Forests (Aug 2024)

Land Surface Temperature May Have a Greater Impact than Air Temperature on the Autumn Phenology in the Tibetan Plateau

  • Hanya Tang,
  • Xizao Sun,
  • Xuelin Zhou,
  • Cheng Li,
  • Lei Ma,
  • Jinlian Liu,
  • Zhi Ding,
  • Shiwei Liu,
  • Pujia Yu,
  • Luyao Jia,
  • Feng Zhang

DOI
https://doi.org/10.3390/f15081476
Journal volume & issue
Vol. 15, no. 8
p. 1476

Abstract

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The Tibetan Plateau (TP), with its unique geographical and climatic conditions, holds a significant role in global climate change. Therefore, it is particularly urgent to fully understand its vegetation phenology. Herbaceous plants are widely distributed in the TP. However, previous studies have predominantly examined the impact of air temperature on the end date of the vegetation growing season (EOS), with less emphasis on the influence of land surface temperature (LST). In this study, the dynamic changes in the EOS from 2001 to 2020 were analyzed by utilizing the Normalized Difference Vegetation Index (NDVI) data published by NASA. Furthermore, the impact of climate change on the EOS was examined, and the dominant factor (air temperature, LST, or precipitation) influencing the EOS was identified. The main findings were as follows: the average annual EOS predominantly occurred between day of year (DOY) 240 and 280, with an advance from the edge of the plateau to the center. The EOS across the entire region displayed a marginal tendency towards delay, with an average rate of 0.017 days/year. Among all vegetation, shrubs showed the most pronounced delay at a rate of 0.04 days/year. In terms of precipitation, the impact of climate change increased precipitation in both summer and autumn, which could delay EOS. In terms of temperature, an increase in summer Tmin, autumn air temperatures and summer LST delayed the EOS, while an increase in autumn LST advanced the EOS. Compared to air temperature and precipitation, LST had a stronger controlling effect on the EOS (the largest pixel area dominated by LST). These results could offer new insights for enhancing the parameters of vegetation phenology models across the TP.

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