Sensitivity Analysis in Earth Observation Modelling

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.

Autorentext
Dr. George P. Petropoulos is Assistant Professor in Geoinformatics at the Department of Geography, Harokopio University of Athens Greece. His research focuses on the exploitation of geoinformation technology & geospatial data analysis techniques in geographical and environmental applications. He is author/co-author of over 110 articles, 40 book chapters, and has edited 6 books. He has developed collaborations with key scientists in his area of specialisation globally and his research & teaching work has received international recognition via several significant awards and research funding. Prashant K. Srivastava obtained his PhD from the Department of Civil Engineering at the University of Bristol in Bristol, UK,
and currently serves on the faculty at the Institute of Environment and Sustainable Development at Banaras Hindu University in
Varanasi, India. He formerly worked in the Hydrological Sciences Department at the NASA Goddard Space Flight Center and
is currently an investigator for several national and international projects. He has published 100+ papers, many books, and
several book chapters. He is also acting as an editorial board member of several reputed journals.

Inhalt
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

7. 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

8. 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

9. 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

10. 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

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

    J. Indu, D. Nagesh Kumar

12. 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

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

    J. Verrelst, J.P. Rivera

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

    M.C. Kennedy, G.P. Petropoulos

15. 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

16. Challenges and Future Outlook of Sensitivity Analysis

    H. Gupta, S. Razavi