Sensitivity Analysis in Earth Observation Modelling

Sensitivity Analysis in Earth Observation Modelling

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Product Details

ISBN-13: 9780128030110
Publisher: Elsevier Science
Publication date: 11/01/2016
Pages: 448
Product dimensions: 7.50(w) x 9.20(h) x 1.00(d)

About the Author

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.

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.

Table of Contents

Section I: Introduction toSA 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)-BasedSA 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: OtherSA 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

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