Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling.
Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement.
1. Section 1. Introduction to SA in Earth Observation (EO) - p. xxiii 2. Chapter 1. Overview of Sensitivity Analysis Methods in Earth Observation Modeling - p. 3 3. Chapter 2. Model Input Data Uncertainty and Its Potential Impact on Soil Properties - p. 25 4. Section 2. Local SA Methods: Case Studies - p. 52 5. Chapter 3. Local Sensitivity Analysis of the LandSoil Erosion Model Applied to a Virtual Catchment - p. 55 6. Chapter 4. Sensitivity of Vegetation Phenological Parameters: From Satellite Sensors to Spatial Resolution and Temporal Compositing Period - p. 75 7. Chapter 5. Radar Rainfall Sensitivity Analysis Using Multivariate Distributed Ensemble Generator - p. 91 8. Chapter 6. Field-Scale Sensitivity of Vegetation Discrimination to Hyperspectral Reflectance and Coupled Statistics - p. 103 9. Section 3. Global (or variance)-Based SA Methods: Case Studies - p. 121 10. Chapter 7. A Multimethod Global Sensitivity Analysis Approach to Support the Calibration and Evaluation of Land Surface Models - p. 125 11. Chapter 8. Global Sensitivity Analysis for Supporting History Matching of Geomechanical Reservoir Models Using Satellite InSAR Data: A Case Study at the CO2 Storage Site of In Salah, Algeria - p. 145 12. Chapter 9. Artificial Neural Networks for Spectral Sensitivity Analysis to Optimize Inversion Algorithms for Satellite-Based Earth Observation: Sulfate Aerosol Observations With High-Resolution Thermal Infrared Sounders - p. 161 13. Chapter 10. Global Sensitivity Analysis for Uncertain Parameters, Models, and Scenarios - p. 177 14. 1. Introduction - p. 177 15. 2. Morris Method - p. 180 16. 3. Sobol' Method - p. 182 17. 4. Sobol' Method for Multiple Models and Scenarios - p. 190 18. 5. Synthetic Study With Multiple Scenarios and Models - p. 195 19. 6. Using Global Sensitivity Analysis for Satellite Data and Models - p. 203 20. 7. Conclusions and Perspectives - p. 205 21. Section 4. Other SA Methods: Case Studies - p. 210 22. Chapter 11. Sensitivity and Uncertainty Analyses for Stochastic Flood Hazard Simulation - p. 213 23. Chapter 12. Sensitivity of Wells in a Large Groundwater Monitoring Network and Its Evaluation Using GRACE Satellite Derived Information - p. 235 24. Chapter 13. Making the Most of the Earth Observation Data Using Effective Sampling Techniques - p. 257 25. Chapter 14. Ensemble-Based Multivariate Sensitivity Analysis of Satellite Rainfall Estimates Using Copula Model - p. 273 26. Section 5. Software Tools in SA for EO - p. 294 27. Chapter 15. Efficient Tools for Global Sensitivity Analysis Based on High-Dimensional Model Representation - p. 297 28. Chapter 16. A Global Sensitivity Analysis Toolbox to Quantify Drivers of Vegetation Radiative Transfer Models - p. 319 29. Chapter 17. GEM-SA: The Gaussian Emulation Machine for Sensitivity Analysis - p. 341 30. Chapter 18. An Introduction to the SAFE Matlab Toolbox With Practical Examples and Guidelines - p. 363 31. Section 6. Challenges and Future Outlook - p. 378 32. Chapter 19. Sensitivity in Ecological Modeling: From Local to Regional Scales - p. 381 33. Chapter 20. Challenges and Future Outlook of Sensitivity Analysis - p. 397 34. Index - p. 417
Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling.
Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement.
1. Section 1. Introduction to SA in Earth Observation (EO) - p. xxiii 2. Chapter 1. Overview of Sensitivity Analysis Methods in Earth Observation Modeling - p. 3 3. Chapter 2. Model Input Data Uncertainty and Its Potential Impact on Soil Properties - p. 25 4. Section 2. Local SA Methods: Case Studies - p. 52 5. Chapter 3. Local Sensitivity Analysis of the LandSoil Erosion Model Applied to a Virtual Catchment - p. 55 6. Chapter 4. Sensitivity of Vegetation Phenological Parameters: From Satellite Sensors to Spatial Resolution and Temporal Compositing Period - p. 75 7. Chapter 5. Radar Rainfall Sensitivity Analysis Using Multivariate Distributed Ensemble Generator - p. 91 8. Chapter 6. Field-Scale Sensitivity of Vegetation Discrimination to Hyperspectral Reflectance and Coupled Statistics - p. 103 9. Section 3. Global (or variance)-Based SA Methods: Case Studies - p. 121 10. Chapter 7. A Multimethod Global Sensitivity Analysis Approach to Support the Calibration and Evaluation of Land Surface Models - p. 125 11. Chapter 8. Global Sensitivity Analysis for Supporting History Matching of Geomechanical Reservoir Models Using Satellite InSAR Data: A Case Study at the CO2 Storage Site of In Salah, Algeria - p. 145 12. Chapter 9. Artificial Neural Networks for Spectral Sensitivity Analysis to Optimize Inversion Algorithms for Satellite-Based Earth Observation: Sulfate Aerosol Observations With High-Resolution Thermal Infrared Sounders - p. 161 13. Chapter 10. Global Sensitivity Analysis for Uncertain Parameters, Models, and Scenarios - p. 177 14. 1. Introduction - p. 177 15. 2. Morris Method - p. 180 16. 3. Sobol' Method - p. 182 17. 4. Sobol' Method for Multiple Models and Scenarios - p. 190 18. 5. Synthetic Study With Multiple Scenarios and Models - p. 195 19. 6. Using Global Sensitivity Analysis for Satellite Data and Models - p. 203 20. 7. Conclusions and Perspectives - p. 205 21. Section 4. Other SA Methods: Case Studies - p. 210 22. Chapter 11. Sensitivity and Uncertainty Analyses for Stochastic Flood Hazard Simulation - p. 213 23. Chapter 12. Sensitivity of Wells in a Large Groundwater Monitoring Network and Its Evaluation Using GRACE Satellite Derived Information - p. 235 24. Chapter 13. Making the Most of the Earth Observation Data Using Effective Sampling Techniques - p. 257 25. Chapter 14. Ensemble-Based Multivariate Sensitivity Analysis of Satellite Rainfall Estimates Using Copula Model - p. 273 26. Section 5. Software Tools in SA for EO - p. 294 27. Chapter 15. Efficient Tools for Global Sensitivity Analysis Based on High-Dimensional Model Representation - p. 297 28. Chapter 16. A Global Sensitivity Analysis Toolbox to Quantify Drivers of Vegetation Radiative Transfer Models - p. 319 29. Chapter 17. GEM-SA: The Gaussian Emulation Machine for Sensitivity Analysis - p. 341 30. Chapter 18. An Introduction to the SAFE Matlab Toolbox With Practical Examples and Guidelines - p. 363 31. Section 6. Challenges and Future Outlook - p. 378 32. Chapter 19. Sensitivity in Ecological Modeling: From Local to Regional Scales - p. 381 33. Chapter 20. Challenges and Future Outlook of Sensitivity Analysis - p. 397 34. Index - p. 417
