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Advancing Earth Surface Representation via Enhanced Use of Earth Observations in Monitoring and Forecasting Applications
The representation of the Earth's surface in global monitoring and forecasting applications is moving towards capturing more of the relevant processes, while maintaining elevated computational efficiency and therefore a moderate complexity. These schemes are developed and continuously improved thanks to well instrumented field-sites that can observe coupled processes occurring at the surface–atmosphere interface (e.g., forest, grassland, cropland areas and diverse climate zones). Approaching global kilometer-scale resolutions, in situ observations alone cannot fulfil the modelling needs, and the use of satellite observation becomes essential to guide modelling innovation and to calibrate and validate new parameterization schemes that can support data assimilation applications. In this book, we review some of the recent contributions, highlighting how satellite data are used to inform Earth surface model development (vegetation state and seasonality, soil moisture conditions, surface temperature and turbulent fluxes, land-use change detection, agricultural indicators and irrigation) when moving towards global km-scale resolutions.
Change Detection and Image Time-Series Analysis 1
Change Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios an...
Satellites for Atmospheric Sciences 1
How can atmospheric variables such as temperature, wind, rain and ozone be measured by satellites? How are these measurements taken and what has been learned since the first measurements in the 1970s? What data are currently available and what data are expected in the future? The first volume of this encyclopedic book answers these questions by reporting the history of satellite meteorology and addresses how national and international agencies define coordinated programs to cover user needs. It also presents the principles of satellite remote sensing to deliver products suited to user requirements. This book is completed by a glossary and appendices with a list of supporting instruments already in use.
Satellites for Atmospheric Sciences 2
How can atmospheric variables such as temperature, wind, rain and ozone be measured by satellites? How are these measurements taken and what has been learned since the first measurements in the 1970s? What data are currently available and what data are expected in the future? The second volume of this encyclopedic book presents each field of application – meteorology, atmospheric composition and climate – with its main aims as well as the specific areas which can be addressed through the use of satellite remote sensing. This book presents the satellite products used for operational purposes as well as those that allow for the advancement of scientific knowledge. The instruments that are at their origin are described, as well as the processing, delivery times and the knowledge they provide. This book is completed by a glossary and appendices with a list of supporting instruments already in use.
Change Detection and Image Time Series Analysis 2
Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter ...
Methodologies For The Conception, Design And Application Of Soft Computing - Proceedings Of The 5th International Conference On Soft Computing And Information/intelligent Systems (In 2 Volumes)
Soft computing is the common name for a certain form of natural information processing that has its original form in biology, especially in the function of human brain. It is a discipline rooted in a group of technologies such as fuzzy logic, neural networks, chaos, genetic algorithms, probabilistic reasoning and learning algorithms. Today, soft computing has become an acknowledged concept; however, for a long time, such components of soft computing have been debated and individually developed.Since its beginning in 1990, the series of IIZUKA conferences has covered various kinds of technologies that constitute soft computing. This series has played a pioneering role in promoting the develop...
IGARSS 2003
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