Satellite remote sensing is currently the best monitoring means to obtain global carbon source and sink data. The United States, Japan, China and other countries are vigorously developing spaceborne detection technology. However, the important factors that restrict the application of greenhouse gas satellite remote sensing technology include the limited accuracy of data products. How to improve the retrieval level of greenhouse gas payloads is a problem that needs to be solved urgently. One effective way to improve data quality is to carry out satellite ground synchronous authenticity verification and system error correction. This paper mainly aims at the shortcomings of the existing TCCON and the portable verification equipment EM27/SUN, and develops a High-Resolution Fourier Transform Spectrometer (HRFTS) based on dynamic collimation technology. Through the gas absorption method and the band scanning method of the hyperspectral monochromatic light source, the instrument’s absorption spectrum measurement capability and the Instrument Line Shape (ILS) are demonstrated. The instrument’s spectral resolution is consistent with the on-orbit greenhouse gas satellite load, reaching 0.26 cm⁻¹. For the interference data obtained by the spectrometer, spectral restoration processing, data quality control and inversion algorithm optimization were carried out to solve the problems of baseline correction, spectral fine registration, and environmental parameter profile reconstruction, and cross comparison experiments with EM27/SUN were carried out simultaneously. Finally, for the gases monitoring instrument (GMI) of the GF5-02 satellite launched on 7 September 2021, the first satellite ground synchronization verification experiment with high space-time matching was carried out. The results showed that the CO₂ column concentration deviation of the satellite ground synchronization inversion was about 1.5 ppm, and the CH₄ column concentration deviation was about 11.3 ppb, which verified the on-orbit detection accuracy of the GMI, and laid a foundation for the subsequent satellite inversion algorithm optimization and systematic error correction.

To cope with global climate change and monitor the concentration distribution of CO ₂ and CH ₄ in the troposphere, the Greenhouse Gases Monitoring Instrument (GMI) carried by GF5-02 satellite was successfully launched on September 7, 2021. GMI utilizes spatial heterodyne spectroscopy technique to acquire interferogram images. During the acquisition process, interferogram images are inevitably corrupted by various noises, such as defective pixels, baseline drifts and random noise, which will greatly degrade the quality of recovered spectra and further reduce the retrieval accuracy of greenhouse gases. In this paper, we model the mixed noise removal of interferogram images as an inverse problem and propose a novel optimization framework by integrating the robust principal component analysis (RPCA), unidirectional total variation (UTV) and penalized least squares (PLS) together. Specifically, the low-rankness of interference fringes and sparsity of defective pixels are both considered in the RPCA model, and the directionality of interference fringes are enhanced by the UTV model, while the PLS model guarantees the fidelity and smoothness of the fitting baselines. An efficient alternating direction method of multipliers procedure is designed to solve the proposed framework. A number of experiments were conducted to illustrate the superior performance of proposed framework in terms of visual quality and quantitative assessment, as compared with the state-of-the-art algorithms.