Edited by
Yajing Yan, Université Savoie Mont Blanc, France.
ISBN : 9781789451429
Publication Date : November 2024
Hardcover 250 pp
165.00 USD
Co-publisher
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.
Part 1. Data Assimilation.
1. Methods for Assimilation of Observations: Application to Numerical Weather Prediction, Olivier Talagrand.
2. Ensemble Data Assimilation in Volcanology, Mary Grace Bato, Virginie Pinel and Yajing Yan.
3. Data Assimilation in Glaciology, Fabien Gillet-Chaulet.
Part 2. Inversion.
4. Probabilistic Inversion Methods, Alexandrine Gesret.
5. Modeling Radar Backscattering from Forests: A First Step to Inversion, Elise Colin and Laetitia Thirion-Lefevre.
6. Radiative Transfer Model Inversion and Application to Coastal Observation, Touria Bajjouk, Audrey Minghelli, Malik Chami and Tristan Petit.
7. Deep-learning Analysis of Cherenkov Telescope Array Images, Mikaël Jacquemont, Thomas Vuillaume, Alexandre Benoit, Gilles Maurin and Patrick Lambert.
Yajing Yan is a lecturer at the LISTIC laboratory, Université Savoie Mont Blanc, France. Her research focuses on multi-temporal InSAR analysis, remote sensing data fusion and assimilation, and machine learning.
