Input Parameters 564
23.4 Radiative Transfer Modeling 564
23.5 Aerosol Inversion 565
23.6 Error Budget 569
23.7 Collection-5 MOD09 571
23.8 Performance of the MODIS C5 Algorithms 571
23.9 Future Plans 574
References 574
Chapter 24: Aqua and Terra MODIS Albedo and Reflectance Anisotropy Products 576
24.1 Introduction 576
24.2 MODIS Albedo and Reflectance Anisotropy Algorithm 577
24.3 Algorithm Quality 581
24.4 Summary 584
References 584
Chapter 25: MODIS Land Surface Temperature and Emissivity 589
25.1 Introduction 589
25.2 MODIS LST Algorithms and Their Implementation in the LST PGEs 593
25.3 Test Results of the V5 PGE16 Code 595
25.4 Validation and Uncertainty Analysis 599
25.5 Conclusions 601
References 602
Chapter 26: MODIS Vegetation Indices 604
26.1 Introduction 604
26.2 Definition and Theoretical Basis 605
26.3 Algorithm State and Heritage 606
26.3.1 Compositing Approach 608
26.3.2 Dynamic Range of the VI Products 609
26.4 Validation and Accuracy of the VI Product Suite 610
26.4.1 Angular Sources of Uncertainty 611
26.4.2 Atmosphere and Clouds 611
26.4.3 Biophysical Validation 612
26.5 Science and Applications 616
26.5.1 Carbon and Water Science 616
26.5.2 Phenology Studies 618
26.5.3 Societal Applications 619
26.6 Vegetation Index Continuity and Long-Term Data Records 620
26.7 Conclusions 622
References 623
Chapter 27: Leaf Area Index and Fraction of Absorbed PAR Products from Terra and Aqua MODIS Sensors: Analysis, Validation, and 628
27.1 Introduction 628
27.2 MODIS LAI/FPAR Algorithm and Products 629
27.3 Analysis of Collection 4 Terra LAI/FPAR Global Time-Series 630
27.3.1 The Collection 4 Algorithm 631
27.3.2 Data 633
27.3.3 Analysis 633
27.4 Validation of MODIS Terra LAI/FPAR Products 638
27.4.1 Validation Methodology 638
27.4.2 Validation Results 640
27.4.3 Sources of Retrieval Uncertainties 643
27.5 Generation of Improved Quality LAI/FPAR Products from Combination of Terra and Aqua Data 648
27.5.1 Collection 5 Algorithm Refinements 648
27.5.2 Data 649
27.5.3 Analysis 649
27.6 Conclusions 655
References 656
Chapter 28: MODIS-Derived Terrestrial Primary Production 659
28.1 Introduction 661
28.2 Description of MODIS GPP/NPP 662
28.2.1 Theoretical Basis of the Algorithm 662
28.2.2 The Algorithm 663
28.2.3 Data Flow and Products 664
28.3 Input Uncertainties and the Algorithm 666
28.4 Validation 668
28.5 Processing Improvements and the Algorithm 670
28.6 Global Six-Year (2000–2005) Results 673
28.6.1 Mean Annual GPP, NPP and QC 673
28.6.2 Seasonality 674
28.6.3 Interannual Variability 676
28.7 Land Management and Biospheric Monitoring Applications 678
28.8 Future Directions 680
Abbreviations 659
References 681
Chapter 29: MODIS-Derived Global Fire Products 685
29.1 Introduction 685
29.2 MODIS Active Fire Product (MOD14) Status and Validation 686
29.3 Examples of MODIS Active Fire Studies 690
29.3.1 The Fire Information for Resource Management System 690
29.3.2 Amazon Multi-source Fire Integration 693
29.4 The Burned Area Product (MCD45) 695
29.4.1 Algorithm Overview 695
29.4.2 Product Overview 696
29.4.3 Product Examples 697
29.5 Conclusions 700
References 701
Chapter 30: MODIS Snow and Ice Products, and Their Assessment and Applications 704
30.1 Introduction 704
30.2 MODIS Snow Products 708
30.2.1 MODIS/Terra Snow Cover 5-Min L2 Swath 500 m 713
30.2.2 MODIS/Terra Snow Cover Daily L3 Global 500-m SIN Grid 714
30.2.3 MODIS/Terra Snow Cover Daily L3 Global 0.05° CMG 715
30.2.4 MODIS/Terra Snow Cover 8-Day L3 Global 500-m SIN Grid 715
30.2.5 MODIS/Terra Snow Cover 8-Day L3 Global 0.05° CMG 715
30.2.6 MODIS/Terra Snow Cover Monthly L3 Global 0.05° CMG 715
30.3 Evaluation of Errors in the Snow Products 716
