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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: ISTE Consignment
- Sprache: Englisch
Remote sensing data are now the primary sources for observing Earth and the Universe. Data inversion and assimilation techniques are the main tools for estimating and predicting the geophysical parameters that characterize the evolution of our planet and the Universe, using remote sensing data.
Inversion and Data Assimilation in Remote Sensing explores recent advances in the inversion and assimilation of remote sensing data. It presents traditional and current neural network methods, as well as a number of topics where these methods have been developed or adapted to suit the specific nature of the field. The assimilation section covers prediction problems in volcanology and glaciology. Lastly, the inversion section covers biomass inversion using SAR images, bio-physio-hydrological inversion in coastal areas using multi- and hyperspectral images, and astrophysical inversion using telescope data.
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Seitenzahl: 426
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Preface
PART 1: Data Assimilation
1 Methods for Assimilation of Observations: Application to Numerical Weather Prediction
1.1 Introduction
1.2 The linear and Gaussian case
1.3 Optimal interpolation – three-dimensional variational assimilation
1.4 Taking the dynamics of the flow into account
1.5 Particle filters
1.6 Artificial intelligence
1.7 Extensions and applications
1.8 References
2 Ensemble Data Assimilation in Volcanology
2.1 Volcano monitoring and eruption forecasting
2.2 Ensemble data assimilation
2.3 Potentiality assessment of volcanic data assimilation for eruption forecasting based on synthetic simulations
2.4 Application: The 2004–2014 inter-eruptive activity at Grímsvötn volcano, Iceland
2.5 Conclusions and outlook
2.6 Acknowledgments
2.7 References
3 Data Assimilation in Glaciology
3.1 Introduction
3.2 Predicting a paradigm shift for polar ice-sheet models
3.3 Principles of ice sheet dynamics
3.4 Parameter estimation
3.5 State and parameter estimation
3.6 Conclusions and outlook
3.7 References
PART 2: Inversion
4 Probabilistic Inversion Methods
4.1 Local methods versus global methods
4.2 Bayesian formalism
4.3 Model parameterization
4.4 Markov chain Monte Carlo-based sampling algorithms
4.5 Conclusions and outlook
4.6 References
5 Modeling Radar Backscattering from Forests: A First Step to Inversion
5.1. Introduction
5.2. Vegetation model historical background
5.3. How to choose a model for inversion?
5.4. Biomass inversion
5.5. Conclusions and outlook
5.6. References
6 Radiative Transfer Model Inversion and Application to Coastal Observation
6.1. Introduction
6.2. Principle and treatment method
6.3. Biophysical model of radiative transfer
6.4. Examples of applications in coastal areas
6.5. Conclusions and outlook
6.6. References
7 Deep-learning Analysis of Cherenkov Telescope Array Images
7.1. Gamma astronomy
7.2. Deep neural networks
7.3.
γ
-PhysNet: a multitasking architecture for the complete reconstruction of gamma events
7.4. Performance evaluation
7.5. Conclusions and outlook
7.6. Acknowledgments
7.7. References
List of Authors
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1. Model parameters and true values assigned for the synthetic simulat...
Chapter 7
Table 7.1. AUC score, precision and gamma/proton classification task recall fo...
Table 7.2. AUC score, precision and recall of the gamma/proton classification ...
List of Illustrations
Chapter 1
Figure 1.1. Spatial correlation (between point 45N, 35W and surrounding points...
Chapter 2
Figure 2.1. Simplified schematic view of a magma plumbing system feeding volca...
Figure 2.2. Schematic diagram of the two-magma reservoir model, modified after...
Figure 2.3. Ground deformation measured at Sierra Negra volcano, Ecuador. (Lef...
Figure 2.4. The step-by-step EnKF strategy implemented in this study. The brok...
Figure 2.5. The evolution of the overpressures after performing the state-para...
Figure 2.6. The EnKF-predicted uncertain parameters after performing the state...
Figure 2.7. Six of the 80 EnKF-estimated displacements after the state-paramet...
