The spatiotemporal variation in sulphur dioxide (SO₂) concentration during 2005–2008 over China was monitored from the planetary boundary layer (PBL) SO₂ column concentration retrieved from Aura ozone monitoring instrument (OMI) data, in this study. The obtained results indicate that SO₂ concentration has an imbalanced spatial distribution with the highest level in central East China. Of the 31 provinces/municipalities/autonomous regions in mainland China, 23 still exhibited a rising trend of SO₂ concentration during 2005–2008, against eight that showed a sign of decrease. The correlation coefficient between the mean SO₂ concentration during 2005–2008 and coal consumption per unit area is as high as 0.760 if Shanghai is excluded from the analysis. With the exception of Beijing, all other provinces increased their coal consumption per unit area. Therefore, there is much more work to be done to bring down SO₂ concentration.

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) has been widely used in atmospheric environment and climate change research. Based on data of the Aerosol Robotic Network and Sun–Sky Radiometer Observation Network in the Yangtze River Delta, the retrieval accuracies of MODIS C6.1 Dark Target (DT), Deep Blue (DB), and C6.0 Multi-angle Implementation of Atmospheric Correction (MAIAC) products under different land cover types, aerosol types, and observation geometries were analyzed. About 65.64% of MAIAC AOD is within the expected error (Within EE), which is significantly higher than 41.43% for DT and 56.98% for DB. The DT product accuracy varies most obviously with the seasons, and the Within EE in winter is more than three times that in spring. The DB and MAIAC products have low accuracy in summer but high in other seasons. The accuracy of the DT product gradually decreases with the increase in urban and water land-cover proportion. After being corrected by bias and mean relative error, the DT accuracy is significantly improved, and the Within EE increases by 24.12% and 32.33%, respectively. The observation geometries and aerosol types were also examined to investigate their effects on AOD retrieval.

Atmospheric aerosol significantly affects the climate environment and public health, and Aerosol Optical Depth (AOD) is a fundamental optical characteristic parameter of aerosols, so it is important to develop methods for obtaining AOD. In this work, a novel AOD retrieval algorithm based on a Convolutional Neural Network (CNN) method that could provide continuous and detailed aerosol distribution is proposed. The algorithm utilizes data from Sentinel-2 and Aerosol Robotic Network (AERONET) spanning from 2016 to 2022. The CNN AOD data are consistent with the AERONET measurements, with an R² of 0.95 and RMSE of 0.049 on the test dataset. CNN demonstrates superior performance in retrieving AOD compared with other algorithms. CNN retrieves AOD well on high reflectance surfaces, such as urban and bare soil, with RMSEs of 0.051 and 0.042, respectively. CNN efficiently retrieves AOD in different seasons, but it performs better in summer and winter than in spring and autumn. In addition, to study the relationship between image size and model retrieval performance, image datasets of 32 × 32, 64 × 64 and 128 × 128 pixels were created to train and test the CNN model. The results show that the 128-size CNN performs better because large images contain rich aerosol information.

Land cover changes are the main causes of carbon emissions and ecological damage, so accurate land cover data are essential for precise carbon emissions estimates. The Dempster–Shafer (D-S) evidence theory was used to fuse three sets of 30-m land cover data: China’s National Land Use and Cover Change (CNLUCC), Global 30-m Land Cover Classification with a Fine Classification System (GLC_FCS30), and the China Land Cover Dataset (CLCD), to product high-precision land cover data, and carbon emissions were calculated based on carbon emission factor method in the Yangtze River Delta (YRD) from 1990 to 2020. The results show that (1) the average annual accuracies of the three products, CNLUCC, GLC_FCS30, and CLCD, are 64%, 78%, and 76%, respectively, with CLC_FCS30 showing higher classification accuracy. (2) The D-S evidence theory as a fusion method effectively integrates multi-source land cover data, enhancing the overall accuracy of the fused data by 11% to 22% compared with the original data. (3) Carbon emissions in the YRD region reached 594.043 million tons by the end of 2020, marking an increase of 468.480 million tons since 1990. Notably, there is a significant spatial variability in carbon emissions, with coastal areas exhibiting higher emissions, and a shift toward inland regions. This can serve as a valuable reference for urban planning and environmental conservation in the YRD region.

The impact of aerosol is a key issue on atmospheric correction in remote sensing. The aim of this work is to evaluate the radiative impact of the aerosol type on the results of the atmospheric correction in Lake Taihu, a typical case-2 water. The atmospheric correction of MERIS images were carried out using the 6S atmospheric radiation transfer model. The water reflectances obtained by different aerosol types were compared with the measured spectral data. The correction results of the Continental and Maritime model are similar, and more reliable than the results of urban model. Continental aerosol model achieved good results in bands from 3 to 9, but in other bands, the results of maritime aerosol model are better than the continental results. Therefore, Selection of an accurate aerosol model is the primary task of atmospheric correction.