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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.

Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.

Accurately estimating fine ambient particulate matter (PM2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM2.5. However, there is little research on full-coverage PM2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing–Tianjin–Hebei (BTH). The LME model was used to calibrate the PM2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R2) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m3 in cross-validation (CV). The seasonal R2 values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 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 PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants.

Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) is related to various adverse health effects. Ground measurements can yield highly accurate PM2.5 concentrations but have certain limitations in the discussion of spatial-temporal variations in PM2.5. Satellite remote sensing can obtain continuous and long-term coverage data, and many previous studies have demonstrated the relationship between PM2.5 and AOD (aerosol optical depth) from theoretical analysis and observation. In this study, a new aerosol product with a high spatial-temporal resolution retrieved from the AHI (the Advance Himawari Imager) was obtained using a vertical-humidity correction method to estimate hourly PM2.5 concentrations in Hebei. The hygroscopic growth factor ( f ( RH ) ) was fitted at each site (in a total of 137 matched sites). Meanwhile, assuming that there was little change in f ( RH ) at a certain scale, the nearest f ( RH ) of each pixel was determined to calculate PM2.5 concentrations. Compared to the correlation between AOD and PM2.5, the relationship between the “dry” mass extinction efficiency obtained by vertical-humidity correction and the ground-measured PM2.5 significantly improved, with r coefficient values increasing from 0.19–0.47 to 0.61–0.76. The satellite-estimated hourly PM2.5 concentrations were consistent with the ground-measured PM2.5, with a high r (0.8 ± 0.07) and a low RMSE (root mean square error, 30.4 ± 5.5 μg/m3) values, and the accuracy in the afternoon (13:00–16:00) was higher than that in the morning (09:00–12:00). Meanwhile, in a comparison of the daily average PM2.5 concentrations of 11 sites from different cities, the r values were approximately 0.91 ± 0.03, and the RMSEs were between 13.94 and 31.44 μg/m3. Lastly, pollution processes were analyzed, and the analysis indicated that the high spatial-temporal resolution of the PM2.5 data could continuously and intuitively reflect the characteristics of regional pollutants (such as diffusion and accumulation), which is of great significance for the assessment of regional air quality.

The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8’s view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies.

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.

Fine particulate matter (PM2.5) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM2.5 with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (Ha) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (f(RH)) was fitted site-by-site and month by month (1–12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM2.5 concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017–2018. The results indicate that the correlation between “dry” extinction coefficient and PM2.5 is slightly improved compared to the correlation between AOD and PM2.5, with r coefficient values increasing from 0.12–0.45 to 0.32–0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM2.5 and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM2.5 concentration and analyze a pollution process in the fall and winter.

Aerosol optical depth (AOD) is a common indicator applied in monitoring aerosols in the atmosphere. The hilly landscape and rapid economic growth of the megacity Chongqing have facilitated increased aerosol concentration, and it is meaningful to accurately retrieve AOD over Chongqing. The HJ-1A/B satellite of China carries a sensor/camera called the Charge Coupled Device (CCD), the spatial resolution of which meets the requirement for retrieving high resolution AOD. In this paper, analysis of the AOD retrievals from different methods using the HJ-1 satellite data revealed the most suitable algorithm. Through comparison with the AOD product of Moderate Resolution Imaging Spectroradiometer (MODIS), the AOD retrieval results using enhanced vegetation index (EVI) to estimate dark pixels showed the highest correlation. The continental aerosol model was used to build a lookup table that was able to facilitate a good AOD retrieval for both city and rural areas. Finally, the algorithm that combined dark pixels, buffer areas, and the deep blue algorithm was found to be most suitable for AOD retrieval. The AOD retrieval results based on the HJ-1 data were consistent with MODIS products, and our algorithm yields reasonable results in most cases. The results were also compared with ground-based PM10 measurements synchronized with the overpass time of the HJ-1 satellite, and high correlation was found. The findings are relevant to other Chinese satellite data used for retrieving AOD on the same channels.

Air pollution is becoming increasingly serious with rapid economic development in China, and the primary pollutant has converted from PM10 to PM2.5, which is associated with more adverse impacts on human health. Satellite remote sensing, with help of its quantitative observations over large spatial and temporal extent, has become a significant supplement to the in situ measurements. This study exploits the aerosol optical depth (AOD) product retrieved from the Medium Resolution Spectrum Imager (MERSI) onboard Fengyun-3C satellite to estimate PM2.5 estimation over the major urban areas of Chongqing, a metropolitan city in the Southwestern China. A semi-empirical model and the linear mixed effect (LME) model are combined based on in situ observations from local air quality and meteorological networks from May 2014 to May 2015. This combined model is able to explain about 90% of the variation of the estimated PM2.5, and performs better than LME model by achieving higher correlation and smaller deviations between the satellites estimated PM2.5 and in situ measurements. Benefiting from the high resolution of 1 km × 1 km, MERSI AOD achieves much more detailed spatial distribution of ground-level PM2.5 over the major urban areas of Chongqing, compared to most of concurrent satellite products, such as the MODIS L2 AOD. According to the estimation, PM2.5 concentration is higher in cold seasons than in warm seasons in Chongqing. Peak levels of PM2.5 is found in Yuzhong District, the center of Chongqing urban area, while the concentration gradually decreases in surrounding areas, indicating that air pollution in Chongqing is highly contributed by local anthropogenic emissions.