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Data Assimilation for the Geosciences

Data Assimilation for the Geosciences
From Theory to Application

by Steven James Fletcher

  • Publisher : Elsevier
  • Release : 2017-03-10
  • Pages : 976
  • ISBN : 0128044845
  • Language : En, Es, Fr & De
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Data Assimilation for the Geosciences: From Theory to Application brings together all of the mathematical,statistical, and probability background knowledge needed to formulate data assimilation systems in one place. It includes practical exercises for understanding theoretical formulation and presents some aspects of coding the theory with a toy problem. The book also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to the atmosphere, oceans, as well as the land surface and other geophysical situations. It offers a comprehensive presentation of the subject, from basic principles to advanced methods, such as Particle Filters and Markov-Chain Monte-Carlo methods. Additionally, Data Assimilation for the Geosciences: From Theory to Application covers the applications of data assimilation techniques in various disciplines of the geosciences, making the book useful to students, teachers, and research scientists. Includes practical exercises, enabling readers to apply concepts in a theoretical formulation Offers explanations for how to code certain parts of the theory Presents a step-by-step guide on how, and why, data assimilation works and can be used

Advanced Data Assimilation for Geosciences

Advanced Data Assimilation for Geosciences
Lecture Notes of the Les Houches School of Physics : Special Issue, June 2012

by Eric Blayo

  • Publisher : Oxford University Press, USA
  • Release : 2014-10-30
  • Pages : 576
  • ISBN : 0198723849
  • Language : En, Es, Fr & De
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In many applications of geophysics (weather forecast, study of climate evolution and variability), it is necessary to get the best possible estimate of the state of the system under study. In general, information about this system comes from observations and numerical models. However, none of these sources is perfect. Data assimilation designates the set of mathematical methods used to optimally combine observations with models, to fulfil the need of an accurateestimate of the system state. Because of the weather forecast problem in particular, the geophysical sciences have shaped a long history and a strong background on data assimilation, particularly with big and complex systems such as the atmosphere and the ocean. This book gathers notes from lecturesgiven during a three-week summer school on the fundamentals and the most recent developments of geophysical data assimilation.

Exam Prep for: Data Assimilation for the Geosciences

Exam Prep for: Data Assimilation for the Geosciences
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Data Assimilation: Methods, Algorithms, and Applications

Data Assimilation: Methods, Algorithms, and Applications

by Mark Asch,Marc Bocquet,Maelle Nodet

  • Publisher : SIAM
  • Release : 2016-12-29
  • Pages : 306
  • ISBN : 1611974542
  • Language : En, Es, Fr & De
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Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing ?why? and not just ?how.? Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.

Data Assimilation

Data Assimilation
A Mathematical Introduction

by Kody Law,Andrew Stuart,Konstantinos Zygalakis

  • Publisher : Springer
  • Release : 2015-09-05
  • Pages : 242
  • ISBN : 3319203258
  • Language : En, Es, Fr & De
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This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. The numerous examples and illustrations make understanding of the theoretical underpinnings of data assimilation accessible. Furthermore, the examples, exercises and MATLAB software, make the book suitable for students in applied mathematics, either through a lecture course, or through self-study.

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications
A Book

by Seon Ki Park,Liang Xu

  • Publisher : Springer
  • Release : 2016-12-26
  • Pages : 553
  • ISBN : 3319434152
  • Language : En, Es, Fr & De
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This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.

Nonlinear Data Assimilation

Nonlinear Data Assimilation
A Book

by Peter Jan Van Leeuwen,Yuan Cheng,Sebastian Reich

  • Publisher : Springer
  • Release : 2015-07-22
  • Pages : 118
  • ISBN : 3319183478
  • Language : En, Es, Fr & De
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This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.

Data Assimilation

Data Assimilation
The Ensemble Kalman Filter

by Geir Evensen

  • Publisher : Springer Science & Business Media
  • Release : 2006-12-22
  • Pages : 280
  • ISBN : 3540383018
  • Language : En, Es, Fr & De
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This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.

Atmospheric Modeling, Data Assimilation and Predictability

Atmospheric Modeling, Data Assimilation and Predictability
A Book

by Eugenia Kalnay,Kalnay Eugenia

  • Publisher : Cambridge University Press
  • Release : 2003
  • Pages : 341
  • ISBN : 9780521796293
  • Language : En, Es, Fr & De
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This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.

