Semantic segmentation of high-resolution remote sensing images (HRSIs) is a challenging task because objects in HRSIs usually have great scale variance and appearance variance. Although deep convolutional neural networks (DCNNs) have been widely applied in the semantic segmentation of HRSIs, they have inherent limitations in capturing global context. Attention mechanisms and transformer can effectively model long-range dependencies, but they often result in high computational costs when being applied to process HRSIs. In this article, an encoder-decoder network (MSGCNet) is proposed to fully and efficiently model multiscale context and long-range dependencies of HRSIs. Specifically, the multiscale interaction (MSI) module employs an efficient cross-attention to facilitate interaction among multiscale features of the encoder, which bridges the semantic gap between high- and low-level features and introduces more scale information to the network. In order to efficiently model long-range dependencies in both spatial and channel dimensions, the transformer-based decoder block (TBDB) implements window-based efficient multihead self-attention (W-EMSA) and enables interactions cross windows. Furthermore, to further integrate the global context generated by TBDB, the scale-aware fusion (SAF) module is proposed to deeply supervise the decoder, which iteratively fuses hierarchical features through spatial attention. As demonstrated by both quantitative and qualitative experimental results on two publicly available datasets, the proposed MSGCNet exhibits superior performance compared to currently popular methods. The code will be available at http://github.com/JingxiangZhou/MSGCNet .

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM _2.5 , PM ₁₀ , O ₃ , NO ₂ , SO ₂ , and CO), we propose a robust estimation framework called Learning Air Pollutants from Satellite Observations (LAPSO). The principle of LAPSO is to derive a non-linear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyses based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models, lower hardware requirements as well as a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites (R ² > 0.8 in polluted areas and uncertainty ≪ 5 μg/m ³ for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOMI onboard the Sentinel-5P demonstrated marginally better performance for estimating PM _2.5 . Although the selection of satellite observations did not significantly affect the results of O ₃ estimation, the number and spatial sampling density of in-situ sites imposed large impacts on the O ₃ estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

Continuously monitoring surface ozone (O ₃ ) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O ₃ estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a deep forest 21 (DF21) model to interpolate O ₃ concentration by learning spatial patterns and a long short-term memory (LSTM) model to forecast O ₃ concentration by learning data from the past. We used sites of city-level in situ networks as the control sites to manifest short-distance O ₃ transportation. Satellite-based observations of O ₃ precursor indicators were incorporated to capture O ₃ photochemical reactions. DF21 explained a larger fraction of O ₃ variability (90%) with a mean bias error (MBE) of smaller than $1~\mu \text {g/m}^{3}$ . We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas ( $R^{2} > 0.85$ and bias $< 2~\mu \text {g/m}^{3}$ ). The forecast O ₃ concentration via LSTM showed a good and stable agreement ( $R^{2}\approx 0.85$ and bias $< 5~\mu \text {g/m}^{3}$ ) with ground-based measurements for 8-, 24-, 28-, and 72-h time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O ₃ estimates for broad users.

The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection, paving the way for scene-level landscape pattern analysis and decision making. Encoder-decoder structures based on attention mechanisms have been frequently used for fine-resolution image segmentation. In this paper, we incorporate a coordinate attention (CA) mechanism, adopt an asymmetric convolution block (ACB), and design a refinement fusion block (RFB), forming a network named the fusion coordinate and asymmetry-based U-Net (FCAU-Net). Furthermore, we propose novel convolutional neural network (CNN) architecture to fully capture long-term dependencies and fine-grained details in fine-resolution remotely sensed imagery. This approach has the following advantages: (1) the CA mechanism embeds position information into a channel attention mechanism to enhance the feature representations produced by the network while effectively capturing position information and channel relationships; (2) the ACB enhances the feature representation ability of the standard convolution layer and captures and refines the feature information in each layer of the encoder; and (3) the RFB effectively integrates low-level spatial information and high-level abstract features to eliminate background noise when extracting feature information, reduces the fitting residuals of the fused features, and improves the ability of the network to capture information flows. Extensive experiments conducted on two public datasets (ZY-3 and DeepGlobe) demonstrate the effectiveness of the FCAU-Net. The proposed FCAU-Net transcends U-Net, Attention U-Net, the pyramid scene parsing network (PSPNet), DeepLab v3+, the multistage attention residual U-Net (MAResU-Net), MACU-Net, and the Transformer U-Net (TransUNet). Specifically, the FCAU-Net achieves a 97.97% (95.05%) pixel accuracy (PA), a 98.53% (91.27%) mean PA (mPA), a 95.17% (85.54%) mean intersection over union (mIoU), and a 96.07% (90.74%) frequency-weighted IoU (FWIoU) on the ZY-3 (DeepGlobe) dataset.

Fine particulate matter (PM_2.5) threatens human health and the natural environment. Estimating the near-ground PM_2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM_2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM_2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM_2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM_2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R²) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m³, 8 ± 3%, and 1.40 ± 0.2 μg/m³, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R² values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m³, ~5%, and 0.60 ± 0.2 μg/m³, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R² values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM_2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants.

In contrast to the threats to urban human health, impacts of air pollutants on the ecosystem photosynthesis seem to be less concerned. The existence of aerosols could promote photosynthesis by increasing the ratio of diffuse to direct solar radiation; on the contrary, ozone (O ₃ ) could inhibit photosynthesis, as it is detrimental to leaf stomata. However, it is unknown whether these two opposite impacts worldwide cancel each other out. In the current mainstream methods, earth system models may show conflicts with in situ experimental results due to their relatively coarse resolution. In virtue of satellite remote sensing and a global eddy covariance (EC) network, we studied ten years of data to explore the impacts of aerosol and O ₃ on photosynthesis by fitting an explainable machine learning model. The impacts of aerosol on gross primary productivity (GPP) were positive in many cases, yet very weak. By means of the nitrogen dioxide (NO ₂ ) to formaldehyde (HCHO) ratio, O ₃ was seen with positive impacts on photosynthesis under the NO _x -sensitive regime, but the apparent positive impacts correlated with the plant phenology. Under the volatile organic compound (VOC)-sensitive regime, the impacts of O ₃ on GPP were not obvious, which was likely due to the prioritized depletion of O ₃ by NO ₂ and VOCs. The impacts of air pollutants depended on many factors and results varied case by case, but the overall net impacts were negative.