FSC-USNet: Fractional Snow Cover Retrieval on the Tibetan Plateau by Integrating Improved Attention Mechanisms

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 fractional snow cover (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, FSC U-shape net, is proposed to improve snow cover mapping accuracy. The input images of FengYun-4A advanced geosynchronous radiation imager 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, 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
R
²
and explained variance score 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.