Semi-Lagrangian Advection Methods and Their Applications in Geoscience

Semi-Lagrangian Advection Methods and Their Applications in Geoscience
A Book

by Steven J. Fletcher

  • Publisher : Elsevier
  • Release : 2019-11-18
  • Pages : 624
  • ISBN : 0128172231
  • Language : En, Es, Fr & De
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Semi-Lagrangian Advection Methods and Their Applications in Geoscience provides a much-needed resource on semi-Lagrangian theory, methods, and applications. Covering a variety of applications, the book brings together developments of the semi-Lagrangian in one place and offers a comparison of semi-Lagrangian methods with Eulerian-based approaches. It also includes a chapter dedicated to difficulties of dealing with the adjoint of semi-Lagrangian methods and illustrates the behavior of different schemes for different applications. This allows for a better understanding of which schemes are most efficient, stable, consistent, and likely to introduce the minimum model error into a given problem. Beneficial for students learning about numerical approximations to advection, researchers applying these techniques to geoscientific modeling, and practitioners looking for the best approach for modeling, Semi-Lagrangian Advection Methods and Their Applications in Geoscience fills a crucial gap in numerical modeling and data assimilation in geoscience. Provides a single resource for understanding semi-Lagrangian methods and what is involved in its application Includes exercises and codes to supplement learning and create opportunities for practice Includes coverage of adjoints, examining the advantages and disadvantages of different approaches in multiple coordinate systems and different discretizations Includes links to numerical datasets and animations to further enhance understanding

Probabilistic Forecasting and Bayesian Data Assimilation

Probabilistic Forecasting and Bayesian Data Assimilation
A Book

by Sebastian Reich,Colin Cotter

  • Publisher : Cambridge University Press
  • Release : 2015-05-14
  • Pages : 306
  • ISBN : 1107069394
  • Language : En, Es, Fr & De
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Covers key ideas and concepts. Ideal introduction for graduate students in any field where Bayesian data assimilation is applied.

Nonlinear Systems

Nonlinear Systems
Design, Analysis, Estimation and Control

by Dongbin Lee,Christos Volos,Timothy Burg

  • Publisher : BoD – Books on Demand
  • Release : 2016-10-19
  • Pages : 364
  • ISBN : 9535127144
  • Language : En, Es, Fr & De
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The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter 14. Chapter 1 and Chapter 2 treat design techniques based on linearization of nonlinear systems. An analysis of nonlinear system over quantum mechanics is discussed in Chapter 3. Chapter 4 to Chapter 7 are estimation methods using Kalman filtering while solving nonlinear control systems using iterative approach. Optimal approaches are discussed in Chapter 8 with retarded control of nonlinear system in singular situation, and Chapter 9 extends optimal theory to H-infinity control for a nonlinear control system.Chapters 10 and 11 present the control of nonlinear dynamic systems, twin-rotor helicopter and 3D crane system, which are both underactuated, cascaded dynamic systems. Chapter 12 applies controls to antisynchronization/synchronization in the chaotic models based on Lyapunov exponent theorem, and Chapter 13 discusses developed stability analytic approaches in terms of Lyapunov stability. The analysis of economic activities, especially the relationship between stock return and economic growth, is presented in Chapter 14.

Dynamic Data Assimilation

Dynamic Data Assimilation
A Least Squares Approach

by John M. Lewis,S. Lakshmivarahan,Sudarshan Dhall

  • Publisher : Cambridge University Press
  • Release : 2006-08-03
  • Pages : 654
  • ISBN : 0521851556
  • Language : En, Es, Fr & De
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Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints.

Four-Dimensional Model Assimilation of Data

Four-Dimensional Model Assimilation of Data
A Strategy for the Earth System Sciences

by National Research Council,Division on Earth and Life Studies,Commission on Geosciences, Environment and Resources,Panel on Model-Assimilated Data Sets for Atmospheric and Oceanic Research

  • Publisher : National Academies Press
  • Release : 1991-02-01
  • Pages : 88
  • ISBN : 0309045363
  • Language : En, Es, Fr & De
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This volume explores and evaluates the development, multiple applications, and usefulness of four-dimensional (space and time) model assimilations of data in the atmospheric and oceanographic sciences and projects their applicability to the earth sciences as a whole. Using the predictive power of geophysical laws incorporated in the general circulation model to produce a background field for comparison with incoming raw observations, the model assimilation process synthesizes diverse, temporarily inconsistent, and spatially incomplete observations from worldwide land, sea, and space data acquisition systems into a coherent representation of an evolving earth system. The book concludes that this subdiscipline is fundamental to the geophysical sciences and presents a basic strategy to extend the application of this subdiscipline to the earth sciences as a whole.

