Due to the advantage of geostationary satellites, Himawari-8/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R² for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R² = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R² increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training.

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO₂) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm⁻¹ spectral resolution covering a 15 μm CO₂-absorbing band, providing a way of monitoring CO₂ with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R² of XCO₂ prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R² = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO₂. The deviation of XCO₂ predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO₂ concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R² = 0.9671, RMSE = 0.7072 ppmv), Europe (R² = 0.9703, RMSE = 0.8733 ppmv), North America (R² = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R² = 0.9558, RMSE = 0.4614 ppmv).

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.