Ramachandran / Justice / Abrams Land Remote Sensing and Global Environmental Change

NASA's Earth Observing System and the Science of ASTER and MODIS
1. Auflage 2010
ISBN: 978-1-4419-6749-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 11, 873 Seiten

Reihe: Remote Sensing and Digital Image Processing

Land Remote Sensing and Global Environmental Change: The Science of ASTER and MODIS is an edited compendium of contributions dealing with ASTER and MODIS satellite sensors aboard NASA's Terra and Aqua platforms launched as part of the Earth Observing System fleet in 1999 and 2002 respectively. This volume is divided into six sections. The first three sections provide insights into the history, philosophy, and evolution of the EOS, ASTER and MODIS instrument designs and calibration mechanisms, and the data systems components used to manage and provide the science data and derived products. The latter three sections exclusively deal with ASTER and MODIS data products and their applications, and the future of these two classes of remotely sensed observations.

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.

Autoren/Hrsg.

Ramachandran, Bhaskar

Justice, Christopher O.

Abrams, Michael J.

Inhaltsverzeichnis

1;Part I: The Earth Observing System and the Evolution of ASTER and MODIS;43
2;Chapter 1: Evolution of NASA’s Earth Observing System and Development of the Moderate-Resolution Imaging Spectroradiometer and ;45
2.1;1.1 Introduction;45
2.2;1.2 Evolution of NASA’s Earth Observing System;45
2.3;1.3 Development, Characterization, and Performance of the Earth Observing System Moderate Resolution Imaging Spectroradiomet;51
2.3.1;1.3.1 Background;51
2.3.2;1.3.2 Sensor Concepts;54
2.3.3;1.3.3 Performance;56
2.3.4;1.3.4 The Post-MODIS Future;62
2.4;1.4 History of the Advanced Spaceborne Thermal Emission and Reflection Radiometer;64
2.5;References;74
3;Chapter 2: Philosophy and Architecture of the EOS Data and Information System;77
3.1;2.1 Introduction;77
3.2;2.2 Inception and Early History;77
3.3;2.3 Early Architecture;80
3.4;2.4 An EOSDIS to Support the EOS Missions;81
3.5;2.5 Science Investigator-Led Processing Systems;83
3.6;2.6 Data Policy;84
3.7;2.7 EOSDIS and Participating Communities;85
3.8;2.8 Metrics;87
3.9;2.9 Lessons Learned and Best Practices Today;87
3.10;2.10 Next Generation Challenges;89
3.11;Reference;89
4;Chapter 3: Lessons Learned from the EOSDIS Engineering Experience;90
4.1;3.1 EOSDIS Core System Background;90
4.1.1;3.1.1 Incremental Method of Requirements Elaboration Proved Effective;90
4.1.2;3.1.2 Heavy Dependence on COTS Technology Increases the System’s Complexity in the Long Run;91
4.1.3;3.1.3 Incremental Releases Are a Much Better Way to Deploy Capability than Big-Bang Releases;92
4.1.4;3.1.4 Mode Management Approach to Software Development and Testing;93
4.1.5;3.1.5 Development of DAAC-Unique Extensions;93
4.1.6;3.1.6 Data Migration in Multi-petabyte ECS Archives Is a Continuous Operational Function;94
4.1.7;3.1.7 Silent Data Corruption Will Occur if Data Volumes Are Large Enough;94
4.1.8;3.1.8 Providing Access to Online Data;95
4.2;References;95
5;Part II: ASTER and MODIS: Instrument Design, Radiometry, and Geometry;96
6;Chapter 4: Terra ASTER Instrument Design and Geometry;99
6.1;4.1 Overview;99
6.2;4.2 Baseline Performance;99
6.3;4.3 System Layout;101
6.4;4.4 System Components;101
6.5;4.5 Spectral Performance;102
6.6;4.6 Radiometric Performance;105
6.7;4.7 Geometric Performance;111
6.8;4.8 Modulation Transfer Function;114
6.9;4.9 Level-1A Data Product;115
6.10;4.10 Level-1B Data Product;117
6.11;References;122
7;Chapter 5: ASTER VNIR and SWIR Radiometric Calibration and Atmospheric Correction;123
7.1;5.1 Introduction;123
7.2;5.2 Conversion to At-Sensor, Spectral Radiance;124
7.2.1;5.2.1 Unit Conversion Coefficients;124
7.3;5.3 Determination of Radiometric Calibration Coefficients;125
7.3.1;5.3.1 Preflight Determination of Radiometric Calibration Coefficients;126
7.3.2;5.3.2 Onboard Calibration;126
7.3.3;5.3.3 Vicarious Calibration;128
7.3.3.1;5.3.3.1 Reflectance-Based Approach;129
