Sensitivity Analysis in Earth Observation Modelling - 1st Edition - ISBN: 9780128030110, 9780128030318

Sensitivity Analysis in Earth Observation Modelling

1st Edition

Editors: George Petropoulos Prashant Srivastava
eBook ISBN: 9780128030318
Paperback ISBN: 9780128030110
Imprint: Elsevier
Published Date: 17th October 2016
Page Count: 448
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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.

Key Features

  • Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling
  • Provides readers with a roadmap for directing future efforts
  • Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling
  • Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow


Geospatial experts, GIS consultants, Modellers, Remote sensing researchers; Geophysical and Geological researchers, policy makers, environmental consultants

Table of Contents

  • Dedication
  • List of Contributors
  • Preface
  • Section 1. Introduction to SA in Earth Observation (EO)
    • Chapter 1. Overview of Sensitivity Analysis Methods in Earth Observation Modeling
      • 1. Introduction
      • 2. Local Sensitivity Analysis
      • 3. Global Sensitivity Analysis
      • 4. Graphical Methods for Global Sensitivity Analysis
      • 5. Conclusions
    • Chapter 2. Model Input Data Uncertainty and Its Potential Impact on Soil Properties
      • 1. Introduction
      • 2. A World of Models – How Can They Be Classified?
      • 3. Can We Trust Models? – Model Accuracy and Their Sensitivity to Input Data Uncertainty
      • 4. Selecting the Most Appropriate Model
      • 5. Why and How to Account for Modeling Uncertainties Caused by Different Input Data Sources
      • 6. Assessing Sensitivity of Environmental Models
      • 7. How Soil Texture Measured With Visible-Near-Infrared Spectroscopy Affects Hydrological Modeling: A Case Study
      • 8. What Did We Learn?
  • Section 2. Local SA Methods: Case Studies
    • Chapter 3. Local Sensitivity Analysis of the LandSoil Erosion Model Applied to a Virtual Catchment
      • 1. Introduction
      • 2. Materials and Methods
      • 3. Results and Discussion
      • 4. Concluding Remarks
    • Chapter 4. Sensitivity of Vegetation Phenological Parameters: From Satellite Sensors to Spatial Resolution and Temporal Compositing Period
      • 1. Introduction
      • 2. Monitoring Vegetation Phenology
      • 3. Sensitivity Analysis
      • 4. Sensitivity of Remotely Sensed Phenological Parameters
      • 5. Case Study
      • 6. Conclusion
    • Chapter 5. Radar Rainfall Sensitivity Analysis Using Multivariate Distributed Ensemble Generator
      • 1. Introduction
      • 2. Data and Methods
      • 3. Methodology
      • 4. Results and Discussion
      • 5. Conclusions
    • Chapter 6. Field-Scale Sensitivity of Vegetation Discrimination to Hyperspectral Reflectance and Coupled Statistics
      • 1. Introduction
      • 2. Background on Spectral Discrimination of Vegetation
      • 3. Sensitivity of Spectral Discrimination of Vegetation to the Type of Reflectance and Statistical Test
      • 4. Final Remarks
  • Section 3. Global (or variance)-Based SA Methods: Case Studies
    • Chapter 7. A Multimethod Global Sensitivity Analysis Approach to Support the Calibration and Evaluation of Land Surface Models
      • 1. Introduction
      • 2. Model and Methods
      • 3. Results
      • 4. Conclusions
    • 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
      • 1. Introduction
      • 2. Case Study
      • 3. Methods
      • 4. Application
      • Summary and Future Work
    • 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
      • 1. Introduction
      • 2. Data and Methods
      • 3. Results
      • 4. Conclusions
    • Chapter 10. Global Sensitivity Analysis for Uncertain Parameters, Models, and Scenarios
      • 1. Introduction
      • 2. Morris Method
      • 3. Sobol' Method
      • 4. Sobol' Method for Multiple Models and Scenarios
      • 5. Synthetic Study With Multiple Scenarios and Models
      • 6. Using Global Sensitivity Analysis for Satellite Data and Models
      • 7. Conclusions and Perspectives
  • Section 4. Other SA Methods: Case Studies
    • Chapter 11. Sensitivity and Uncertainty Analyses for Stochastic Flood Hazard Simulation
      • 1. Introduction
      • 2. Basic Principles of Stochastic Approach to Flood Hazard
      • 3. Uncertainty Associated With Stochastically Derived Flood Quantiles
      • 4. Results
      • 5. Effect of Earth Observations on Uncertainty in Probabilistic Flood Estimates
      • 6. Concluding Remarks
    • Chapter 12. Sensitivity of Wells in a Large Groundwater Monitoring Network and Its Evaluation Using GRACE Satellite Derived Information
      • 1. Introduction
      • 2. Methodology
      • 3. Study Area
      • 4. Results and Discussion
      • 5. Summary and Conclusions
    • Chapter 13. Making the Most of the Earth Observation Data Using Effective Sampling Techniques
      • 1. Introduction: Looking From Above
      • 2. Data Assimilation
      • 3. Sampling Schemes
      • 4. Bootstrap Sampling
      • 5. Latin Hypercube Sampling
      • 6. Case Study Using Bootstrap Sampling
      • 7. Conclusions
    • Chapter 14. Ensemble-Based Multivariate Sensitivity Analysis of Satellite Rainfall Estimates Using Copula Model
      • 1. Introduction
      • 2. Satellite Rainfall Estimates
      • 3. Methodology of Ensemble-Based Multivariate Analysis
      • 4. Application (Case Study) and Results
      • 5. Conclusions and Future Directions
  • Section 5. Software Tools in SA for EO
    • Chapter 15. Efficient Tools for Global Sensitivity Analysis Based on High-Dimensional Model Representation
      • 1. Introduction
      • 2. High-Dimensional Model Representation
      • 3. Graphical User Interface-High-Dimensional Model Representation Software
      • 4. Applications and Case Studies
      • 5. Summary and Conclusions
    • Chapter 16. A Global Sensitivity Analysis Toolbox to Quantify Drivers of Vegetation Radiative Transfer Models
      • 1. Introduction
      • 2. Variance-Based Global Sensitivity Analysis
      • 3. Radiative Transfer Models and ARTMO
      • 4. Global Sensitivity Analysis Toolbox
      • 5. Case Studies
      • 6. Discussion
      • 7. Conclusions
    • Chapter 17. GEM-SA: The Gaussian Emulation Machine for Sensitivity Analysis
      • 1. Bayesian Analysis of Computer Models
      • 2. Gaussian Process Prior Distribution for a Code Output
      • 3. Posterior Distribution After Observing Code Runs
      • 4. Functionality Included Within Gaussian Emulation for Sensitivity Analysis
      • 5. Uncertainty in Emulator Roughness Parameters
      • 6. Using the Gaussian Emulation for Sensitivity Analysis Interface
      • 7. Summary of Inputs/Outputs
      • 8. Case Study: SimSphere
      • 9. Using Gaussian Emulation for Sensitivity Analysis Emulators With Your Own Software
      • 10. Conclusions
    • Chapter 18. An Introduction to the SAFE Matlab Toolbox With Practical Examples and Guidelines
      • 1. Introduction
      • 2. Structure of the Toolbox
      • 3. Global Sensitivity Analysis Methods and Examples of Application
      • 4. Guidelines for the Implementation of Global Sensitivity Analysis
      • 5. Outlook
  • Section 6. Challenges and Future Outlook
    • Chapter 19. Sensitivity in Ecological Modeling: From Local to Regional Scales
      • 1. Introduction
      • 2. Sensitivity in Process-Based Ecological Models
      • 3. Time-Dependent Sensitivity and Its Implications
      • 4. Global Sensitivity Analysis in Social-Ecological Systems
      • 5. Sensitivity of Social-Ecological Models to Land Use Mapping Error
      • 6. Computing Strategy
      • 7. Concluding Remarks
    • Chapter 20. Challenges and Future Outlook of Sensitivity Analysis
      • 1. Introduction
      • 2. Brief Review of Some Commonly Used Sensitivity Analysis Methods
      • 3. Challenges and Future Outlook
      • 4. Conclusions
  • Index


