Methane, a powerful greenhouse gas, significantly influences climate change. Estimating methane emission fluxes is critical for implementing emission reduction strategies, and plumes segmentation is an important technique for accurate quantification. However, existing methane plumes segmentation studies focus on pixel-level localization of spatial and spectral features, which makes it difficult to capture global contextual information of remote sensing images. To tackle these challenges, we propose a novel Frequency and Spatial Domain Injection Network (FSDINet) for methane plumes segmentation. FSDINet introduces three blocks that process spectral features utilizing the frequency domain to capture global contextual information and supplement local information with pixel values in the spatial domain, therefore allowing for precise localization and segmentation. We conduct extensive experiments on hyperspectral and multispectral datasets. On the AVIRIS-NG hyperspectral dataset, the majority of our model evaluation scores exceed those of existing methods in a range of conditions, including both strong and weak plumes, with the highest Precision and IOU of 87.75% and 65.56%, respectively. On the WorldView-3 multispectral dataset, Precision and IOU improved up to 8.76% and 4.04% over methods utilizing spatial and spectral features. Furthermore, the zero-shot experiments on the EMIT dataset confirm the robust generalization capacity of our model when confronted with different sensor data.

Coal-fired power plants, as major anthropogenic CO₂ emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO₂ emissions from these point sources is crucial, as it will aid in quantifying CO₂ emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO₂ diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO₂ simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO₂ diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO₂ diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R² (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO₂ point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO₂ point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO₂ concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO₂ emissions using top-down approaches.

Hyperspectral imaging for satellites is currently an important tool for global monitoring of methane point sources, and it can be used to retrieve methane concentration to enable source location and “top-down” emission estimation. The matched filter (MF) is the main method used to retrieve methane enhancement from hyperspectral imaging. However, many false positive retrievals occur over heterogeneous land surfaces because of the confusion between methane absorption and surface reflectance spectra. This hinders the accurate quantification of methane point source emissions from hyperspectral imaging. To overcome this hindrance, we present an improved matched filter that includes background filtering to mitigate the reflectance spectra mismatch between the target and background. By analyzing the land cover shortwave-infrared spectral library, we found that wideband spectral slopes can be used to distinguish between surface types. Based on this, we designed the background sample filtering process and verified its performance using simulation and the advanced hyperspectral imager data. The results show that the improved matched filter can effectively reduce false retrievals over heterogeneous land surfaces and obtain a more realistic methane plume. For example, near an emission source with a $\Delta$ XCH $_{4}$ of 1000 ppb, the simulated retrieval bias can be less than 1.3% using a 1% filter threshold. Our method can enhance the ability of satellites to quantify methane concentrations on complex land surfaces.

The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant potential to enhance the efficiency and effectiveness of data processing in the GST. This paper develops a method for detecting carbon dioxide (CO₂) emission hotspots using a convolutional neural network (CNN) with short-lived and co-emitted nitrogen dioxide (NO₂) as a proxy. To address the data gaps in model parameter training, we constructed a dataset comprising over 210,000 samples of NO₂ plumes and emissions based on atmospheric dispersion models. The trained model performed well on the test set, with most samples achieving an identification accuracy above 80% and more than half exceeding 94%. The trained model was also applied to the NO₂ column data from the TROPOspheric Monitoring Instrument (TROPOMI) for hotspot detection, and the detections were compared with the MEIC inventory. The results demonstrate that in high-emission areas, the proposed method successfully identifies emission hotspots with an average accuracy of over 80%, showing a high degree of consistency with the emission inventory. In areas with multiple observations from TROPOMI, we observed a high degree of consistency between high NO₂ emission areas and high CO₂ emission areas from the Global Open-Source Data Inventory for Anthropogenic CO₂ (ODIAC), indicating that high NO₂ emission hotspots can also indicate CO₂ emission hotspots. In the future, as hyperspectral and high spatial resolution remote sensing data for CO₂ and NO₂ continue to grow, our methods will play an increasingly important role in global data preprocessing and global emission estimation.

Ozone absorbs ultraviolet radiation, which has a significant impact on research in astrobiology and other fields in that investigate the middle and upper atmosphere. A retrieval algorithm for ozone profiles in the middle and upper atmosphere was developed using the spectral data from the TROPOspheric Monitoring Instrument (TROPOMI). A priori ozone profiles were constructed through the Goddard Earth Observing System-Chem (GEOS-Chem) model. These profiles were closer to the true atmosphere in the spatial and temporal dimensions when compared to the ozone climatology. The TpO3 ozone climatology was used as a reference to highlight the reliability of the a priori ozone profile from GEOS-Chem. The inversion results based on GEOS-Chem and TpO3 climatology were compared with ground-based ozone measurements and the satellite products of the Microwave Limb Sounder (MLS) and the Ozone Mapping and Profiles Suite_Limb Profile (OMPS_LP). The comparisons reveal that the correlation coefficient R values for the inversion results based on GEOS-Chem were greater than 0.90 at most altitudes, making them better than the values based on TpO3 climatology. The differences in subcolumn concentration between the GEOS-Chem inversion results and the ground-based measurements were smaller than those between TpO3 climatology results and the ground-based measurements. The relative differences between the inversion results based on the GEOS-Chem and the satellite products was generally smaller than those between the inversion results based on TpO3 climatology and the satellite products. The mean relative difference between the GEOS-Chem inversion results and MLS is −9.10%, and OMPS_LP is 1.46%, while those based on TpO3 climatology is −14.51% and −4.70% from 20 to 45 km These results imply that using a priori ozone profiles generated through GEOS-Chem leads to more accurate inversion results.