7.3.4;5.3.4 Cross Calibration;133
7.3.5;5.3.5 OBC RCC Trends and Comparison to the Vicarious Calibration;133
7.3.5.1;5.3.5.1 RCC Trends;133
7.3.5.2;5.3.5.2 Comparison to the Vicarious Calibration;136
7.3.5.3;5.3.5.3 Radiometric Calibration Coefficients for VNIR and SWIR;139
7.4;5.4 Other Radiometric Performance Issues;140
7.4.1;5.4.1 Offset;141
7.4.2;5.4.2 Noise Equivalent Reflectance and Temperature;142
7.4.3;5.4.3 Modulation Transfer Function;142
7.4.4;5.4.4 SWIR Crosstalk;144
7.5;5.5 Atmospheric Correction;150
7.5.1;5.5.1 Method Description;150
7.5.2;5.5.2 LUT Resolution;151
7.5.3;5.5.3 Uncertainty Estimates;153
7.6;References;154
8;Chapter 6: ASTER TIR Radiometric Calibration and Atmospheric Correction;157
8.1;6.1 ASTER TIR Radiometry;157
8.1.1;6.1.1 Onboard Calibration;157
8.1.2;6.1.2 Responsivity Trend;158
8.1.3;6.1.3 Radiometric Calibration for Products;159
8.1.4;6.1.4 Vicarious Calibration;162
8.1.5;6.1.5 Stray Light;163
8.1.6;6.1.6 Other Radiometric Performances;164
8.2;6.2 ASTER TIR Atmospheric Correction;165
8.2.1;6.2.1 Theoretical Basis;165
8.2.2;6.2.2 Standard Atmospheric Correction for ASTER/TIR;166
8.2.2.1;6.2.2.1 Algorithm Overview;166
8.2.2.2;6.2.2.2 Implementation;166
8.2.2.3;6.2.2.3 Input Parameters to MODTRAN;167
8.2.2.4;6.2.2.4 Validation;168
8.2.3;6.2.3 Alternative Atmospheric Correction: Water Vapor Scaling Method;168
8.2.3.1;6.2.3.1 Algorithm Overview;168
8.2.3.2;6.2.3.2 Validation;170
8.3;References;170
9;Chapter 7: Terra and Aqua MODIS Design, Radiometry, and Geometry in Support of Land Remote Sensing;173
9.1;7.1 Introduction;173
9.2;7.2 MODIS Sensor Design;176
9.2.1;7.2.1 MODIS Optics;176
9.2.2;7.2.2 Focal Plane Assemblies;177
9.2.3;7.2.3 Onboard Calibrators;178
9.3;7.3 Radiometric Calibration;181
9.3.1;7.3.1 Reflective Solar Band Calibration;181
9.3.2;7.3.2 Thermal Emissive Band Calibration;183
9.3.3;7.3.3 On-Orbit Performance;185
9.4;7.4 MODIS Geometry;192
9.5;7.5 Cross-Calibration of Terra and Aqua MODIS;196
9.6;7.6 Summary;200
9.7;References;202
10;Part III: ASTER and MODIS: Data Systems;205
11;Chapter 8: ASTER and MODIS Land Data Management at the Land Processes, and National Snow and Ice Data Centers;207
11.1;8.1 Introduction;207
11.2;8.2 ASTER Data Management at LP DAAC;209
11.2.1;8.2.1 History of ASTER Data Management;209
11.2.2;8.2.2 ASTER Data Archival, Production, and Distribution Statistics;211
11.2.3;8.2.3 Contemporary ASTER Data Management;211
11.3;8.3 MODIS Land Data Management at LP DAAC;213
11.3.1;8.3.1 MODIS Land Data Archival and Distribution Statistics;216
11.4;8.4 MODIS Snow and Sea Ice Data Management at NSIDC DAAC;216
11.4.1;8.4.1 MODIS Snow and Sea Ice Data Archival and Distribution Statistics;220
11.4.2;8.4.2 MODIS Metadata Management and Quality Assurance Updates at Both Data Centers;220
11.4.3;8.4.3 ECS Evolution-Related Changes at Both Data Centers;221
11.5;8.5 Closing Thoughts;221
11.6;References;222
12;Chapter 9: An Overview of the EOS Data Distribution Systems;223
12.1;9.1 Introduction;223
12.2;9.2 History and Evolution;226
12.2.1;9.2.1 Version-0 Information Management Subsystem;226
12.2.2;9.2.2 Overview of EDG and WIST;228
12.3;9.3 Key User Interfaces for EOS MODIS and ASTER Data Products;228
12.3.1;9.3.1 EDG and the WIST;228
12.3.1.1;9.3.1.1 Searching for Data;229
12.3.1.2;9.3.1.2 Navigating Search Results;230
12.3.1.3;9.3.1.3 Browse Images;231
12.3.1.4;9.3.1.4 Data Access;231
12.3.1.5;9.3.1.5 Ordering;231
12.3.1.6;9.3.1.6 Subsetting;231
12.3.2;9.3.2 Data Pools;232
12.3.2.1;9.3.2.1 LP DAAC’s Data Pool;233
12.3.2.2;9.3.2.2 NSIDC DAAC’s Data Pool;233
12.3.3;9.3.3 EOS Clearinghouse;234
12.3.4;9.3.4 Global Change Master Directory;234
12.3.5;9.3.5 Interfaces Specializing in Land Processes Data;235
12.3.5.1;9.3.5.1 United States Geological Survey: Global Visualization Viewer;235
12.3.5.2;9.3.5.2 Oak Ridge National Laboratory’s Mercury System;235
12.3.5.3;9.3.5.3 MODIS Search ‘N Order Web Interface;236
12.4;9.4 Future;236
12.5;9.5 Appendix: Acronyms;241
12.6;References;242
13;Chapter 10: The Language of EOS Data: Hierarchical Data Format;243
