Fine-scale monitoring of lake ice phenology by synthesizing remote sensed and climatologic features based on high-resolution satellite constellation and modeling

Jie Tong; Yaling Lin; Chenyu Fan; Kai Liu; Tan Chen; Fanxuan Zeng; Pengfei Zhan; Linghong Ke; Yongnian Gao; Chunqiao Song

doi:10.1016/j.scitotenv.2023.169002

Fine-scale monitoring of lake ice phenology by synthesizing remote sensed and climatologic features based on high-resolution satellite constellation and modeling

Sci Total Environ. 2024 Feb 20:912:169002. doi: 10.1016/j.scitotenv.2023.169002. Epub 2023 Nov 29.

Authors

Jie Tong¹, Yaling Lin², Chenyu Fan², Kai Liu³, Tan Chen⁴, Fanxuan Zeng⁴, Pengfei Zhan², Linghong Ke⁵, Yongnian Gao⁶, Chunqiao Song⁷

Affiliations

¹ Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China.
² Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Science, Beijing 100049, China.
³ Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China.
⁴ Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
⁵ College of Hydrology and Water Resources, Hohai University, Nanjing 211100, China.
⁶ School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China. Electronic address: yngao@hhu.edu.cn.
⁷ Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China; University of Chinese Academy of Science, Beijing 100049, China. Electronic address: cqsong@niglas.ac.cn.

PMID: 38040347
DOI: 10.1016/j.scitotenv.2023.169002

Abstract

Lake ice, as a crucial component of the cryosphere, serves as a sensitive indicator of climate change. Fine-scale monitoring of spatiotemporal patterns in lake ice phenology holds significant importance in scientific research and environmental management. However, the rapid and dynamic nature of the freeze-thaw process of lake ice poses challenges to existing methods, resulting in their limited application in small lakes. In this study, we propose a novel approach of investigating ice phenology of lakes in various sizes. We conducted a case study in Hoh Xil, known for its vulnerability to climate change and a wide distribution of small lakes, to analyze the ice phenology of 372 lakes (>1 km²) during 2017-2021. Firstly, ensemble machine-learning model was developed for lake ice identification from Landsat-8/9 and Sentinel-2 A/B imagery. The accuracy evaluation reveals the overall good performance for ice extraction results based on Landsat-8/9 (97.03 %) and Sentinel-2 A/B (96.89 %). Next, the XGBoost models were employed to reconstruct ice coverages on unobserved dates for the freezeup and breakup periods, respectively. Totally, 744 XGBoost models were constructed for the study lakes, and the majority of them perform well. Based on the reconstructed daily ice coverage, phenology parameters could be extracted for examining the spatiotemporal characteristics of ice cover and possible relationships with lake sizes and terrains. From early-October to early-November, the Hoh Xil lakes freeze from the northwest to the southeast, while the breakup period starts in late-March and lasts until late-June. Moreover, the results indicate relatively small variability in freezeup-end dates among lakes, but significant differences in breakup dates, showing a greater sensitivity to temperature variations. Furthermore, ice phenology in small lakes exhibit stronger consistency with subtle climatic fluctuations. The results highlight the significant role of ice phenology in small lakes, as they dominate the overall tendency of ice phenology in Hoh Xil.

Keywords: Hoh Xil; Lake ice phenology; Landsat 8; Landsat 9; Machine learning; Sentinel-2.