Figure 2.8. The estimated (A) uncertain parameters and (B) overpressures after...
Figure 2.9. Comparison between the EnKF-predicted and MCMC-predicted (A) uncer...
Figure 2.10. (Top) The overpressure evolution inside the shallow and deep magm...
Chapter 3
Figure 3.1. Typical elements of a polar ice-sheet model. The ice sheet interac...
Figure 3.2. Winter surface velocities for the Greenland ice sheet measured by ...
Figure 3.3. Assimilation results for surface velocity observations to reverse ...
Chapter 4
Figure 4.1. Simple example of seismic tomography. (a) Acquisition geometry and...
Figure 4.2. (a) Example of four-layer model. (b) Property in each of the four ...
Figure 4.3. Decomposition of a sonic log (blue) on a family of Haar wavelets (...
Figure 4.4. Voronoi tessellation.
Figure 4.5. Construction of the Johson Mehl tessellation over time (from Belha...
Figure 4.6. Representations of results including uncertainties for an example ...
Figure 4.7. (a) Example of a bimodal distribution (in blue) with two samplers ...
Figure 4.8. (a) Energies of the interacting chains according to the number of ...
Figure 4.9. (a) Distribution to be inferred. (b) A posteriori distribution obt...
Figure 4.10. (a) True velocity model and position of the 17 sources (white squ...
Figure 4.11. (a) Comparison of the evolution of the cost function according to...
Chapter 5
Figure 5.1. The inversion process: recovering descriptive quantities of the sc...
Figure 5.2. The different inversion approaches to modeling: “black box”-type i...
Figure 5.3. Different choices to be made for modeling: (ad) examples of elemen...
Figure 5.4. The RVoG model inversion: the description of vegetation as a rando...
Chapter 6
Figure 6.1. Contributions of radiation reaching a satellite sensor (LT OA) in ...
Figure 6.2. In situ observational data acquisition methods for calibration and...
Figure 6.3. Examples of data used for validating products originating from mul...
Figure 6.4. Detection of harmful algal bloom Nodularia spumigena in the Baltic...
Figure 6.5. Spatiotemporal monitoring of the Rhône river plume (France) during...
Figure 6.6A. (a) Airborne Lidar bathymetry at West Palm Beach. (b) Satellite b...
Figure 6.6B. (c) RGB composite color (Caballero and Stumpf 2019)
Figure 6.7. Bathymetric maps obtained from ETM/Landsat, Casi, Meris, Chris/Pro...
Figure 6.8. Regional Bathymetry (a) from an echosounder and (b) from Pleiades ...
Figure 6.9. a) Map of benthic habitats of the Pakri marine area obtained by su...
Figure 6.10A. Spatiotemporal dynamics between 2009 and 2015 of coral reef of a...
Figure 6.10B. Spatio-temporal dynamics between 2009 and 2015 of coral reef of ...
Chapter 7
Figure 7.1. Principle of the Cherenkov effect telescope, or Imaging Atmospheri...
Figure 7.2. (a) 0.5 TeV energy gamma event; (b) 27 GeV energy proton event
Figure 7.3. (a) Moment extraction from the ellipse produced by a gamma ray. (b...
Figure 7.4. Example of hard parameter sharing architecture. The encoder is sha...
Figure 7.5. Example of soft parameter sharing architecture
Figure 7.6. Attention mechanism as per squeeze-and-excitation channel. The par...
Figure 7.7. Dual attention mechanism combining spatial attention and per chann...
Figure 7.8. γ-PhysNet architecture for IACT image analysis
Figure 7.9. Multitasking block inspired by the physics of reconstruction. FC s...
Figure 7.10. Introduction of attention mechanisms into the γ-PhysNet encoder. ...
Figure 7.11. Energy resolution as a function of energy rebuilt in the LST1 ene...
Figure 7.12. Angular resolution according to the simulated energy in the LST1 ...
