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.

Snow on the Tibetan Plateau (TP) is not only a freshwater resource for the major rivers in Asia but also plays a significant role in adjusting temperature by reflecting solar radiation. Fractional snow cover mapping with fine spatial and temporal resolution is of great significance for clarifying snow resources and accurately managing snow water resources. However, due to the complex TP topography, the existing FSC retrieval methods are affected by a variety of disturbance factors, resulting in a decrease in accuracy. In this study, a deep learning-based FSC retrieval method, fractional snow cover U-shape net (FSC-USNet), is proposed to improve snow cover mapping accuracy. The input images of FengYun-4A Advanced Geosynchronous Radiation Imager (FY4A/AGRI) and geographic data are extracted using the proposed spatial-channel feature extraction module to characterize the shallow features with texture information into high-dimensional feature images. Additionally, an attention mechanism is introduced to improve the feature differences between different FSC degrees. Finally, the correlation of the decoded features in the channel direction is mined using the channel refinement module to obtain the final FSC results. In this study, a backpropagation artificial neural network (BPANN), random forest, ResNet_FSC, and UNet are trained, compared and validated against the MOA10A1 FSC product. The results show that the proposed method effectively mitigates the problems of unclear texture of snow edges, poor robustness and underestimation in some areas, which exist in other models. Additionally, the proposed method has higher accuracy, with an R2 and explained variance score (EVS) reaching 0.7182 and 0.7332, respectively. Compared to the MOD10A1 snow product, the proposed method exhibits higher detection accuracy in mountainous areas with high snow cover and significantly improves the low snow cover detection rate.

The urbanization process over the past decades has resulted in increasing attention being paid to atmospheric pollution by researchers, especially changes in PM_2.5 concentration. This study attempted to explore the spatiotemporal changes in PM_2.5 concentration in China from 2000 to 2021, as well as their interaction patterns and intensities with temperature, precipitation, vegetation coverage, and land use types. This was carried out by analyzing monthly average PM_2.5 concentration data and various meteorological and geographical factors. Suggestions have also been made to reduce PM_2.5 concentration and improve air quality. The results show that in the past 22 years, the overall concentration of PM_2.5 in China has shown a downward trend, with an average annual rate of 1.42 μg/m³ from 2013 to 2021, accompanied by a clear spatial pattern and significant seasonal changes. The high pollution areas are mainly concentrated in the Tarim Basin, Sichuan Basin, North China Plain, and the Middle and Lower Yangtze Valley Plain, where the PM_2.5 concentration in autumn and winter is significantly higher than that in spring and summer. In addition, based on the national spatial scale, PM_2.5 concentration is negatively correlated with precipitation and vegetation coverage, while it is significantly positively correlated with arable land and impervious surfaces. Strengthening the control of farmland pollution, accelerating urban greening construction, further expanding the scale of forests and grasslands, and enriching vegetation types will help reduce PM_2.5 concentration and improve air quality.

The SSE 50ETF option is China's first stock index option product launched in 2015. For a number of reasons, the options market can sometimes create arbitrage opportunities. Based on the theory of option parity arbitrage and taking into account the transaction costs, this paper explores effective options arbitrage strategies and practices them. Based on the theory of option parity arbitrage and taking into account the transaction costs, this paper establishes an effective option arbitrage strategy model and puts it into practice. The results show that there are indeed arbitrage opportunities in the market that exceed the risk-free rate of return, but there are not many such opportunities, and there is not much arbitrage space under many opportunities. This is not only the embodiment of high market efficiency, but also the result of taking various transaction costs into full consideration in this paper to ensure the effectiveness of arbitrage.