ESPEREL

Description

Improvements in satellite remote sensing of pollutants e.g. with the current Sentinel 5 Precursor (S5P), and future Sentinels 4 and 5, appear to be a game changer to better estimate pollutant emissions. Indeed, the unprecedented spatial and temporal coverage and resolution of these satellite observations, combined with data assimilation techniques, paves the way to provide corrected emissions of the inventories on a daily-to-hourly basis. One of the main challenges is to manage and exploit the large amount of data at high resolution provided by these satellites. The main objective of the ESPEREL porject is to explore new approaches based on deep learning to build a system able to better estimate pollutant emissions at high spatial and temporal resolution with a near-real-time processing capability.

This project is the result of a partnership with LISA and is funded by the DiiP.

People

Supervision team:

• Gaëlle Dufour (LISA)
• Sylvain Lobry
• Adriana Coman (LISA)
• Maxim Eremenko (LISA)

Research Engineer:

• Shen Liang