Reconstructing the Seasonality and Trend in Global Leaf Area Index During 2001–2017 for Prognostic Modeling

Abstract

Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. However, there are still large uncertainties in simulating the global LAI in Earth system models. In this study, we used climate and soil variables and the Farquhar's biochemical model to reconstruct global LAI, explore the mechanisms controlling global LAI seasonality, and analyze the feasibility of Farquhar's biochemical model in estimating the effect of CO 2 fertilization on global LAI. The results show that the reconstructed LAI (RLAI) based on climate and soil variables can explain 93% of the seasonal dynamics of global LAI. However, RLAI only explained 27.3% of the global LAI trend and captured 29.8% of the land area with a significant trend. RLAI after incorporating the CO 2 fertilization effect, which is estimated by Farquhar's biochemical model, can explain 68.8% (41.5% improvement from RLAI) of the global LAI trend and capture 63.3% (33.5% improvement from RLAI) of the area with a significant LAI trend. These results suggest that it is feasible to use Farquhar's biochemical model to estimate the effect of CO 2 fertilization on the global trend in LAI. The statistical model for reconstructing the seasonal dynamics of LAI and the Farquhar model-based method for estimating the LAI temporal trend developed in this study would be useful for improving or evaluating the performance of prognostic models for future global carbon cycle research. Furthermore, this study may provide a new way to simulate global LAI for prognostic modeling.

Regional Contributions and Climate Attributions to Interannual Variation of Global Net Ecosystems Production by an ECOSYSTEM Processed Model Driven by Remote Sensing Data over the Past 35 Years

“…Studying global and regional IAV of NEP relies on ecosystem models. Terrestrial biosphere models can be classified into two main types depending on how leaf area index (LAI) is obtained: diagnostic and prognostic models [18]. Prognostic models use climatic and edaphic conditions to simulate vegetation structure and the carbon cycle [1].…”

Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations

“…They successfully detected long-term global land change. Wang et al (2020) [4] analyzed the long-term trend of global Leaf Area Index (LAI) and developed a prognostic model to simulate it.…”