A Meteorology-Driven Lstm For Predicting Time Series Of Vegetation Leaf Area Index In Land Surface Model

• Published in: IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 4915 - 4918
• Main Authors: Xiong, Zhenhua, Xin, Qinchuan, Zhang, Zhicheng, Gui, Hanliang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.07.2023
• Subjects: Biological system modeling; deep learning; LAI; Land surface; land surface model; LSTM; Predictive models; terrestrial vegetation; Time series analysis; Uncertainty; Vegetation; Vegetation mapping
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS52108.2023.10281840

The leaf area index (LAI) is a basic vegetation structural variable in the ecosystem and climate model. Predicting vegetation LAI time series is essential for understanding the response of vegetation growth processes to climate change. Current predictive numerical phenology models are still subject to large uncertainties. Developing LAI time series prediction models is beneficial to improve the ability of land surface models to predict future vegetation growth. This study develops a deep learning model based on meteorological variables and attentional mechanisms to predict vegetation LAI time series. We tested our ALSTM for different biome types across North America and evaluated the model using satellite and flux tower data. The results demonstrated that the ALSTM outperforms other baseline models. The predicted ALSTM LAI shows high temporal and spatial consistency compared to satellite-based LAI. The ALSTM model can effectively predict downstream gross primary productivity (GPP) when coupled with the common land model (CoLM). ALSTM performs high accuracy in predicting the LAI of terrestrial vegetation and provides a reference for developing biophysical methods to predict future vegetation ecological processes for land surface models.