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About the Editor

George Petropoulos

George Petropoulos

Dr. Petropoulos’ research work focuses on exploiting Earth Observation (EO) data alone or synergistically with land surface process models in deriving regional estimates of key state variables of the Earth's energy and water budget, including energy fluxes and soil surface moisture. He is also conducting research on the use of remote sensing technology in obtaining information about the land cover and if changes occurred from either anthropogenic activities (e.g. urbanization, mining activity) or natural hazards (mainly floods and fires). In this framework, he is researching and optimizing new image processing techniques to recently launched EO satellites, with a large part of his work focusing on the development and enhancement of EO-based operational products. As part of this research he is also conducting all-inclusive benchmarking studies to EO products or land surface models, including advanced sensitivity analysis techniques.

Affiliations and Expertise

Senior Lecturer, Remote Sensing and GIS, Aberystwyth University, UK

Prashant Srivastava

Dr. Srivastava is working in Hydrological Sciences, NASA Goddard Space Flight Center on SMAP satellite soil moisture retrieval algorithm development, instrumentation and simulation for various applications, and affiliated with IESD, Banaras Hindu University as a faculty. He received his PhD degree from Department of Civil Engineering, University of Bristol, Bristol, UK. He has published 100+ papers in peer-reviewed journals, published 4 books with reputed publishing houses and authored several book chapters and conference papers.

Affiliations and Expertise

Hydrological Sciences (Code 617), ESSIC/NASA Goddard Space Flight Center, Greenbelt, MD, USA

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