Enhancing Light Efficiency Modeling with Symbolic Regression and KANs

Maximum light use efficiency (LUEmax) is a key parameter in state-of-the-art global carbon models (GCMs), representing the maximum conversion rate of absorbed photosynthetically active radiation into vegetation biomass under non-stress conditions. Despite its significance, LUEmax is often oversimplified in most GCMs, where its variation is constrained by a limited number of plant functional types (PFTs). This coarse classification overlooks well-documented variability within PFTs and fails to account for adaptation and acclimation processes, introducing substantial uncertainty in carbon cycle estimates.

Recent studies suggest that replacing PFT-based parameterization with spatially explicit LUEmax maps could significantly enhance ecosystem productivity modeling. In this study, we explore the potential of symbolic regression, an emerging machine learning technique based on genetic algorithms for deriving explicit mathematical relationships, alongside Kolmogorov-Arnold Networks (KANs) based on parameterized neural networks, which facilitate interpretable functional discovery, to estimate LUEmax from climatic data and key ecosystem traits.

Using novel plant trait datasets and multiannual flux tower eddy covariance observations combined with MODIS data, we assess the ability of symbolic regression techniques and KANs to derive equations linking LUEmax to ecosystem traits. Our findings demonstrate that these approaches improve the generalization of LUEmax estimation and enhance interpretability, offering significant implications for global-scale environmental modeling and remote sensing applications.