The Greenhouse Gases Monitoring Instrument (GMI), on board the Chinese Gaofen-5 (GF-5) satellite, provides rich observation data for the global remote sensing of atmospheric CO₂. To meet the high-precision satellite retrieval needs of atmospheric CO₂, this paper designs a coupled bidirectional reflectance distribution function (BRDF) CO₂ retrieval (CBCR) method, which describes the surface reflectance characteristics by the BRDF, corrects for atmospheric scattering based on full physics retrieval theory, and ensures the stable retrieval of multiple parameters and atmospheric CO₂ by enriching prior constraints. Theoretical analysis shows that the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics is significantly related to the aerosol optical depth (AOD), solar zenith angle (SZA), and viewing zenith angle (VZA). The validation of GMI CO₂ retrievals shows that the CBCR method significantly reduced the influence of the surface bidirectional reflectance characteristics under high AOD and high SZA conditions, decreased the atmospheric CO₂ retrieval error from 0.58 ± 5.64 ppm to −1.33 ± 3.13 ppm, and increased the correlation with the temporal variation of actual atmospheric CO₂ from 34.7 to 76.8%. Our CBCR method can correct the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics on atmospheric CO₂ retrieval, and this work demonstrates that describing the surface reflectance characteristics by using BRDF is a promising idea in the field of satellite CO₂ retrievals.

The Greenhouse Gas Monitoring Instrument (GMI), carried by Gaofen 5 (GF5-01), and the Hyperspectral Observation Satellite (GF5-02) were successfully launched on 9 May 2018, and September 7, 2021, respectively, and are the only passive greenhouse gas payloads in China that can regularly obtain effective detection data in-orbit at this stage. Before launch, the research team carried out much laboratory calibration work and designed an on-board calibration system based on solar radiation sources which guarantees the quantitative accuracy of the payload data to the greatest extent. In order to more effectively meet the high frequency calibration requirements over the whole life cycle of the payload, the research team carried out research using the on-track site calibration method based on digital calibration field network technology, and the obtained calibration coefficient effectively complements the laboratory and on-board calibration results. The working principle of the GMI is quite different from that of a traditional imaging payload. Spatial heterodyne spectroscopy (SHS) is used to detect the absorption spectrum of greenhouse gases, has a large field of view and is non-imaging and hyperspectral. The existing fixed-site alternative calibration methods cannot fully meet the requirements of calibration tasks. In this paper, we propose a set of global digital calibration radiation field screening criteria that can meet the characteristics of the GMI and design a method to calculate the site calibration coefficients of non-absorption spectral channels according to the characteristics of hyperspectral data. Based on the historical observation data of the GMI, the initial calibration calculation of the payload launch was carried out, and the calibration results of four spectral channels of the GMI were obtained: The calibration coefficient range of the O₂ channel is 1.05–1.15, the mean value is 1.10 and the standard deviation is 2.72%; the calibration coefficient of the CO₂-1 channel is 1.05–1.13, the mean value is 1.09 and the standard deviation is 2.64%; the calibration coefficient of the CH₄ channel is 1.08–1.10, the mean value is 1.11 and the standard deviation is 2.73%; the calibration coefficient of the CO₂-2 channel is 1.09–1.14, the mean value is 1.12 and the standard deviation is 2.93%. The above results show that the radiation performance of each channel of the GMI shows no significant attenuation during this period, that the site calibration coefficient has no significant fluctuation and that the in-orbit operation state is stable.

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.