Exploring a New Approach to Modeling Leaf Area Index

Leaves are the main carriers of vegetation for photosynthesis and other physiological activities. The leaf density and canopy structure can be characterized by the leaf area index (LAI), defined as the total one-sided green leaf area per unit of the ground surface. As a key variable in modeling terrestrial vegetation, LAI has a major impact on carbon and water exchanges among soil, vegetation, and atmosphere. Typically, process-based models predict LAI as the outcome of vegetation leaf growth and turnover processes (Figure 1). Specifically, the leaf growth is dependent on vegetation gross primary production, respiration, allocation to leaves, and leaf mass per area. Additionally, these turnover processes are related to leaf longevity.

Figure 1. Schematic diagram of the LAI prediction framework, modified from the study of Cui et al., 2019. CUE, carbon use efficiency; τveg, vegetation C turnover time; Fleaf, leaf C fraction. SLA, specific leaf area; PLAI, LAI‐level photosynthesis. GPP, gross primary productivity; LAI, leaf area index; NPP, net primary productivity.

Numerous studies have assessed process-based model performances on the prediction of LAI. Medlyn et al. (2016) found that the simulation of the maximum LAI differs significantly among models, and the largest difference can reach 3 m2 m−2. The models also differ in the magnitude and sign of the projected change of LAI in response to elevated CO2 concentration and warming. The differing predictions arise because the way the modeled processes are parameterized for different plant functional types varies between models. Inter-model differences reflect a lack of understanding of the control of these basic processes.

An alternative approach that could be used to predict LAI is based on the idea of the Eco-evolutionary optimality theory (EEO). It assumes that plants acclimate or adapt to their environment on both shorter eco-physiological timescales and on longer demographic and evolutionary timescales. An EEO approach to predicting vegetation cover, based on the idea of mass balance and maximum carbon profit, has been successfully applied to both natural vegetation in Austria (Yang et al., 2018) and crops in China (Qiao et al., 2020). A similar EEO approach can be used to predict LAI and its dynamics globally in a parsimonious way.

Stay tuned for our findings as we explore solutions based on this approach.


Cui, et al., (2019). Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region. Global Biogeochemical Cycles, 33(6), 668-689.

Medlyn et al., (2016). Using models to guide field experiments: a priori predictions for the CO2 response of a nutrient- and water-limited native Eucalypt woodland. Global Change Biology, 22(8), 2834-2851.

Yang et al., (2018). Applying the Concept of Ecohydrological Equilibrium to Predict Steady State Leaf Area Index. Journal of Advances in Modeling Earth Systems, 10(8).

Qiao, et al. (2020). Extending a first-principles primary production model to predict wheat yields. Agricultural and Forest Meteorology, 287, 107932.


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