13.1;10.1 Introduction;243
13.2;10.2 Brief History and Evolution of HDF;244
13.2.1;10.2.1 Early History of Scientific Data Formats;244
13.2.2;10.2.2 Origins of HDF;245
13.2.2.1;10.2.2.1 Features of HDF;245
13.2.2.2;10.2.2.2 The HDF Data Model;245
13.2.3;10.2.3 Why NASA Chose HDF for EOS; What Other SDFs Were Considered?;246
13.2.4;10.2.4 Pros and Cons of HDF (With Respect to Earth Science Data);247
13.3;10.3 Overview of the HDF Data Model;247
13.3.1;10.3.1 HDF4;247
13.3.2;10.3.2 HDF5;248
13.3.3;10.3.3 HDF-EOS;248
13.3.3.1;10.3.3.1 Swath Data Model;249
13.3.3.2;10.3.3.2 GRID Data Model;251
13.3.4;10.3.4 The EOS Metadata Standard;253
13.3.4.1;10.3.4.1 Metadata Data Model;253
13.3.4.2;10.3.4.2 Attribute Storage in HDF-EOS Files;254
13.4;10.4 How MODIS Uses HDF;255
13.4.1;10.4.1 MODIS;255
13.4.2;10.4.2 MODIS L1B as an Example of Hybrid HDF and HDF-EOS;255
13.4.3;10.4.3 HDF Objects;256
13.4.4;10.4.4 Higher-Level Products;257
13.4.5;10.4.5 MODIS Metadata;258
13.4.5.1;10.4.5.1 Collection Description;258
13.4.5.2;10.4.5.2 Spatial Domain Container;259
13.4.5.3;10.4.5.3 Range Date Time;259
13.4.5.4;10.4.5.4 Orbital Spatial Domain Container;261
13.4.5.5;10.4.5.5 Additional Attributes;261
13.5;10.5 How ASTER Uses HDF-EOS;261
13.5.1;10.5.1 ASTER;261
13.5.1.1;10.5.1.1 ASTER Level-1A Data;262
13.5.1.2;10.5.1.2 ASTER Level-1B Data;262
13.5.1.3;10.5.1.3 ASTER Level-2 and Level-3 Products;262
13.5.2;10.5.2 ASTER Metadata;263
13.6;10.6 Software Support for HDF and HDF-EOS;263
13.7;References;267
14;Part IV: ASTER Science and Applications;268
15;Chapter 11: The ASTER Data System: An Overview of the Data Products in Japan and in the United States;271
15.1;11.1 Introduction;272
15.2;11.2 ASTER Data Flow Overview;273
15.3;11.3 The Role of ASTER GDS;274
15.3.1;11.3.1 Mission Operations;274
15.3.2;11.3.2 Data Processing;275
15.4;11.4 The Role of the Land Processes DAAC;277
15.5;11.5 ASTER Standard Data Products;278
15.6;11.6 Access to ASTER Data and Products;279
15.6.1;11.6.1 Aster Gds;280
15.6.2;11.6.2 LP DAAC;281
15.7;11.7 Conclusions;282
15.8;References;282
16;Chapter 12: ASTER Applications in Volcanology;283
16.1;12.1 Introduction;283
16.2;12.2 Surface Temperature Mapping;283
16.3;12.3 Volcano Observations with ASTER;285
16.3.1;12.3.1 Sulfur Dioxide Flux Estimation at Miyakejima Volcano, Japan;285
16.3.1.1;12.3.1.1 Introduction;285
16.3.1.2;12.3.1.2 The SO2 Flux Estimation Using a Thermal Infrared Multispectral Scanner;285
16.3.2;12.3.2 Thermal Monitoring of the 2006 Merapi Volcano Eruption, Indonesia;287
16.3.2.1;12.3.2.1 Introduction;287
16.3.2.2;12.3.2.2 Volcanic Activity of Merapi Volcano;287
16.3.2.3;12.3.2.3 Analysis of Daytime ASTER Images of the Merapi 2006 Eruption;288
16.3.2.4;12.3.2.4 Analysis of Nighttime ASTER Images of the Merapi 2006 Eruption;288
16.3.3;12.3.3 The 2005 Sierra Negra Eruption, Galapagos Islands;289
16.3.3.1;12.3.3.1 Introduction;289
16.3.3.2;12.3.3.2 ASTER Image Analyses;291
16.3.4;12.3.4 Discolored Seawater Observation at Satsuma-Iwojima, Japan;291
16.3.4.1;12.3.4.1 Introduction;291
16.3.4.2;12.3.4.2 Geologic Setting of Satsuma-Iwojima;293
16.3.4.3;12.3.4.3 Satellite Image Analyses;294
16.3.5;12.3.5 The 2005 Fukutoku-Okanoba Submarine Volcano Eruption, Japan;295
16.3.5.1;12.3.5.1 Introduction;295
16.3.5.2;12.3.5.2 Discolored Seawater and Floating Objects Analysis with ASTER VNIR;296
16.3.6;12.3.6 The 2006 Home Reef Submarine Volcano Eruption in Tonga;298
16.3.6.1;12.3.6.1 Introduction;298
16.3.6.2;12.3.6.2 ASTER Images of Home Reef;299
16.3.7;12.3.7 Detection of Flank and Summit Thermal Anomalies in Advance of the Chikurachki Volcano’s 2003 Eruption;300
16.3.7.1;12.3.7.1 Introduction;300
16.3.7.2;12.3.7.2 Analysis;301
16.4;12.4 Global Volcano Observation Plan, the ASTER Image Database for Volcanoes, and the ASTER Volcano Archive;304
16.4.1;12.4.1 Introduction;304
16.4.2;12.4.2 The Global Volcano Observation Plan with ASTER;305
16.4.3;12.4.3 ASTER Image Database for Volcanoes;306
16.4.4;12.4.4 The ASTER Volcano Archive;306
16.5;12.5 Conclusions;308
16.6;References;309
