Vegetation is a critical component of the biosphere, providing irreplaceable ecosystem services to humanity. It regulates regional and global climates by absorbing atmospheric CO₂ and modulating sensible and latent heat fluxes. Against the backdrop of global change, accurately predicting future vegetation dynamics is essential for improving Earth system modeling. This project integrates ground-based leaf area index (LAI) observations, medium-to-high-resolution remote sensing data, and supplementary plot surveys to reconstruct a globally consistent and accurate LAI time-series dataset using radiative transfer theory and machine learning models. This dataset will then be applied to evaluate the performance of Earth System Models (ESMs) in simulating vegetation dynamics and to predict global LAI trends under multiple climate scenarios.

Significance:

• Data Innovation: Delivers a reliable global LAI time-series product for vegetation-climate interaction studies.

• Model Advancement: Identifies strengths and weaknesses of ESMs in vegetation simulation, guiding model optimization.

• Policy Support: Enhances scenario-based predictions of vegetation responses to climate change, supporting evidence-based ecosystem management and sustainable development strategies.

【Principal Investigator:Dr. Zaichun Zhu ，Duration: January 2020 – December 2022】