Snow plays a significant role in the global energy balance, climate change, hydrological cycles, and other areas. However, traditional surface observation methods are limited in capturing the spatiotemporal dynamics of snow. This paper systematically reviews Machine Learning algorithms applicable to snow cover recognition. It highlights traditional Machine Learning methods such as Support Vector Machines and Random Forests, as well as more semantically oriented Deep Learning methods, including CNNs, attention mechanisms, and Transformers. These methods have shown robust performance in the domain of snow identification. Lastly, the paper discusses the strengths and weaknesses of different approaches and suggests directions for future research. Through this paper, readers will gain a comprehensive understanding of Machine Learning-based snow recognition algorithms and how these algorithms can be leveraged better.
Ma et al. (Thu,) studied this question.