Effective Improvement of the Accuracy of Snow Cover Discrimination Using a Random Forests Algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 19; p. 4644
• Main Authors: He, Rui, Qin, Yan, Zhao, Qiudong, Chang, Yaping, Jin, Zizhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Accuracy; Algorithms; Automation; case studies; China; Classification; Climate change; Decision trees; forests; Global climate; Hydrologic cycle; Hydrology; Land surface temperature; Machine learning; Maximum likelihood method; model validation; Monitoring; Normalized difference vegetative index; random forests algorithm; reflectance; Regions; Remote sensing; Rivers; shallow snow cover; Snow; Snow cover; snow cover discrimination model; Snow depth; snowpack; surface temperature; Sustainable development; Three-Rivers Headwater Region; vegetation index; water; China; Tibet; Tibetan Plateau

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate...