Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau

作者：Xin Pang, Hongyi Li, Hongrui Ren, Yaru Yang, Qin Zhao, Yiwei Liu, Xiaohua Hao, Liting Niu · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17111889 · 被引用次数：4 · 研究领域：Arctic and Antarctic ice dynamics、Cryospheric studies and observations、Climate change and permafrost

Accurate remote sensing identification of river ice not only provides scientific evidence for climate change but also offers early warning information for disasters such as ice jams. Currently, many researchers have used remote sensing index-based methods to identify river ice in alpine regions. However, in high-altitude areas, these index-based methods face limitations in recognizing river ice and distinguishing ice-snow mixtures. With the rapid advancement of machine learning techniques, some scholars have begun to use machine learning methods to extract river ice in northern latitudes. However, there is still a lack of systematic studies on the ability of machine learning to enhance river ice identification in high-altitude, complex terrains. The study evaluates the performance of machine learning methods and the RDRI index method across six aspects: river type, altitude, river width, ice periods, satellite data, and snow cover interference. The results show that machine learning, particularly the RF method, demonstrates superior generalization ability and higher recognition accuracy for river ice in the complex high-altitude terrain of the Tibetan Plateau by leveraging a variety of input data, including spectral and topographical information. The RF model performs best under all types of test conditions, with an average Kappa coefficient of 0.9088, outperforming other machine learning methods and significantly outperforming the traditional exponential method, demonstratin...