Preprints
https://doi.org/10.5194/gmd-2022-288
https://doi.org/10.5194/gmd-2022-288
Submitted as: model description paper
12 Dec 2022
Submitted as: model description paper | 12 Dec 2022
Status: this preprint is currently under review for the journal GMD.

Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants

Joffrey Dumont Le Brazidec1, Pierre Vanderbecken1, Alban Farchi1, Marc Bocquet1, Jinghui Lian2,3, Grégoire Broquet2, Gerrit Kuhlmann4, Alexandre Danjou2, and Thomas Lauvaux2 Joffrey Dumont Le Brazidec et al.
  • 1CEREA, École des Ponts and EDF R&D, Île-de-France, France
  • 2Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
  • 3Origins.S.A.S, Suez Group, Île-de-France, France
  • 4Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switerzland

Abstract. Under the Copernicus programme, an operational CO2 monitoring system (CO2MVS) is being developed and will exploit data from future satellites monitoring the amount of CO2 within the atmosphere. Methods for estimating CO2 emissions from significant local emitters (hotspots, i.e. cities or power plants) can greatly benefit from the availability of such satellite images, displaying atmospheric plumes of CO2. Indeed, local emissions are strongly correlated to the size, shape and concentrations distribution of the corresponding plume, the visible consequence of the emission. The estimation of emissions from a given source can therefore directly benefit from the detection of its associated plumes in the satellite image.

In this study, we address the problem of plume segmentation, i.e. the problem of finding all pixels in an image that constitute a city or power plant plume. This represents a significant challenge, as the signal from CO2 plumes induced by emissions from cities or power plants is inherently difficult to detect since it rarely exceeds values of a few ppm and is perturbed by variable regional CO2 background signals and observation errors. To address this key issue, we investigate the potential of deep learning methods and in particular convolutional neural networks to learn to distinguish plume-specific spatial features from background or instrument features. Specifically, a U-net algorithm, an image-to-image convolutional neural network, with a state-of-the-art encoder, is used to transform an XCO2 field into an image representing the positions of the targeted plume. Our models are trained on hourly 1 km simulated XCO2 fields in the regions of Paris, Berlin and several German power plants. Each field represents the plume of the hotspot, the background consisting of the signal of anthropogenic and biogenic CO2 surface fluxes near or far from the targeted source and the simulated satellite observation errors.

The performance of the deep learning method is thereafter evaluated and compared with a plume segmentation technique based on thresholding in two contexts: the first where the model is trained and tested on data from the same region, and the second where the model is trained and tested in two different regions. In both contexts, our method outperforms the usual segmentation technique based on thresholding and demonstrates its ability to generalise in various cases: city plumes, power plant plumes, and areas with multiple plumes. Although less accurate than in the first context, the ability of the algorithm to extrapolate on new geographical data is conclusive, paving the way to a promising universal segmentation model, trained on a well-chosen sample of power plants and cities, and able to detect the majority of the plumes from all of them. Finally, the highly accurate results for segmentation suggest a significant potential of convolutional neural networks for estimating local emissions from spaceborne imagery.

Joffrey Dumont Le Brazidec et al.

Status: open (until 06 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-288', Anonymous Referee #1, 09 Jan 2023 reply
  • CEC1: 'Comment on gmd-2022-288', Juan Antonio Añel, 15 Jan 2023 reply
    • AC1: 'Reply on CEC1', Joffrey Dumont Le Brazidec, 16 Jan 2023 reply
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 16 Jan 2023 reply
        • AC2: 'Reply on CEC2', Joffrey Dumont Le Brazidec, 16 Jan 2023 reply

Joffrey Dumont Le Brazidec et al.

Joffrey Dumont Le Brazidec et al.

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Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.