Long description:

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.

Table of Contents:

Section I: Introduction to SA in Earth Observation (EO)
1. Overview of Sensitivity Analysis Methods in Earth Observation Modeling

L. Lee, P.K. Srivastava, G.P. Petropoulos

2. Model Input Data Uncertainty and its Potential Impact on Soil Properties

T. Mannschatz, P. Dietrich

Section II : Local SA Methods: Case Studies
3. Local Sensitivity Analysis of the LandSoil Erosion Model Applied to a Virtual Catchment

R. Caimpalini, S. Follain, B. Cheviron, Y. Le Bissonnais, A. Couturier

4. Sensitivity of Vegetation Phenological Parameters from Satellite Sensors to Spatial Resolution and Temporal Compositing Period

G.L. Mountford, P.M. Atkinson, J. Dash, T. Lankester, S. Hubbard

5. Radar Rainfall Sensitivity Analysis Using Multivariate Distributed Ensemble Generator

Q. Dai, D. Han, P.K. Srivastava

6. Field-Scale Sensitivity of Vegetation Discrimination to Hyperspectral Reflectance and Coupled Statistics

K. Manevski, M. Jabloun, M. Gupta, C. Kalaitzidis

Section III: Global (or Variance)-Based SA Methods: Case Studies
7. A Multimethod Global Sensitivity Analysis Approach to Support the Calibration and Evaluation of Land Surface Models

F. Pianosi, J. Iwema, R. Rosolem, T. Wagener

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

J. Rohmer, A. Loschetter, D. Raucoules

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. Sellitto

10. Global Sensitivity Analysis for Uncertain Parameters, Models, and Scenarios

M. Ye, M.C. Hill

Section IV: Other SA Methods: Case Studies
11. Sensitivity and Uncertainty Analyses for Stochastic Flood Hazard Simulation

Z. Micovic, M.G. Schaefer, B.L. Barker

12. Sensitivity of Wells in a Large Groundwater Monitoring Newtork and Its Evaluation Using GRACE Satellite Derived Information

V. Uddameri, A. Karim, E.A. Hernandez, P.K. Srivastava

13. Making the Most of the Earth Observation Data Using Effective Sampling Techniques

J. Indu, D. Nagesh Kumar

14. Ensemble-Based Multivariate Sensitivity Analysis of Satellite Rainfall Estimates Using Copula Model

S. Moazami, S. Golian

Section V: Software Tools in SA for EO
15. Efficient Tools for Global Sensitivity Analysis Based on High-Dimensional Model Representation

T. Ziehn, A.S. Tomlin

16. A Global Sensitivity Analysis Toolbox to Quantify Drivers of Vegetation Radiative Transfer Models

J. Verrelst, J.P. Rivera

17. GEM-SA: The Gaussian Emulation Machine for Sensitivity Analysis

M.C. Kennedy, G.P. Petropoulos

18. An Introduction to The SAFE Matlab Toolbox with Practical Examples and Guidelines

F. Sarrazin, F. Pianosi, T. Wagener

Section VI: Challenges and Future Outlook
19. Sensitivity in Ecological Modeling: From Local to Regional Scales

X. Song, B.A. Bryan, L. Gao, G. Zhao, M. Dong

20. Challenges and Future Outlook of Sensitivity Analysis

H. Gupta, S. Razavi