17;Chapter 13: Issues Affecting Geological Mapping with ASTER Data: A Case Study of the Mt Fitton Area, South Australia;311
17.1;13.1 Introduction;311
17.1.1;13.1.1 Sensor Resolution Issues;311
17.1.2;13.1.2 Atmospheric Effects and SWIR Crosstalk Issues;314
17.2;13.2 Mt Fitton Test Site, South Australia;315
17.3;13.3 Previous Remote Sensing Studies at Mt Fitton;316
17.4;13.4 ASTER Pre-processing and Mineral Map Generation;319
17.4.1;13.4.1 ASTER Data Levels and Products;319
17.4.2;13.4.2 ASTER SWIR Crosstalk Correction;322
17.4.3;13.4.3 Geological and Mineral Information Extraction Techniques for Multitemporal Mapping;322
17.5;13.5 Results;324
17.5.1;13.5.1 AlOH, MgOH/Carbonate, and Ferrous Iron Mapping Using ASTER SWIR Radiance-at-Sensor Data;324
17.5.2;13.5.2 Seasonal and Pre-processing Effects on ASTER SWIR Mapping Results;325
17.5.3;13.5.3 The Significance of Topographic Illumination Effects on ASTER SWIR Results;330
17.5.4;13.5.4 Estimation and Correction of ASTER SWIR Radiance Offsets;332
17.5.5;13.5.5 Geological Mapping Results with ASTER Thermal Infrared Data;335
17.6;13.6 Conclusions;336
17.7;References;337
18;Chapter 14: ASTER Data Use in Mining Applications;339
18.1;14.1 Introduction;339
18.2;14.2 Regional Reconnaissance and Mineral Assessment;341
18.2.1;14.2.1 High Zagros and Jebal Barez Mountains, Zagros Magmatic Arc, Iran;341
18.3;14.3 District-Scale Alteration Mapping;347
18.3.1;14.3.1 Chimborazo-Zaldivar Mining District, Northern Chile;347
18.4;14.4 Localized Fieldwork and Logistics;354
18.4.1;14.4.1 Oyu Tolgoi Mining District, Mongolia;354
18.5;14.5 Summary and Conclusions;359
18.6;References;360
19;Chapter 15: ASTER Imaging and Analysis of Glacier Hazards;362
19.1;15.1 Introduction;362
19.2;15.2 Why Are Glaciers Dangerous, and How Can Satellite Imaging Assist?;364
19.3;15.3 Types and Case Examples of Glacier Hazards and Disasters;366
19.4;15.4 Satellite Vision: Capabilities and Limitations;373
19.4.1;15.4.1 Palcacocha: What Is Visible from the Ground and Space?;373
19.4.2;15.4.2 The 2003 Palcacocha Crisis;384
19.5;15.5 Alpine Glacier Hazards Evolution in this Century of Global Warming;385
19.5.1;15.5.1 List of Causes of Evolution of Glacier Hazards;387
19.6;15.6 A Future Technological Approach to Hazards Detection and Predictive Modeling;389
19.6.1;15.6.1 Fuzzy Logic for the Autonomous Assessment of Glacier Hazards;393
19.6.2;15.6.2 Fuzzy Expert Systems for Glacier-Induced Hazards Assessment;395
19.7;15.7 Toward a System and Protocol for Glacier Hazards Research and Communications;402
19.8;References;406
20;Chapter 16: ASTER Application in Urban Heat Balance Analysis: A Case Study of Nagoya;411
20.1;16.1 Introduction;411
20.2;16.2 Study Area and Data Used;412
20.2.1;16.2.1 Description of the Study Area;412
20.2.2;16.2.2 Satellite Data and Preprocessing;413
20.2.3;16.2.3 Meteorological Data and Preprocessing;415
20.3;16.3 Methodology of Heat Flux Calculation;417
20.4;16.4 Results and Discussion;421
20.4.1;16.4.1 Spatial Pattern and Temporal Variation of Artificial Increase in Sensible Heat Flux;423
20.4.2;16.4.2 Assessment of the Accuracy of Has Estimation;424
20.4.3;16.4.3 Comparison of Seasonal and Temporal Variations in Has at Specific Sites;425
20.4.4;16.4.4 Contributions of Has and Hn as Causes of the Heat-Island Effect;426
20.4.5;16.4.5 Sensitivity Study of Has Calculation;426
20.4.6;16.4.6 Comparison of Heat Flux Ratio with in Situ Observations;427
20.5;16.5 Summary;429
20.6;References;430
21;Chapter 17: Monitoring Urban Change with ASTER Data;432
21.1;17.1 Introduction;432
21.1.1;17.1.1 Scale and Resolution;433
21.1.2;17.1.2 Spectral and Radiometric Properties for Urban Monitoring;435
21.1.3;17.1.3 Height Extraction;438
21.1.4;17.1.4 ASTER Science Team Acquisition Request (City STAR) for Urban Areas and Urban Environmental Monitoring Project at Arizo;438
21.2;17.2 Technical Specification and Applications for Urban Analysis;440
21.2.1;17.2.1 Urban Vegetation and Open Space Detection Versus Developed and Impervious Surfaces;440
21.2.2;17.2.2 Urban Landscape Structure;441
21.2.3;17.2.3 Thermal Analysis and Pattern;443
