batch7_satellite_ges
- /dataset contient un échantillon de données OCO-2 et des données d'inventaire ;
- /notebooks centralise les notebooks réalisés par l'équipe ;
- /pipeline comprend des scripts pour générer les données nécessaires.
General presentation
Our goal is to localize CO² emission on earth. We are working with :
- Matthieu Porte, from IGN who submit the projet
- Marie Heckmann, from the French Ministry of Ecology
- Frederic Chevalier, from IPSL (!!Link) ,one of the author of https://www.atmos-chem-phys-discuss.net/acp-2020-123/acp-2020-123.pdf
What we have as input
The OCO-2 Satellite (Orbiting Carbon Observatory) use spectrometers to detect CO² in atmosphere :
!! More info here : https://oco.jpl.nasa.gov/instrument/ So it can't see through clouds or fog. And don't work the same over ground or water.
The swath of the satellite is small : only 10km :
And the coverage is partial, no orbit are contiguous.
More info on the mission on https://earth.esa.int/web/eoportal/satellite-missions/o/oco-2.
So that's very limitative and frustrating. We don't have a high resolution images.
NASA made global CO² image :
But it is extrapolation of the data, it's not what the satellite really see.
What we want to do
- Detect peak in data by looking at CO² Plume
- Agregate known sources of CO²
- Compare peak to known sources to correct them
- Machine Learning to find new source
- Display the result on a comprehensive map
What we have achieved
- XXX Picture of CO² Plume
What is comming next
- Find nearest inventory from peak position, using the wind vector.
We need help
- Better peak detection: So far, we are fitting gaussian curves to detect relevant peaks. 2 issues:
- we use SciKit Learn curve_fit. Do you know a better algorithme or how to tune parameters of curve_fit ?
- we are looking at other methodologies to detect anomalies (our 'peaks') in the concentrations - any idea?
- Wind modeling to estimate emission from detected concentration - any idea? (inverting the gaussian plume model)
- Interactive dasboard to share our work on the web (Streamlit ?)