This study aims to (1) investigate whether two-big-leaf light use efficiency (LUE) models (TL) outperform big-leaf LUE models (BL) by incorporating different gross primary productivity (GPP) responses in sunlit and shaded leaves; (2) explore the robustness of using the leaf area index (LAI), clumping index (Ω) and spherical leaf angle distribution to partition canopies into sunlit and shaded leaves across canopy architectures; (3) identify optimal light response forms in LUE models. To exclude influences of drivers of GPP other than radiation, we collected various formulations of GPP response functions to temperature, vapor pressure deficit, CO₂, soil water supply, light intensity and cloudiness index to construct 5600 BLs and 1120 TLs. These models were evaluated at 196 globally-distributed eddy covariance sites from the FLUXNET observational network using the Nash-Sutcliffe model efficiency (NSE), root mean squared error and Bayesian information criterion. Across all sites, the best big-leaf model (BL*; NSE=0.82) was statistically equal to the best TL (TL*; NSE=0.84). However, daily dynamics in GPP under hot and dry conditions were best described using TL* in 17% of sites, highlighting the local importance in separating sunlit and shaded leaves. Across approaches to represent effective LAI, the best approach relies on using normalized difference vegetation index with a spherical or flexible leaf angle distribution across sites rather than satellite LAI and Ω. We also observed similar performance between non-rectangular hyperbola and reciprocal light response functions across TLs. Models degrade when the maximum LUE is not differentiated between sunlit and shaded leaves, but not when light saturation levels are the same. Despite functional differences, the best five TLs agree in a larger contribution of shaded leaf area to total GPP, resulting from higher LAI and LUE. Overall, these results suggest marginal but robust selection of TL compared to BL.

本研究旨在 (1) 通过将不同的总初级生产力 (GPP) 响应纳入阳光照射和遮荫的叶子，研究两个大叶光利用效率 (LUE) 模型 (TL) 是否优于大叶 LUE 模型 (BL)；(2) 探索使用叶面积指数 (LAI)、聚集指数 ( Ω ) 和球形叶角分布将冠层划分为跨冠层结构的阳光照射和阴影叶子的稳健性；(3) 识别 LUE 模型中的最佳光响应形式。为了排除除辐射以外的 GPP 驱动因素的影响，我们收集了 GPP 对温度、蒸汽压不足、CO _{2的响应函数的各种公式}, 土壤供水、光照强度和云量指数构建 5600 个 BLs 和 1120 个 TLs。使用 Nash-Sutcliffe 模型效率 (NSE)、均方根误差和贝叶斯信息准则，在来自 FLUXNET 观测网络的 196 个全球分布的涡流协方差点对这些模型进行了评估。在所有站点中，最佳大叶模型（BL*；NSE=0.82）在统计学上等于最佳 TL（TL*；NSE=0.84）。然而，在炎热和干燥条件下，GPP 的每日动态最好在 17% 的地点使用 TL* 进行描述，突出了当地在区分阳光照射和阴影叶子方面的重要性。在表示有效 LAI 的各种方法中，最好的方法依赖于使用具有跨站点的球形或灵活叶角分布的归一化差异植被指数，而不是卫星 LAI 和Ω. 我们还观察到非矩形双曲线和 TL 的倒数光响应函数之间的相似性能。当最大 LUE 未区分阳光照射和阴影叶子时，模型会降级，但当光饱和度相同时则不会。尽管存在功能差异，但最好的五个 TL 一致认为阴影叶面积对总 GPP 的贡献更大，这是由于更高的 LAI 和 LUE。总体而言，这些结果表明与 BL 相比，TL 的选择边际但稳健。