21.3;17.3 Monitoring Urban Areas: Latest Urban Environmental Monitoring Project Research Endeavors;446
21.3.1;17.3.1 Object-Oriented Land Use/Land Cover Classification: Phoenix Versus Las Vegas;447
21.3.2;17.3.2 Urban Land Cover Mapping Using ASTER: A Concept for Designing Practical Classification Schemes for “100 Cities”;448
21.4;17.4 Conclusions and Outlook;450
21.5;References;451
22;Chapter 18: Estimation of Methane Emission from West Siberian Lowland with Subpixel Land Cover Characterization Between MODIS a;455
22.1;18.1 Introduction;456
22.2;18.2 Methodology;457
22.2.1;18.2.1 Outline of this Research;457
22.2.2;18.2.2 Study Area;457
22.2.3;18.2.3 CH4 Flux Measurement;459
22.2.4;18.2.4 ASTER and MODIS Data Sets Used in the Study;460
22.2.5;18.2.5 Spectral Mixture Analysis;463
22.3;18.3 Results and Discussions;464
22.3.1;18.3.1 Spectral Mixture Analysis;464
22.3.2;18.3.2 Accuracy Assessment;466
22.3.3;18.3.3 Estimation of CH4 Emission;468
22.4;18.4 Discussions;469
22.5;18.5 Concluding Remarks;470
22.6;References;470
23;Chapter 19: ASTER Stereoscopic Data and Digital Elevation Models*;472
23.1;19.1 Introduction;472
23.2;19.2 ASTER, Stereoscopy, and DEMs;473
23.2.1;19.2.1 Basic Aspects of ASTER Stereoscopic Data;473
23.2.2;19.2.2 Basic Aspects of DEM Stereoscopy1;476
23.3;19.3 ASTER DEM Production at the LP DAAC;479
23.3.1;19.3.1 DEM Generation Algorithms;480
23.3.2;19.3.2 DEM Products and Validation;483
23.4;19.4 ASTER DEM Production at ERSDAC, Japan;485
23.4.1;19.4.1 DEM Generation Algorithms;486
23.4.2;19.4.2 Products Description and Validation;488
23.5;19.5 Concluding Remarks;492
23.6;References;492
24;Chapter 20: Using ASTER Stereo Images to Quantify Surface Roughness;495
24.1;20.1 Introduction;496
24.2;20.2 Approach;497
24.2.1;20.2.1 Relative SR Estimates;497
24.2.2;20.2.2 Calibration;499
24.2.3;20.2.3 ASTER Stereo Data;500
24.3;20.3 Methods;501
24.3.1;20.3.1 Field Work;501
24.3.2;20.3.2 Reflection Model;502
24.3.3;20.3.3 Atmospheric Corrections;502
24.4;20.4 Results;502
24.5;20.5 Discussion;508
24.6;20.6 Summary and Conclusion;510
24.7;Acknowledgments;512
24.8;References;512
25;Chapter 21: Technoscientific Diplomacy: The Practice of International Politics in the ASTER Collaboration;514
25.1;21.1 Introduction;514
25.2;21.2 Trying to Share Separately;516
25.3;21.3 Enacting Japan–U.S. Partnership;527
25.4;21.4 Conclusion;530
25.5;References;532
26;Part V: MODIS Science and Applications;535
27;Chapter 22: MODIS Land Data Products: Generation, Quality Assurance and Validation;538
27.1;22.1 Introduction;538
27.2;22.2 Land Products;539
27.3;22.3 MODIS Data Production;540
27.3.1;22.3.1 Data Flows;542
27.3.2;22.3.2 The MODIS Adaptive Processing System;544
27.3.3;22.3.3 Software Integration and Testing;546
27.3.4;22.3.4 Algorithm Improvements;546
27.4;22.4 MODIS Reprocessing: Collections;547
27.5;22.5 Quality Assessment;549
27.5.1;22.5.1 Rationale for Quality Assessment;549
27.5.2;22.5.2 MODIS Land Quality Assessment Roles;550
27.5.3;22.5.3 Product Quality Documentation;550
27.5.4;22.5.4 LDOPE Web Site;552
27.5.4.1;22.5.4.1 Known Issues;552
27.5.4.2;22.5.4.2 Global Browse;553
27.5.4.3;22.5.4.3 Metadata Database;553
27.5.4.4;22.5.4.4 Time-Series Analysis;554
27.6;22.6 Validation Approach;555
27.7;22.7 Conclusion;558
27.8;References;558
28;Chapter 23: MODIS Directional Surface Reflectance Product: Method, Error Estimates and Validation;561
28.1;23.1 Introduction;561
28.2;23.2 Theoretical Basis;562
28.3;23.3 MODIS AC Input Parameters;564
28.4;23.4 Radiative Transfer Modeling;564
28.5;23.5 Aerosol Inversion;565
28.6;23.6 Error Budget;569
28.7;23.7 Collection-5 MOD09;571
28.8;23.8 Performance of the MODIS C5 Algorithms;571
28.9;23.9 Future Plans;574
28.10;References;574
29;Chapter 24: Aqua and Terra MODIS Albedo and Reflectance Anisotropy Products;576
29.1;24.1 Introduction;576
29.2;24.2 MODIS Albedo and Reflectance Anisotropy Algorithm;577
29.3;24.3 Algorithm Quality;581
29.4;24.4 Summary;584
29.5;References;584
30;Chapter 25: MODIS Land Surface Temperature and Emissivity;589
30.1;25.1 Introduction;589
