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Constructing time-series submerged aquatic vegetation by integrating process-based modeling and satellite images

Author

Listed:
  • Qi, Lingyan
  • Yin, Han
  • Wang, Zhengxin
  • Dai, Liuyi
  • Ye, Liangtao
  • Zhang, Kejia
  • Guo, Mingzhu
  • Qi, Haifeng
  • Huang, Jiacong

Abstract

Submerged aquatic vegetation (SAV) plays a critical role in lake ecosystem health. However, quantifying the spatiotemporal patterns of SAV biomass remains challenging due to limited time-series data. To address this challenge, we integrated a process-based SAV dynamic model with a satellite-based SAV biomass estimation model to construct a time-series SAV dataset for Lake Zhanbei, a sub-lake within China's largest freshwater lake, Lake Poyang. The integrated model effectively captured SAV biomass dynamics, with model performance of R2=0.60 and RMSE=0.24 kg/m2 compared to measured data. Results showed that SAV was more abundant near floodplain areas. A significant decline of SAV biomass was observed from 0.76 kg/m2 (2021) to 0.19 kg/m2 (2022), primarily due to a drop in the annual average water level from 14.1 m (2021) to 13.4 m (2022) caused by extreme drought. Water level was the most sensitive driver of SAV biomass, while temperature also had a notable impact under optimal water levels. Our scenario simulations revealed that global warming could enhance SAV growth, while nutrients had minimal effects. Compared with in-situ measurements from previous publications, the integrated model offers a cost-effective and high-resolution approach to study SAV dynamics, with potential applications in other lakes.

Suggested Citation

  • Qi, Lingyan & Yin, Han & Wang, Zhengxin & Dai, Liuyi & Ye, Liangtao & Zhang, Kejia & Guo, Mingzhu & Qi, Haifeng & Huang, Jiacong, 2025. "Constructing time-series submerged aquatic vegetation by integrating process-based modeling and satellite images," Ecological Modelling, Elsevier, vol. 504(C).
    • Handle: RePEc:eee:ecomod:v:504:y:2025:i:c:s0304380025000602
    DOI: 10.1016/j.ecolmodel.2025.111074
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