Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products.

Besides local emissions, long-range transportation of polluted air masses also has a huge impact on haze pollution. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to determine the transport paths and potential sources of haze pollution in the Yangtze River Delta Urban Agglomeration. Haze days were determined by setting the threshold of meteorological elements. Shanghai, Hangzhou, Nanjing and Hefei were selected as four representative cities to calculate the −72 h backward transport trajectory of haze air mass; thus, the main transport path was obtained after clustering. A potential source contribution function and concentration weighted field were used to identify potential pollution sources of the study. The results showed that the number of haze days in the northern Yangtze River Delta Urban Agglomeration is much higher than that in the south. Haze days and Fine particulate matter (PM_2.5) concentration showed a downward trend. The transport paths could be summarized as long-range transports from the northwest and coastal direction during the dry season and short-distance transports from all directions. −72 h air flow trajectories come from the higher altitudes in dry season than these in wet season. The main sources of potential pollution are Hebei, Shandong, Anhui and northern Jiangsu.

Near-real-time estimation of precipitation from geostationary satellites plays a vital role in natural disaster mitigation due to timely monitoring, high spatial–temporal resolution, and large coverage, yet this letter remains a large challenge. In this letter, a novel deep learning-based algorithm entitled precipitation estimation using a multiscale network (DLPE-MS) is proposed to estimate precipitation during summer over the eastern Continental United States (CONUS) of America. When inputting bispectral satellite information (10.3 and 6.2 $\mu \text{m}$ ), this algorithm provides near-real-time rainfall rates at hourly and $0.04^{\circ}\times 0.04^{\circ}$ resolution. In order to emphasize the information of a precipitation region at different scales using satellites’ data, we design a multiscale framework based on convolutional neural networks (CNNs). In addition, a loss function named balanced weight mean square error (BWMSE) is proposed to settle the problem of underestimation caused by a shortage in heavy rainy samples. Compared with the mean square error (MSE), the BWMSE has more balance parameters for different objects when training, which is able to mitigate the deviation between the prediction and ground truth in tailed categories (heavy rainy). Results show that this algorithm achieves the highest probability of detection (POD) and correlation coefficient (CC) with the value of 95.5% and 0.5. The statistical results of the precipitation cases also show that the DLPE-MS can significantly improve the estimated values in tailed categories than other products and methods. After testing, this algorithm is able to estimate the precipitation for the study area within 0.19 s.