24-336 Tracking Anthropogenic Co2 with Space-based
il y a 4 semaines
Doctorat, 36 mois
- Temps plein
- Aucune expérience exigée
- Maitrise, IEP, IUP, Bac+4
- Atmospheric Composition & Climate
**Mission**:
Tracking anthropogenic emissions of the two main greenhouse gases (GHGs) influenced by human activities, carbon dioxide (CO2) and methane (CH4), is crucial to assessing progress towards the objectives of the Paris Agreement signed in 2015. Complementing the observations made by the surface network, satellite measurements of near-infrared radiation now constitute one of the essential pillars of the global GHG observation system. Numerous satellite observation concepts dedicated to CO2 or CH4 monitoring are planned or under development for the coming decade.
After a decade that saw the emergence of space-based GHG observations using spectrometers operating in the near infrared with low spatial resolution (NASA's OCO-2 mission, JAXA's GOSAT or CNES's MicroCarb), GHG measurement from space has recently entered a new phase: that of measuring anthropogenic emissions using innovative spectro-imager concepts, combining high-resolution spectral and spatial measurements. We can cite 3 missions of particular interest to the French community: 1) the CO2M mission, the 7th Sentinel of the Copernicus program, scheduled for launch in 2025-2026, is a near-infrared imaging spectrometer that will measure CO2 columns at weekly frequency, with an accuracy of 0.7 ppm and a spatial resolution of 4 km² ; 2) the NanoCarb instrument, developed as part of the SCARBO project and whose validation campaigns are supported by CNES, is based on a miniaturized static Fabry-Perot interferometer which measures truncated interferograms at optical path differences that are optimally sensitive to CO2, with a resolution of 4 km2 over fields of view of 100 km2, for an accuracy comparable to CO2M ; 3) Finally, Absolute Sensing's GESat project aims to develop a constellation of mini-satellites for the detection and quantification of the main anthropogenic methane sources, with the launch of the first satellite based on a measurement similar to NanoCarb in 2024.
High spatial resolution (images of ~100 km2 with pixels of ~1 km2) makes it possible to study the emission plumes of individual source points; high spectral resolution provides the measurements needed to estimate CO2 or CH4 columns. Most of the studies carried out in preparation for these missions have focused on the precision achievable by these different concepts on the CO2 or CH4 column at the scale of each individual pixel. This enabled them to be compared with conventional spectrometer concepts such as OCO-2 or MicroCarb (e.g. Dogniaux et al., 2022, 2023). However, these studies overlooked an important aspect of these new measurements: the possibility of taking advantage of the combination of the two high resolutions, both spatial and spectral, through approaches that take into account the existing correlation between adjacent pixels. What's more, the sheer volume of data provided by these measurements means that the radiative transfer inversion codes classically used in ground segments need to be accelerated in order to interpret the measurement as a gas concentration.
The aim of this thesis will be to develop a new approach to the analysis of observations made by space-based spectro-imagers, taking advantage of the combination of two high resolutions, spatial and spectral. In particular, new deep learning methods have emerged to tackle this problem. One of the most powerful deep learning algorithms is based on the use of Convolutional Neural Networks (CNN). These programming models enable image recognition by automatically assigning to each input image a label corresponding to the class to which it belongs. They thus seem well suited to estimating greenhouse gas columns using spectral and spatial observations made by spectro-imagers. Other deep learning methods may also be considered.
After an algorithmic development phase, taking into account instrumental observation models and radiative transfer modeling (which links geophysical data to the spectral measurement performed), these algorithms will be compared with conventional approaches based on 1D inversion of radiative transfer using simulations of observations for different anthropogenic emission conditions. They will then be applied to spectro-imager concepts: 1) using data from past and planned airborne prototype measurement and validation campaigns; 2) using the first observations made by these satellites, scheduled for launch between 2024 and 2026. Finally, the accuracy of these new measurements in detecting and quantifying point sources of CO2 and CH4 emissions will be assessed, in order to enhance the relevance of international emissions inventories.
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**Profil**:
Earth sciences, computing sciences
**Laboratoire**:
LMD
**MESSAGE from Phd Team**:
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