## 1 Introduction  
The maximum light use efficiency (LUEmax) of vegetation is an important parameter in understanding ecosystem productivity and carbon cycling [1,2], which has become an indispensable part of estimating vegetation productivity. In the light use efficiency (LUE) model, actual light use efficiency is calculated as an LUEmax regulated by stresses from environmental factors, such as light intensity, temperature, water, and nutrients [3,4]. LUEmax is an intrinsic biophysical attribute of plant communities, quantifying the maximum rate at which absorbed photosynthetically active radiation (APAR) is converted into biomass through canopy photosynthesis under optimal environmental conditions [5,6]. Suboptimal temperatures and water deficits lead to a decline in LUEmax, which also varies depending on the vegetation type and environmental conditions [7]. LUEmax shows large variability even within the same plant functional type. In gross primary productivity (GPP) modeling, the LUEmax is an important parameter for evaluating GPP. GPP denotes the carbon absorbed by the Earth's ecosystems through photosynthesis and is an essential carbon cycle component. Precisely estimating the spatiotemporal variability of LUEmax is of great significance for enhancing the accuracy of GPP models, particularly in the context of increasingly severe climate change [5,8,9]. Therefore, the determination of LUEmax values remains largely uncertain and is influenced by numerous factors. The LUEmax has important implications for the accurate estimation of vegetation productivity [10,11]. The awareness of LUEmax can be categorized into the following three types: (1) Fixed value--during the early 1990s, a globally invariant LUEmax value of 0.39 g/MJ was universally applied in terrestrial productivity models, disregarding biome-specific photosynthetic adaptations [12,13]. (2) Adjusting the LUEmax based on the type of vegetation [14, 15, 16]--Growing evidence suggests that LUEmax exhibits biome-dependent variability governed by multiple biotic and abiotic drivers, particularly stomatal regulation, solar geometry, and environmental stress conditions [5]. A prominent example is the MODIS Biome Properties Look-Up Table (BPLUT), which specifies distinct LUEmax parameters for 11 terrestrial biomes incorporated into multiple modeling frameworks [17]. The VPM and GLO-PEM models also differentiate LUEmax values between C3 and C4 plants [18,19]. (3) Dynamic value--The TS-LUE model calculates the LUEmax value in different growth stages using the LAI [20] and the photosynthetically active radiation (PAR)-regulated dynamic LUEmax (PAR-LUE) by considering the nonlinear response of vegetation photosynthesis to solar radiation [21]. However, a fixed value for the LUEmax is a source of error in vegetation GPP estimations [5]. Meanwhile, adjusting the LUEmax based on the vegetation type means that the spatial heterogeneity of the LUEmax is ignored, leading to apparent uncertainties in LUE-based GPP estimation. Additionally, a dynamic LUEmax enhances accuracy and aligns more closely with the natural physiological laws of vegetation. Current research suggests that the LUEmax is not a constant but is influenced by various factors, such as vegetation type and climatic conditions, and it exhibits significant variability at different spatial and temporal scales [22]. This variability leads to uncertainty in the accuracy of modeling GPP [6]. Meanwhile, previous studies mainly focused on the LUEmax of specific vegetation types or climate types and lacked analyses of the changing patterns in LUEmax values for multiple vegetation types in different climate types. Consequently, lacking a common basis for LUEmax values limits comparative analyses, resulting in significant variation in reported LUEmax estimates [23,24].