Abstract. Point sources account for a large portion of anthropogenic greenhouse gas (GHG) emissions. Timely detection, localization, and quantification of these emissions are critical for supporting carbon neutrality efforts. Spaceborne monitoring satellites can provide essential concentration data for identifying point sources. However, existing methods often require human intervention and typically detect plume masks instead of source locations, limiting their utility for regulatory applications. In this study, we present GHGPSE-Net, a deep learning method for greenhouse gas point source extraction. GHGPSE-Net simultaneously performs detection, localization, and quantification of emissions, eliminating the need for traditional segmentation steps. To train and evaluate the model, we construct synthetic datasets using an atmospheric transport model and validate its accuracy against radiosonde profiles and satellite observations. GHGPSE-Net demonstrates desirable performance in the simulation data across detection (F1-score of 0.96), subpixel-level localization and quantification (Pearson's correlation of 0.99, root mean square error of 89.9 tCO2 h−1), tested on ideal instrument of 0.5 km × 0.5 km resolution with retrieval noise of 1.5 parts per million (ppm). The results also demonstrate considerable generalization of the proposed model when tested using two independent datasets. On the identified sources from OCO-3 spaceborne observations, GHGPSE-Net achieves a detection precision of 0.89, localization accuracy of 3.02 km, and a Pearson's R of 0.59 for quantification. The proposed method and datasets provide a valuable foundation for future research towards rapid and automated GHG point source extraction, offering critical data to support swift responses to abnormal emission events.
Pang et al. (Fri,) studied this question.