30.4 Applications of MODIS Snow Products 718
30.4.1 Determination of Snow-Covered Area 719
30.4.2 Hydrological Applications 721
30.4.3 Data Assimilation Model Applications 723
30.4.4 Operational, Educational, and Public Outreach Applications 723
30.4.5 Blending MODIS and Passive-Microwave Snow Data Products 724
30.5 MODIS Sea Ice Products 724
30.5.1 MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1 km 725
30.5.2 MODIS/Terra Sea Ice Extent Daily L3 Global EASE-Grid Day 725
30.5.3 MODIS/Terra Sea Ice Extent and Ice Surface Temperature Daily L3 Global 4-km EASE-Grid Day 726
30.6 Summary 726
References 727
Chapter 31: Characterizing Global Land Cover Type and Seasonal Land Cover Dynamics at Moderate Spatial Resolution With MODIS 731
31.1 Introduction 731
31.2 Background and Scientific Context 731
31.2.1 Significance of Land Cover and Land Cover Dynamics 731
31.3 The MODIS Land Cover Product 732
31.4 Algorithm Descriptions 735
31.4.1 MODIS Global Land Cover (MOD12Q1) 735
31.4.1.1 Training Data 735
31.4.1.2 Input Features 736
31.4.1.3 Decision Tree Classification Theory 736
31.4.1.4 Boosting 737
31.4.1.5 Estimating Class Conditional and Posterior Probabilities 738
31.4.2 MODIS Land Cover Dynamics (MOD12Q2) 739
31.4.2.1 Reprocessing and Current Status 740
31.5 Conclusions and Future Prospects 742
References 743
Chapter 32: MODIS Vegetative Cover Conversion and Vegetation Continuous Fields 747
32.1 Introduction 747
32.2 Pre-processing 748
32.2.1 250-m Composite 748
32.2.2 500-m Composite 749
32.2.3 Future Production 750
32.3 Vegetation Continuous Fields 750
32.3.1 Introduction 750
32.3.2 Methods 751
32.3.3 Results 753
32.3.4 Validation 753
32.4 Vegetative Cover Conversion 754
32.4.1 Introduction 754
32.4.2 Deforestation 755
32.4.2.1 Method 755
32.4.2.2 Results 757
32.4.2.3 Validation 757
32.4.3 Change Due to Burning 760
32.4.3.1 Method 760
32.4.3.2 Results 762
32.4.3.3 Validation 762
32.4.4 Flooding 762
32.4.4.1 Method 762
32.4.4.2 Results 764
32.4.4.3 Validation 765
32.5 Conclusion 766
References 766
Chapter 33: Multisensor Global Retrievals of Evapotranspiration for Climate Studies Using the Surface Energy Budget System 768
33.1 Introduction 768
33.2 Modeling Evapotranspiration 770
33.2.1 Surface Energy Balance System: The Interpretive Model 770
33.2.2 Data Sources 771
33.2.2.1 Remote Sensing Variables 771
33.2.2.2 Meteorological Forcing 773
33.3 Algorithm Validation 773
33.3.1 Local- and Regional-Scale Flux Validation 774
33.3.1.1 Results from Local-Scale (Tower Based) Forcing Data 774
33.3.1.2 Results from the Regional-Scale (NLDAS) Forcing Data 776
33.3.1.3 Summary of Local- and Regional-Scale Validation 780
33.3.2 Globally Distributed Evapotranspiration Validation 780
33.3.2.1 CEOP In-Situ Data and Site Characteristics 781
33.3.2.2 Results from the Globally Distributed Tower-Based Flux Data 781
33.3.2.3 Results from MODIS-CEOP and MODIS-GLDAS Derived Fluxes 784
33.3.2.4 Summary of Globally Distributed Evapotranspiration Validation 785
33.4 Application with EOS-Terra and Aqua Data 786
33.4.1 Regional- to Continental-Scale Investigations Using the Oklahoma Mesonet 787
33.4.2 Developing a Multisensor Approach Toward Global Estimation 791
33.5 Current Status and Future Direction 794
33.5.1 Problems and Issues in Remote Retrievals 794
33.5.2 Future Directions 795
References 797
Part VI: The Future of Land Remote Sensing 800
Chapter 34: The Evolution of U.S. Moderate Resolution Optical Land Remote Sensing from AVHRR to VIIRS 801
34.1 The Origins of Moderate Resolution Land Remote Sensing: The AVHRR 801
34.2 Science Quality Data from the EOS MODIS Instrument 804
34.2.1 Development of the MODIS 804
34.2.2 The MODIS Data System 805
