Victor Pellet received a Engineer degree (i.e. M.Eng.) in signal processing and applied mathematics from the Grenoble Institute of Technology (Grenoble, France, 2015) and a Ph.D. degree in geoscience from Sorbonne University (Paris, France, 2018). Victor’s research focuses on geosciences with an emphasis on the use of applied mathematics and statistics. His Master’s thesis focused on the processing of hyperspectral satellite observations for retrieving information on geophysical variables, and his Ph.D. focused on the satellite monitoring of the water cycle. In the context of climate change, characterizing the water cycle is a key issue of the scientific community. His thesis was part of the European project WACMOS-MED, supported by ESA, and contributed to the international project HyMeX. The aim was to develop satellite products integration methods to optimize the water cycle monitoring (atmosphere, land and ocean) over the Mediterranean basin. His Postdoctoral researches (JSPS fellow @ The University of Tokyo, and CNES fellow @ Paris Observatory) focus on investigating the potential of satellite observations for a better description of water fluxes and stocks at finer spatio-temporal scales. He is currently involved in two ESA projects dealing with high resolution downscaling and physic-guided machine learning-based retrieval. He published eight articles on the subject of hyperspectral satellite observations processing, and eight on the analysis of the water cycle from both methodological and thematic point of view.
Terrestrial land surfaces are characterised by strong heterogeneities of, among other variables, soil texture, orography, land cover, snow, or Soil Moisture (SM). SM is of broad scientific interest due to its role in the Earth system: It impacts the partitioning of the incoming water and energy over land and affects then the variability of the terrestrial water and energy cycle. SM is also of capital practical value for a wide range of applications from floods forecasting to agriculture and water management.
The scientific community has made significant progress in estimating SM from satellite-based Earth Observations (EO). Harmonizing the SM retrievals from active and passive MW measurements from instruments that (i) operate at different wavelengths, polarisations and incidence angles; (ii) have diverging spatial, temporal and radiometric resolutions; and (iii) are hardly ever well collocated in space and time, is a true challenge. For a decade, ESA CCI SM project has released a Climate Data Record (CDR) of daily estimates at a 0.25° resolution, that relies on: 1) a physical-based inversion scheme to retrieve SM from passive MicroWave (MW), 2) a statistical retrieval for active MW, and 3) an a posteriori merging of these two products.
Several sources of improvement can however to be investigated for the retrieval techniques: the synergy of the observations in the retrieval phase and the spatial downscaling based on the data fusion with other satellite observations. These improvements should help improve and generate long-term and high-resolution SM products in particular by exploiting innovative machine learning methods.
In order to close the gap between Earth system research requirements and EO data, we therefore intend to investigate two topics:
We are now at a crossroad of opportunities: on the one hand, AI is becoming one of the most transformative technologies of the 21st century, while on the other hand, European EO capability is delivering a totally unique and comprehensive picture of the planet. This project intends to capitalize on this new context of satellite remote sensing for soil moisture.