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.

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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 (PM2.5) 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.

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