Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020

• 发表在: Remote Sensing
• Main Authors: Weixiao Han, Chunlin Huang, Juan Gu, Jinliang Hou, Ying Zhang
• 格式: 文件
• 语言: 英语
• 出版: MDPI AG 2021-04-01
• 主题: machine learning; Qinghai Lake; MODIS; Google Earth Engine; ice phenology
• 在线阅读: https://www.mdpi.com/2072-4292/13/9/1695

The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years; the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year.