30.2;25.2 MODIS LST Algorithms and Their Implementation in the LST PGEs;593
30.3;25.3 Test Results of the V5 PGE16 Code;595
30.4;25.4 Validation and Uncertainty Analysis;599
30.5;25.5 Conclusions;601
30.6;References;602
31;Chapter 26: MODIS Vegetation Indices;604
31.1;26.1 Introduction;604
31.2;26.2 Definition and Theoretical Basis;605
31.3;26.3 Algorithm State and Heritage;606
31.3.1;26.3.1 Compositing Approach;608
31.3.2;26.3.2 Dynamic Range of the VI Products;609
31.4;26.4 Validation and Accuracy of the VI Product Suite;610
31.4.1;26.4.1 Angular Sources of Uncertainty;611
31.4.2;26.4.2 Atmosphere and Clouds;611
31.4.3;26.4.3 Biophysical Validation;612
31.5;26.5 Science and Applications;616
31.5.1;26.5.1 Carbon and Water Science;616
31.5.2;26.5.2 Phenology Studies;618
31.5.3;26.5.3 Societal Applications;619
31.6;26.6 Vegetation Index Continuity and Long-Term Data Records;620
31.7;26.7 Conclusions;622
31.8;References;623
32;Chapter 27: Leaf Area Index and Fraction of Absorbed PAR Products from Terra and Aqua MODIS Sensors: Analysis, Validation, and;628
32.1;27.1 Introduction;628
32.2;27.2 MODIS LAI/FPAR Algorithm and Products;629
32.3;27.3 Analysis of Collection 4 Terra LAI/FPAR Global Time-Series;630
32.3.1;27.3.1 The Collection 4 Algorithm;631
32.3.2;27.3.2 Data;633
32.3.3;27.3.3 Analysis;633
32.4;27.4 Validation of MODIS Terra LAI/FPAR Products;638
32.4.1;27.4.1 Validation Methodology;638
32.4.2;27.4.2 Validation Results;640
32.4.3;27.4.3 Sources of Retrieval Uncertainties;643
32.5;27.5 Generation of Improved Quality LAI/FPAR Products from Combination of Terra and Aqua Data;648
32.5.1;27.5.1 Collection 5 Algorithm Refinements;648
32.5.2;27.5.2 Data;649
32.5.3;27.5.3 Analysis;649
32.6;27.6 Conclusions;655
32.7;References;656
33;Chapter 28: MODIS-Derived Terrestrial Primary Production;659
33.1;28.1 Introduction;661
33.2;28.2 Description of MODIS GPP/NPP;662
33.2.1;28.2.1 Theoretical Basis of the Algorithm;662
33.2.2;28.2.2 The Algorithm;663
33.2.3;28.2.3 Data Flow and Products;664
33.3;28.3 Input Uncertainties and the Algorithm;666
33.4;28.4 Validation;668
33.5;28.5 Processing Improvements and the Algorithm;670
33.6;28.6 Global Six-Year (2000–2005) Results;673
33.6.1;28.6.1 Mean Annual GPP, NPP and QC;673
33.6.2;28.6.2 Seasonality;674
33.6.3;28.6.3 Interannual Variability;676
33.7;28.7 Land Management and Biospheric Monitoring Applications;678
33.8;28.8 Future Directions;680
33.9;Abbreviations;659
33.10;References;681
34;Chapter 29: MODIS-Derived Global Fire Products;685
34.1;29.1 Introduction;685
34.2;29.2 MODIS Active Fire Product (MOD14) Status and Validation;686
34.3;29.3 Examples of MODIS Active Fire Studies;690
34.3.1;29.3.1 The Fire Information for Resource Management System;690
34.3.2;29.3.2 Amazon Multi-source Fire Integration;693
34.4;29.4 The Burned Area Product (MCD45);695
34.4.1;29.4.1 Algorithm Overview;695
34.4.2;29.4.2 Product Overview;696
34.4.3;29.4.3 Product Examples;697
34.5;29.5 Conclusions;700
34.6;References;701
35;Chapter 30: MODIS Snow and Ice Products, and Their Assessment and Applications;704
35.1;30.1 Introduction;704
35.2;30.2 MODIS Snow Products;708
35.2.1;30.2.1 MODIS/Terra Snow Cover 5-Min L2 Swath 500 m;713
35.2.2;30.2.2 MODIS/Terra Snow Cover Daily L3 Global 500-m SIN Grid;714
35.2.3;30.2.3 MODIS/Terra Snow Cover Daily L3 Global 0.05° CMG;715
35.2.4;30.2.4 MODIS/Terra Snow Cover 8-Day L3 Global 500-m SIN Grid;715
35.2.5;30.2.5 MODIS/Terra Snow Cover 8-Day L3 Global 0.05° CMG;715
35.2.6;30.2.6 MODIS/Terra Snow Cover Monthly L3 Global 0.05° CMG;715
35.3;30.3 Evaluation of Errors in the Snow Products;716
35.4;30.4 Applications of MODIS Snow Products;718
35.4.1;30.4.1 Determination of Snow-Covered Area;719
35.4.2;30.4.2 Hydrological Applications;721
35.4.3;30.4.3 Data Assimilation Model Applications;723
35.4.4;30.4.4 Operational, Educational, and Public Outreach Applications;723
35.4.5;30.4.5 Blending MODIS and Passive-Microwave Snow Data Products;724
35.5;30.5 MODIS Sea Ice Products;724
35.5.1;30.5.1 MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1 km;725
35.5.2;30.5.2 MODIS/Terra Sea Ice Extent Daily L3 Global EASE-Grid Day;725
35.5.3;30.5.3 MODIS/Terra Sea Ice Extent and Ice Surface Temperature Daily L3 Global 4-km EASE-Grid Day;726
35.6;30.6 Summary;726
35.7;References;727
36;Chapter 31: Characterizing Global Land Cover Type and Seasonal Land Cover Dynamics at Moderate Spatial Resolution With MODIS;731
36.1;31.1 Introduction;731
36.2;31.2 Background and Scientific Context;731
36.2.1;31.2.1 Significance of Land Cover and Land Cover Dynamics;731
36.3;31.3 The MODIS Land Cover Product;732
36.4;31.4 Algorithm Descriptions;735
36.4.1;31.4.1 MODIS Global Land Cover (MOD12Q1);735
36.4.1.1;31.4.1.1 Training Data;735
36.4.1.2;31.4.1.2 Input Features;736
36.4.1.3;31.4.1.3 Decision Tree Classification Theory;736
36.4.1.4;31.4.1.4 Boosting;737
36.4.1.5;31.4.1.5 Estimating Class Conditional and Posterior Probabilities;738
36.4.2;31.4.2 MODIS Land Cover Dynamics (MOD12Q2);739
36.4.2.1;31.4.2.1 Reprocessing and Current Status;740
36.5;31.5 Conclusions and Future Prospects;742
36.6;References;743
37;Chapter 32: MODIS Vegetative Cover Conversion and Vegetation Continuous Fields;747
37.1;32.1 Introduction;747
37.2;32.2 Pre-processing;748
37.2.1;32.2.1 250-m Composite;748
37.2.2;32.2.2 500-m Composite;749
37.2.3;32.2.3 Future Production;750
37.3;32.3 Vegetation Continuous Fields;750
37.3.1;32.3.1 Introduction;750
37.3.2;32.3.2 Methods;751
37.3.3;32.3.3 Results;753
37.3.4;32.3.4 Validation;753
37.4;32.4 Vegetative Cover Conversion;754
37.4.1;32.4.1 Introduction;754
37.4.2;32.4.2 Deforestation;755
37.4.2.1;32.4.2.1 Method;755
37.4.2.2;32.4.2.2 Results;757
37.4.2.3;32.4.2.3 Validation;757
37.4.3;32.4.3 Change Due to Burning;760
37.4.3.1;32.4.3.1 Method;760
37.4.3.2;32.4.3.2 Results;762
37.4.3.3;32.4.3.3 Validation;762
37.4.4;32.4.4 Flooding;762
37.4.4.1;32.4.4.1 Method;762
37.4.4.2;32.4.4.2 Results;764
37.4.4.3;32.4.4.3 Validation;765
37.5;32.5 Conclusion;766
37.6;References;766
38;Chapter 33: Multisensor Global Retrievals of Evapotranspiration for Climate Studies Using the Surface Energy Budget System;768
38.1;33.1 Introduction;768
38.2;33.2 Modeling Evapotranspiration;770
38.2.1;33.2.1 Surface Energy Balance System: The Interpretive Model;770
38.2.2;33.2.2 Data Sources;771
38.2.2.1;33.2.2.1 Remote Sensing Variables;771
38.2.2.2;33.2.2.2 Meteorological Forcing;773
38.3;33.3 Algorithm Validation;773
38.3.1;33.3.1 Local- and Regional-Scale Flux Validation;774
38.3.1.1;33.3.1.1 Results from Local-Scale (Tower Based) Forcing Data;774
38.3.1.2;33.3.1.2 Results from the Regional-Scale (NLDAS) Forcing Data;776
38.3.1.3;33.3.1.3 Summary of Local- and Regional-Scale Validation;780
38.3.2;33.3.2 Globally Distributed Evapotranspiration Validation;780
38.3.2.1;33.3.2.1 CEOP In-Situ Data and Site Characteristics;781
38.3.2.2;33.3.2.2 Results from the Globally Distributed Tower-Based Flux Data;781
38.3.2.3;33.3.2.3 Results from MODIS-CEOP and MODIS-GLDAS Derived Fluxes;784
38.3.2.4;33.3.2.4 Summary of Globally Distributed Evapotranspiration Validation;785
38.4;33.4 Application with EOS-Terra and Aqua Data;786
38.4.1;33.4.1 Regional- to Continental-Scale Investigations Using the Oklahoma Mesonet;787
38.4.2;33.4.2 Developing a Multisensor Approach Toward Global Estimation;791
38.5;33.5 Current Status and Future Direction;794
38.5.1;33.5.1 Problems and Issues in Remote Retrievals;794
38.5.2;33.5.2 Future Directions;795
38.6;References;797
39;Part VI: The Future of Land Remote Sensing;800
40;Chapter 34: The Evolution of U.S. Moderate Resolution Optical Land Remote Sensing from AVHRR to VIIRS;801
40.1;34.1 The Origins of Moderate Resolution Land Remote Sensing: The AVHRR;801
40.2;34.2 Science Quality Data from the EOS MODIS Instrument;804
40.2.1;34.2.1 Development of the MODIS;804
40.2.2;34.2.2 The MODIS Data System;805
40.3;34.3 NPOESS and the NPOESS Preparatory Project;807
40.4;34.4 Land Remote Sensing with VIIRS;810
40.4.1;34.4.1 The Instrument;810
40.4.2;34.4.2 The VIIRS Land EDRs and Intermediate Products;811
40.4.2.1;34.4.2.1 The Albedo EDR;815
40.4.2.2;34.4.2.2 The Land Surface Temperature EDR;815