Geoscience Data and Collections

Geoscience Data and Collections
National Resources in Peril

by National Research Council,Division on Earth and Life Studies,Board on Earth Sciences and Resources,Committee on Earth Resources,Committee on the Preservation of Geoscience Data and Collections

  • Publisher : National Academies Press
  • Release : 2002-09-23
  • Pages : 124
  • ISBN : 9780309169660
  • Language : En, Es, Fr & De
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Geoscience data and collections (such as, rock and sediment cores, geophysical data, engineering records, and fossils) are necessary for industries to discover and develop domestic natural resources to fulfill the nation’s energy and mineral requirements and to improve the prediction of immediate and long term hazards, such as land slides, volcanic eruptions and global climate change. While the nation has assembled a wealth of geoscience data and collections, their utility remains incompletely tapped. Many could act as invaluable resources in the future but immediate action is needed if they are to remain available. Housing of and access to geoscience data and collections have become critical issues for industry, federal and state agencies, museums, and universities. Many resources are in imminent danger of being lost through mismanagement, neglect, or disposal. A striking 46 percent of the state geological surveys polled by the committee reported that there is no space available or they have refused to accept new material. In order to address these challenges, Geoscience Data and Collections offers a comprehensive strategy for managing geoscience data and collections in the United States.

Assimilation of Remote Sensing Data into Earth System Models

Assimilation of Remote Sensing Data into Earth System Models
A Book

by Jean-Christophe Calvet,Patricia De Rosnay,Stephen G. Penny

  • Publisher : MDPI
  • Release : 2019-11-20
  • Pages : 236
  • ISBN : 3039216406
  • Language : En, Es, Fr & De
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In the Earth sciences, a transition is currently occurring in multiple fields towards an integrated Earth system approach, with applications including numerical weather prediction, hydrological forecasting, climate impact studies, ocean dynamics estimation and monitoring, and carbon cycle monitoring. These approaches rely on coupled modeling techniques using Earth system models that account for an increased level of complexity of the processes and interactions between atmosphere, ocean, sea ice, and terrestrial surfaces. A crucial component of Earth system approaches is the development of coupled data assimilation of satellite observations to ensure consistent initialization at the interface between the different subsystems. Going towards strongly coupled data assimilation involving all Earth system components is a subject of active research. A lot of progress is being made in the ocean–atmosphere domain, but also over land. As atmospheric models now tend to address subkilometric scales, assimilating high spatial resolution satellite data in the land surface models used in atmospheric models is critical. This evolution is also challenging for hydrological modeling. This book gathers papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation.

