Greenhouse gas satellites can provide consistently global CO₂ data which are important inputs for the top-down inverse estimation of CO₂ emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO₂ emissions monitoring and verification support capacity drive the payload design of future CO₂ satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO₂ plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO₂ precision under different meteorological conditions. The simulations of CO₂ plumes indicate that the enhanced spatial resolution and XCO₂ precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO₂/yr. The satellite-detected maximum of XCO₂ enhancement strongly varies with the wind condition. For a satellite with a XCO₂ precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO₂/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO₂/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO₂ measurements and the hypothesized CO₂ emissions, their cumulative contribution to the overall accuracy of the satellite’s ability to identify realistic enhancement in XCO₂ are investigated in the future. The uncertainties of ΔXCO₂ caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO₂/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO₂ uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km² and 2 × 2 km² pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO₂ emission on this wind direction, because there is more rapid growth for simulated maximal XCO₂ enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO₂ precision higher than 0.7 ppm are suggested, because the CO₂ emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO₂ satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO₂ emissions.

Multi-angle polarization measurement is an important technical means of satellite remote sensing applied to aerosol monitoring. By adding angle information and polarization measurements, aerosol optical and microphysical properties can be more comprehensively and accurately retrieved. The accuracy of aerosol retrieval can reflect the advantages and specific accuracy improvement of multi-angle polarization. In this study, the Multi-angle Imaging SpectroRadiometer (MISR) V23 aerosol products and the Polarization and Directionality of the Earth’s Reflectance (POLDER) GRASP “high-precision” archive were evaluated with the Aerosol Robotic Network (AERONET) observations over China. Validation of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD), and the Ångström exponent (AE) properties was conducted. Our results show that the AOD inversion accuracy of POLDER-3/GRASP is higher with the correlation coefficient (R) of 0.902, slope of 0.896, root mean square error (RMSE) of 0.264, mean absolute error (MAE) of 0.190, and about 40.71% of retrievals within the expected error (EE, $\pm \left(0.05+0.2\times AO{D}_{AERONET}\right)$) lines. For AAOD, the performance of two products is poor, with better results for POLDER-3/GRASP data. POLDER-3/GRASP AE also has higher R of 0.661 compared with that of MISR AE (0.334). According to the validation results, spatiotemporal distribution, and comparison with other traditional scalar satellite data, the performance of multi-angle polarization observations is better and is suitable for the retrieval of aerosol properties.

Aerosol optical depth (AOD), fine-mode fraction (FMF), and absorption aerosol optical depth (AAOD) are essential for quantifying aerosol extinction and related climate and air-quality effects. Yet, most satellite retrievals target a single wavelength or parameter. In this study, a deep neural network (DNN) framework was developed to synergistically retrieve AOD, FMF, and AAOD from Sentinel-5P/TROPOMI at seven wavelengths across 380–772 nm. Parameter-specific feature engineering was designed by incorporating physical linkages among aerosol optical properties. Bayesian optimization was employed to tune hyperparameters, and SHAP (Shapley additive explanations) was used to interpret feature contributions. The proposed model demonstrated high accuracy and robustness on an independent test set. The retrieved AOD showed excellent agreement with AERONET (R = 0.960, MAE = 0.034, RMSE = 0.070), and similarly strong performance was achieved for FMF (R = 0.955, MAE = 0.027, RMSE = 0.039). For AAOD, an overall correlation of 0.86 was obtained (MAE = 0.005, RMSE = 0.008). Comparisons with existing satellite products indicated globally consistent spatial patterns and improved spatial continuity under high aerosol loading. Overall, the proposed data-driven approach enhances the efficiency and coverage of multi-parameter aerosol retrieval while maintaining high accuracy, supporting absorbing aerosol monitoring, aerosol-type discrimination, and climate-effect assessment.

Oxygen-related absorption features provide fundamental constraints for passive atmospheric remote sensing in the reflected-solar spectrum. Because molecular oxygen is well-mixed in the dry atmosphere, O₂ absorption links measured radiance to atmospheric mass, pressure, and effective photon path length, while O₂-O₂ (O₄) collision-induced absorption provides complementary sensitivity to lower-tropospheric photon paths. This review synthesizes the spectroscopic basis, radiative-transfer mechanisms, satellite implementations, retrieval algorithms, and atmospheric applications of O₂ and O₄ measurements from the ultraviolet to the shortwave infrared. Particular emphasis is placed on the O₂ B-band near 687 nm, the O₂ A-band near 760 nm, O₄ bands in the UV–visible range, and the O₂ band near 1.27 µm. These features support retrievals of cloud fraction, cloud pressure, optical centroid pressure, aerosol layer height, surface pressure, dry-air column abundance, and light-path corrections for greenhouse gas observations. We review major algorithmic approaches, including cloud-as-reflecting-boundary models, cloud-as-layer models, DOAS-based retrievals, optimal-estimation frameworks, photon path-length distribution methods, and machine learning or hybrid techniques. Key applications include cloud climatology, aerosol vertical characterization, air mass factor correction, XCO₂ and XCH₄ retrievals, carbon-cycle studies, and multi-mission data integration. Remaining challenges include spectroscopic uncertainty, aerosol and cloud scattering degeneracy, surface bidirectional reflectance, three-dimensional radiative-transfer effects, wavelength-dependent path mismatch, and inconsistent uncertainty characterization. Future progress will depend on improved spectroscopy, active–passive validation, multi-angle polarimetry, physically constrained machine learning, and harmonized multi-mission retrieval frameworks.