34.3 NPOESS and the NPOESS Preparatory Project 807
34.4 Land Remote Sensing with VIIRS 810
34.4.1 The Instrument 810
34.4.2 The VIIRS Land EDRs and Intermediate Products 811
34.4.2.1 The Albedo EDR 815
34.4.2.2 The Land Surface Temperature EDR 815
34.4.2.3 Snow Cover/Depth EDR 816
34.4.2.4 The Vegetation Index EDR 816
34.4.2.5 Surface Type EDR 816
34.4.2.6 Active Fire EDR 817
34.4.2.7 Directional Surface Reflectance: A Retained IP 818
34.4.3 VIIRS Data System and Services 818
34.5 Meeting Future Moderate Resolution Land Data Needs 819
34.5.1 The Challenge of Transitioning from Research to Operations 819
34.5.2 The International Dimension 822
34.5.3 Concluding Remarks 822
References 823
Chapter 35: The Future of Landsat-Class Remote Sensing 827
35.1 Introduction: Importance of Landsat-Class Observations1 827
35.2 Background: Origin and Evolution of Landsat-Class Observations 828
35.2.1 Origin of Landsat Resolution Observatories 829
35.2.2 Landsat Evolution 830
35.2.2.1 Sensor Technology 830
35.2.2.2 An Experiment in Privatization 831
35.2.2.3 Systematic Global Coverage 831
35.2.2.4 National Satellite Land Remote Sensing Data Archive 832
35.2.2.5 Landsat Spin-Offs in the United States 832
35.2.3 International Contributions 833
35.3 Current Status of Landsat-Class Observatories 833
35.3.1 United States Programs 833
35.3.1.1 Landsat 5 833
35.3.1.2 Landsat 7 834
35.3.2 International Efforts 834
35.3.2.1 Resolution, Swath Width, Spectral Bands 834
35.3.2.2 Temporal Repeat Frequency 836
35.3.2.3 Global Survey Mission 836
35.3.2.4 Nadir Pointing 836
35.4 Near-Future Landsat-Class Imaging Plans 837
35.4.1 Landsat Data Continuity Mission 837
35.4.1.1 Mission Specifications 837
35.4.1.2 Thermal Infrared Measurements 838
35.4.2 National Land Imaging Program 838
35.4.3 International Missions 839
35.4.3.1 Cbers 840
35.4.3.2 Hj-1c, -1d 840
35.4.3.3 Sentinel 2 840
35.4.3.4 Irs-2c 841
35.4.3.5 Ingenio 841
35.4.3.6 Dmc 841
35.4.4 Global Earth Observation System of Systems and the U.S. Group on Earth Observations 841
35.4.4.1 Constellations 842
35.4.4.2 Global Systematic Monitoring 842
35.4.4.3 Spectral Coverage 842
35.4.4.4 Atmospheric Attenuation 844
35.5 From Data to Measurements: Next Phase of Landsat-Class Remote Sensing 844
35.5.1 Constraints in Developing Landsat-Class Measurements 844
35.5.1.1 LDCM and USGS Archive Data Policy 844
35.5.1.2 Image Mapping Standards 845
35.5.2 Steps Toward Landsat-Class Earth System Data Records 846
35.5.2.1 Development of the First Decadal Datasets 846
35.5.2.2 Global Land Survey Datasets 846
35.5.2.3 International Cooperator Archives 847
35.5.3 Advanced Data Processing Initiatives 847
35.5.3.1 Scene Merging with Automated Cloud and Shadow Detection 847
35.5.3.2 Synthesis of Passive Optical and Active Sensor Data 848
35.6 Possible Future Mission Goals 848
35.6.1 Temporal Resolution 849
35.6.1.1 Constellations 849
35.6.1.2 How Do Constellations Work? 849
35.6.1.3 Constellation Standards 849
35.6.2 Mission Costs 850
35.6.2.1 Smallsats 850
35.6.2.2 Imaging Spectrometers 850
35.7 Closing Thoughts 851
References 852
Chapter 36: International Coordination of Satellite Land Observations: Integrated Observations of the Land 855
36.1 Introduction 855
36.2 The Need for Land Observations 856
36.3 Stakeholders for Global Land Observations 859
36.4 Products and Observables 859
36.4.1 Land Use, Land Use Change 861
36.4.2 Biophysical Properties Relating to Ecosystem Dynamics 862
36.4.3 Fire 864
36.4.4 Biodiversity and Conservation 865
36.4.5 Agriculture 866
36.4.6 Soils 867
36.4.7 Human Settlements and Socio-Economic Data 868
36.4.8 Water Availability and Use 869
36.4.9 Topography 870
36.5 Concluding Comments 870
References 873