40.4.2.3;34.4.2.3 Snow Cover/Depth EDR;816
40.4.2.4;34.4.2.4 The Vegetation Index EDR;816
40.4.2.5;34.4.2.5 Surface Type EDR;816
40.4.2.6;34.4.2.6 Active Fire EDR;817
40.4.2.7;34.4.2.7 Directional Surface Reflectance: A Retained IP;818
40.4.3;34.4.3 VIIRS Data System and Services;818
40.5;34.5 Meeting Future Moderate Resolution Land Data Needs;819
40.5.1;34.5.1 The Challenge of Transitioning from Research to Operations;819
40.5.2;34.5.2 The International Dimension;822
40.5.3;34.5.3 Concluding Remarks;822
40.6;References;823
41;Chapter 35: The Future of Landsat-Class Remote Sensing;827
41.1;35.1 Introduction: Importance of Landsat-Class Observations1;827
41.2;35.2 Background: Origin and Evolution of Landsat-Class Observations;828
41.2.1;35.2.1 Origin of Landsat Resolution Observatories;829
41.2.2;35.2.2 Landsat Evolution;830
41.2.2.1;35.2.2.1 Sensor Technology;830
41.2.2.2;35.2.2.2 An Experiment in Privatization;831
41.2.2.3;35.2.2.3 Systematic Global Coverage;831
41.2.2.4;35.2.2.4 National Satellite Land Remote Sensing Data Archive;832
41.2.2.5;35.2.2.5 Landsat Spin-Offs in the United States;832
41.2.3;35.2.3 International Contributions;833
41.3;35.3 Current Status of Landsat-Class Observatories;833
41.3.1;35.3.1 United States Programs;833
41.3.1.1;35.3.1.1 Landsat 5;833
41.3.1.2;35.3.1.2 Landsat 7;834
41.3.2;35.3.2 International Efforts;834
41.3.2.1;35.3.2.1 Resolution, Swath Width, Spectral Bands;834
41.3.2.2;35.3.2.2 Temporal Repeat Frequency;836
41.3.2.3;35.3.2.3 Global Survey Mission;836
41.3.2.4;35.3.2.4 Nadir Pointing;836
41.4;35.4 Near-Future Landsat-Class Imaging Plans;837
41.4.1;35.4.1 Landsat Data Continuity Mission;837
41.4.1.1;35.4.1.1 Mission Specifications;837
41.4.1.2;35.4.1.2 Thermal Infrared Measurements;838
41.4.2;35.4.2 National Land Imaging Program;838
41.4.3;35.4.3 International Missions;839
41.4.3.1;35.4.3.1 Cbers;840
41.4.3.2;35.4.3.2 Hj-1c, -1d;840
41.4.3.3;35.4.3.3 Sentinel 2;840
41.4.3.4;35.4.3.4 Irs-2c;841
41.4.3.5;35.4.3.5 Ingenio;841
41.4.3.6;35.4.3.6 Dmc;841
41.4.4;35.4.4 Global Earth Observation System of Systems and the U.S. Group on Earth Observations;841
41.4.4.1;35.4.4.1 Constellations;842
41.4.4.2;35.4.4.2 Global Systematic Monitoring;842
41.4.4.3;35.4.4.3 Spectral Coverage;842
41.4.4.4;35.4.4.4 Atmospheric Attenuation;844
41.5;35.5 From Data to Measurements: Next Phase of Landsat-Class Remote Sensing;844
41.5.1;35.5.1 Constraints in Developing Landsat-Class Measurements;844
41.5.1.1;35.5.1.1 LDCM and USGS Archive Data Policy;844
41.5.1.2;35.5.1.2 Image Mapping Standards;845
41.5.2;35.5.2 Steps Toward Landsat-Class Earth System Data Records;846
41.5.2.1;35.5.2.1 Development of the First Decadal Datasets;846
41.5.2.2;35.5.2.2 Global Land Survey Datasets;846
41.5.2.3;35.5.2.3 International Cooperator Archives;847
41.5.3;35.5.3 Advanced Data Processing Initiatives;847
41.5.3.1;35.5.3.1 Scene Merging with Automated Cloud and Shadow Detection;847
41.5.3.2;35.5.3.2 Synthesis of Passive Optical and Active Sensor Data;848
41.6;35.6 Possible Future Mission Goals;848
41.6.1;35.6.1 Temporal Resolution;849
41.6.1.1;35.6.1.1 Constellations;849
41.6.1.2;35.6.1.2 How Do Constellations Work?;849
41.6.1.3;35.6.1.3 Constellation Standards;849
41.6.2;35.6.2 Mission Costs;850
41.6.2.1;35.6.2.1 Smallsats;850
41.6.2.2;35.6.2.2 Imaging Spectrometers;850
41.7;35.7 Closing Thoughts;851
41.8;References;852
42;Chapter 36: International Coordination of Satellite Land Observations: Integrated Observations of the Land;855
42.1;36.1 Introduction;855
42.2;36.2 The Need for Land Observations;856
42.3;36.3 Stakeholders for Global Land Observations;859
42.4;36.4 Products and Observables;859
42.4.1;36.4.1 Land Use, Land Use Change;861
42.4.2;36.4.2 Biophysical Properties Relating to Ecosystem Dynamics;862
42.4.3;36.4.3 Fire;864
42.4.4;36.4.4 Biodiversity and Conservation;865
42.4.5;36.4.5 Agriculture;866
42.4.6;36.4.6 Soils;867
42.4.7;36.4.7 Human Settlements and Socio-Economic Data;868
42.4.8;36.4.8 Water Availability and Use;869
42.4.9;36.4.9 Topography;870
42.5;36.5 Concluding Comments;870
42.6;References;873