The Greenhouse Gas Monitoring Instrument (GMI) is a short-wavelength infrared (SWIR) hyperspectral-resolution spectrometer onboard the Chinese satellite GaoFen-5 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The spectral quality and greenhouse gas (GHG) inversion ability of the Level 1 products are analyzed, and the results illustrate that the specifications meet the mission's requirements. An initial evaluation of the resolution, signal-to-noise ratio (SNR), and stability of the radiance spectrum reveals that the overall function and performance are within the design objectives. A comparison between our Level 1 products and the theoretical spectrum shows that the root mean square (rms) of the residual is approximately 0.8%, and the Level 1 products of the GMI captured within five months after observations have good spectral stability characteristics (less than 0.005 cm⁻¹ for Band 1, 0.003 cm⁻¹ for Band 2, 0.002 cm⁻¹ for Band 3, and 0.004 cm⁻¹ for Band 4). These results demonstrate that the GMI payload and the processing algorithm all work well and reliably. Furthermore, based on the Level 1 products, a GHG retrieval experiment is carried out, and the results are compared with data from Total Column Carbon Observing Network (TCCON) stations. The initial comparison of the XCO₂ results yields a value of 0.869 for R² (goodness of fit), 0.51 ppm for bias (mean of absolute error), and 0.53 ppm for σ (standard deviation of error). Similarly, the XCH₄ comparison yields values of 0.841 for R², 4.64 ppb for bias, and 4.66 ppb for σ.

Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance.

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 gases Monitoring Instrument on GaoFen-5 satellite-II (GMI-II) uses spatial heterodyne spectroscopy (SHS) for quantitative monitoring of atmospheric greenhouse gases (GHG). Unlike the traditional SHS, the interferometer component of the GMI-II was designed with zero optical path difference offset, effectively improving spectral resolution while maintaining the same detector specifications. The secondary imaging system with non-isometric scaling of spatial and spectral dimensions was designed to decrease the integration time of a frame image or improve the spectral signal-to-noise ratio (SNR) under the same integration time. This paper introduces the design, manufacture, adjustment methods, and test results of the main performance indexes of the GMI-II that indicate that the spectral resolution of the O₂ A-band detection channel is better than 0.6 cm⁻¹ and other channels are better than 0.27 cm⁻¹. Under the typical radiance of other carbon monitors’ on-orbit statistics, the spectral SNR of the GMI-II is more than 300. These test results demonstrate that the GMI-II can be well adapted to quantitative remote sensing monitoring of atmospheric GHG.

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.