Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau

Yanhong Wu; Linan Guo; Bing Zhang; Hongxing Zheng; Lanxin Fan; Haojing Chi; Junsheng Li; Shenglei Wang

doi:10.1038/s41597-022-01863-9

Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau

Sci Data. 2022 Dec 2;9(1):743. doi: 10.1038/s41597-022-01863-9.

Authors

Yanhong Wu^{1

2}, Linan Guo^{2

3}, Bing Zhang^{4

5

6}, Hongxing Zheng⁷, Lanxin Fan^{1

2

8}, Haojing Chi^{1

2

8}, Junsheng Li^{1

2

8}, Shenglei Wang^{1

2}

Affiliations

¹ International Research Centre of Big Data for Sustainable Development Goals, Beijing, 100094, China.
² Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
³ Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China.
⁴ International Research Centre of Big Data for Sustainable Development Goals, Beijing, 100094, China. zb@radi.ac.cn.
⁵ Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China. zb@radi.ac.cn.
⁶ University of the Chinese Academy of Sciences, Beijing, 100049, China. zb@radi.ac.cn.
⁷ CSIRO Land and Water, Canberra, ACT 2601, Australia.
⁸ University of the Chinese Academy of Sciences, Beijing, 100049, China.

Abstract

The Tibetan Plateau (TP) is a region sensitive to global climate change and has been experiencing substantial environmental changes in the past decades. Lake ice phenology (LIP) is a perceptible indicator reflecting changes of lake thermodynamics in response to global warming. Lake ice phenology over the Tibetan Plateau is however rarely observed and recorded. This research presents a dataset containing 39-year (1978-2016) lake ice phenology data of 132 lakes (each with area >40 km²) over the Tibetan Plateau by combining the strengths of both remote sensing (MOD11A2, MOD10A1) and numerical modelling (air2water). Data validation shows that the ice phenology data derived by our method is highly consistent with that based on existing approaches (with R² > 0.75 for all phenology index and RMSE < 5d). The dataset is valuable to investigate the lake-atmosphere interactions and long-term hydrothermal change of lakes across the Tibetan Plateau.

Publication types

Dataset