Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Dear Colleagues,              

Modern geodesy aims to accurately measure and understand the topography of the Earth and its gravitational field. Exemplarily, time series of coordinates recorded with highly accurate GNSS receivers provide important information about plate tectonics, volcano deflation/inflation events, post-glacial rebound, glaciers, or earthquakes. Furthermore, one can detect particular transient signals within these observations, e.g., slow slip events and post-seismic transients. The latter are precursors of natural hazards (earthquakes, landslides, flooding, etc.) and need to be accurately modelled. Regarding climate changes and/or the increase of natural phenomena with potentially dramatic consequences on populations, studying geodynamics and geophysics processes with various geodetic time series will provide theoretical and technical support for the establishment and maintenance of global and regional reference frames, earthquake disaster monitoring, and other engineering and scientific research.

This Special Issue focuses on modelling the geodetic time series recorded by various instruments to monitor the aforementioned phenomena using modern technologies (e.g., GNSS, GRACE, InSAR, Terrestrial Laser Scanners (TLS)). We emphasize the recent advances in the detection of small amplitude transient signals, periodic signals, and long-term trends (e.g., seasonal signals, tectonic rate, etc.) that are contaminated by various types of noise (i.e., stochastic processes, correlations). We especially welcome contributions focusing on modern algorithms and statistical estimators (e.g., MLE, wavelet, machine learning, and data fusion) highlighting the recent progress in statistics applied to environmental geodesy.

Potential topics include, but are not limited to, the following:

1. Geodetic Time Series Analysis: Stochastic Noise Modelling, Functional Model Fitting, Transient Signal Detection, Spatiotemporal Filtering.
2. Application of Machine Learning on Geodetic Time Series Analysis.
3. Tectonic Activity Inferred from GNSS Velocity Field.
4. Crustal Deformation Pattern Detection by Integration of GNSS, TLS, Tide Gauge and InSAR Techniques, Including Stochastic Modelling.
5. Seasonal Hydrological/Environment Loading with GNSS, GRACE, and Hydrological Model.
6. Dynamic Sea Level Changes and Coastal Disaster Early Warning Based on Earth Observations (GNSS, Tide Gauge, SSH).
7. Strong Convective Weather (GNSS-Derived Troposphere Variation Characteristics).

Dr. Xiaoxing He
Dr. Jean-Philippe Montillet
Dr. Zhao Li
Dr. Gaël Kermarrec
Dr. Rui Fernandes
Dr. Feng Zhou
Guest Editors

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