Land Surface Observation, Modeling and Data Assimilation

Land Surface Observation, Modeling and Data Assimilation
A Book

by Shunlin Liang,Xin Li,Xianhong Xie

  • Publisher : World Scientific
  • Release : 2013-09-23
  • Pages : 492
  • ISBN : 981447262X
  • Language : En, Es, Fr & De
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This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are active research scientists, and many of them are internationally known leading experts in their areas, ensuring that the text is authoritative. This book comprises four parts that are logically connected from data, modeling, data assimilation integrating data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation. Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today's earth science and modeling communities. Contents:Observation:Remote Sensing Data Products for Land Surface Data Assimilation System Application (Yunjun Yao, Shunlin Liang and Tongren Xu)Second-Generation Polar-Orbiting Meteorological Satellites of China: The Fengyun 3 Series and Its Applications in Global Monitoring (Peng Zhang)NASA Satellite and Model Land Data Services: Data Access Tutorial (Suhung Shen, Gregory Leptoukh and Hongliang Fang)Modeling:Land Surface Process Study and Modeling in Drylands and High-Elevation Regions (Yingying Chen and Kun Yang)Review of Parameterization and Parameter Estimation for Hydrologic Models (Soroosh Sorooshian and Wei Chu)Data Assimilation:Assimilating Remote Sensing Data into Land Surface Models: Theory and Methods (Xin Li and Yulong Bai)Estimating Model and Observation Error Covariance Information for Land Data Assimilation Systems (Wade T Crow)Inflation Adjustment on Error Covariance Matrices for Ensemble Kalman Filter Assimilation (Xiaogu Zheng, Guocan Wu, Xiao Liang and Shupeng Zhang)A Review of Error Estimation in Land Data Assimilation Systems (Yulong Bai, Xin Li and Qianlong Chai)An Introduction to Multi-scale Kalman Smoother-Based Framework and Its Application to Data Assimilation (Daniel E Salas and Xu Liang)Application:Overview of the North American Land Data Assimilation System (NLDAS) (Youlong Xia, Brian A Cosgrove, Michael B Ek, Justin Sheffield, Lifeng Luo, Eric F Wood, Kingtse Mo and the NLDAS team)Soil Moisture Data Assimilation for State Initialization of Seasonal Climate Prediction (Wenge Ni-Meister)Assimilation of Remote Sensing Data and Crop Simulation Models for Agricultural Study: Recent Advances and Future Directions (Hongliang Fang, Shunlin Liang and Gerrit Hoogenboom)Simultaneous State-Parameter Estimation for Hydrologic Modeling Using Ensemble Kalman Filter (Xianhong Xie) Readership: Graduate students and scientists in remote sensing, hydrology, ecology, environment and other earth sciences. Keywords:Data Assimilation;Uncertainties;Land Surface Processes;Satellite Data;Dynamic ModelsKey Features:The contribution authors are a group of leading experts international in those areasIt elaborates on the state-of-the-art land data assimilation, from theoretical derivations to current application problemsIt provides the latest development of satellite data and products, and presents novel applications of data assimilation for water cycle, crop monitoring and yield estimation

Data Assimilation

Data Assimilation
Making Sense of Observations

by William Lahoz,Boris Khattatov,Richard Menard

  • Publisher : Springer Science & Business Media
  • Release : 2010-07-23
  • Pages : 718
  • ISBN : 9783540747031
  • Language : En, Es, Fr & De
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Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).

Large Scale Inverse Problems

Large Scale Inverse Problems
Computational Methods and Applications in the Earth Sciences

by Mike Cullen,Melina A Freitag,Stefan Kindermann,Robert Scheichl

  • Publisher : Walter de Gruyter
  • Release : 2013-08-29
  • Pages : 212
  • ISBN : 3110282267
  • Language : En, Es, Fr & De
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This book is thesecond volume of a three volume series recording the "Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment" that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basic deterministic technique is to fit a model trajectory to the observations over a period of time to within the observation error. Since the model is not perfect the model trajectory has to be corrected, which defines the data assimilation problem. The stochastic view can be expressed by using an ensemble of model trajectories, and calculating corrections to both the mean value and the spread which allow the observations to be fitted by each ensemble member. In other areas of earth science, only the structure of the model formulation itself is known and the aim is to use the past observation history to determine the unknown model parameters. The book records the achievements of Workshop2 "Large-Scale Inverse Problems and Applications in the Earth Sciences". Itinvolves experts in the theory of inverse problems together with experts working on both theoretical and practical aspects of the techniques by which large inverse problems arise in the earth sciences.

Advanced Remote Sensing

Advanced Remote Sensing
Terrestrial Information Extraction and Applications

by Shunlin Liang,Xiaowen Li,Jindi Wang

  • Publisher : Academic Press
  • Release : 2012-12-06
  • Pages : 800
  • ISBN : 0123859557
  • Language : En, Es, Fr & De
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Advanced Remote Sensing is an application-based reference that provides a single source of mathematical concepts necessary for remote sensing data gathering and assimilation. It presents state-of-the-art techniques for estimating land surface variables from a variety of data types, including optical sensors such as RADAR and LIDAR. Scientists in a number of different fields including geography, geology, atmospheric science, environmental science, planetary science and ecology will have access to critically-important data extraction techniques and their virtually unlimited applications. While rigorous enough for the most experienced of scientists, the techniques are well designed and integrated, making the book’s content intuitive, clearly presented, and practical in its implementation. Comprehensive overview of various practical methods and algorithms Detailed description of the principles and procedures of the state-of-the-art algorithms Real-world case studies open several chapters More than 500 full-color figures and tables Edited by top remote sensing experts with contributions from authors across the geosciences