Figure 7.13. (a) Angular and (b) energy resolutions according to the energy re...
Figure 7.14. Behavior analysis of γ-PhysNet with and without attention for a r...
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Preface
Begin Reading
List of Authors
Index
WILEY END USER LICENSE AGREEMENT
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SCIENCES
Image, Field Director – Laure Blanc-Féraud
Remote Sensing Imagery,Subject Heads – Emmanuel Trouvé and Avik Bhattacharya
Inversion and Data Assimilation in Remote Sensing
Estimation of Geophysical Parameters
Coordinated by
Yajing Yan
First published 2024 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2024The rights of Yajing Yan to be identified as the author of this work have been asserted by her in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2024933649
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78945-142-9
ERC code:PE10 Earth System Science PE10_14 Earth observations from space/remote sensing
Preface
Yajing YAN
LISTIC, Université Savoie Mont-Blanc, Annecy, France
This book is part of the SCIENCES series by ISTE–Wiley and belongs to the Image field of the Engineering and Systems department. The field, Image, covers the entire processing chain, from acquisition to interpretation, by analyzing the data provided by various imaging systems. This field is split into seven subjects, including remote sensing imagery (RSI). Based on this subject, we propose a series of books that portray diverse and comprehensive topics in advanced remote sensing images and their applications for earth observation (EO). There has been an increasing demand for monitoring and predicting our planet’s evolution on a local, regional and global scale. Hence, airborne, space-borne and ground-based platforms with active and passive sensors have acquired images that measure several features at various spatial and temporal resolutions over the past few decades.
RSI has become a broad multidisciplinary domain, attracting scientists across the diverse fields of science and engineering. The books proposed in this RSI series aim to present state-of-the-art and available scientific knowledge about the primary sources of images acquired by optical and radar sensors. The books will cover the processing methods that are developed by the signal and image processing community to extract useful information for end-users for an extensive range of EO applications. Each RSI book focuses on general topics such as change detection, surface displacement measurement, target detection, model inversion and data assimilation.
This book addresses recent advances in remote sensing data assimilation and inversion. Besides a summary of the classical methods, it deals with a wide range of themes where assimilation and inversion techniques have been developed and/or adapted according to the domain specificity; that is, in terms of scientific objectives, characteristics of data and physical models. This book is organized into two parts: assimilation and inversion. Each part begins with a methodological chapter that presents and summarizes the most widely used methods and follows with chapters of various thematic applications. The assimilation part focuses on prediction problems by assimilation of in situ and remote sensing data in land surface monitoring, wildfire modeling, volcanology and glaciology, with the methodological barrier related to the specificity of each theme being highlighted and discussions on possible solutions to explore in order to better implement data assimilation techniques being opened. The inversion part includes biomass inversion with SAR images, bio-physical-hydrological inversion in coast zones with multi- and hyper-spectral images. Moreover, very recent advent in solving inverse problems by neural networks is showcased with illustrations in astrophysical applications.
This book is intended for students, engineers and researchers who want to gain broad knowledge of inversion and data assimilation in the domain of remote sensing imagery. This book has been written with a readership that is acquainted with applied mathematics, signal processing, remote sensing and geo-science. It should enable students, engineers and researchers in these communities to benefit from the methods and results presented in the book. This book is not dedicated to specific themes presented, but the references given will allow readers who want to delve into the subject to refer to other related works. For further in-depth mathematical tools in imaging inverse and optimization problems, readers can refer to “Mathematical tools for solving imaging inverse problems” (edited by P. Escande, D. Fortun and E. Soubies) and “Optimization for Imaging Sciences” (edited by E. Chouzenoux, J.C. Pesquet, N. Pustelnik and A. Repetti), two incoming books in the Image field.
This book represents some collective effort and contributions from a large number of authors. I hereby thank all contributors again. We would also like to thank Emmanuel Trouvé and Avik Bhattacharya who initiated and coordinated the book series “Remote Sensing Imagery” to which the current book belongs.
Yajing YANJune 2024
