Numerous studies have attempted reflectance-based estimations of leaf photosynthetic capacity parameters using different statistical approaches. Although increasing attention has been paid to selecting effective variables for data-driven methods to assess vegetation parameters, there has been less attention to the estimation of leaf photosynthetic capacity. The primary objective of this study is to examine the potential of selecting effective variables for machine learning techniques to estimate leaf photosynthetic capacity in a typical temperate deciduous species (Fagus crenata Blume) from leaf hyperspectral reflectance or its spectral transformations, with or without additional leaf traits. The least absolute shrinkage and selection operator (LASSO) method was coupled to two machine learning models to extract the effective variables for assessing two key leaf photosynthetic parameters (V_cmax and J_max). The results showed that two support vector machine (SVM) models, successfully extracted the effective bands by utilizing the LASSO method from the Der-Log-VIs-Traits and Der-Traits, exhibited the best performance for V_cmax (R² = 0.66, RMSE = 9.73 μmol m⁻² s⁻¹, RPD = 1.72, and AIC = 524.86) and J_max (R² = 0.65, RMSE = 18.33 μmol m⁻² s⁻¹, RPD = 1.68, and AIC = 567.39), respectively, suggesting that the LASSO method can effectively locate important photochemistry variables from hyperspectral data. The models also performed better when based on the first-order derivative spectra, rather than from original or apparent absorption spectra. Furthermore, the results also revealed that the NIR and SWIR spectral regions are important for estimating leaf photosynthetic capacity. This study provides a useful reference for estimating leaf photosynthetic capacity from full-spectrum (visible through shortwave infrared) hyperspectral reflectance data.

许多研究尝试使用不同的统计方法基于反射率估计叶片光合能力参数。尽管人们越来越关注为数据驱动方法选择有效变量来评估植被参数，但人们对叶片光合能力的估计关注较少。本研究的主要目的是检验为机器学习技术选择有效变量以估计典型温带落叶树种（Fagus crenata）叶片光合能力的潜力Blume）来自叶片高光谱反射率或其光谱变换，有或没有额外的叶片特征。将最小绝对收缩和选择算子 (LASSO) 方法与两个机器学习模型相结合，以提取用于评估两个关键叶片光合参数（V _cmax和J _max）的有效变量。结果表明，两种支持向量机 (SVM) 模型利用 LASSO 方法从 Der-Log-VIs-Traits 和 Der-Traits 中成功提取了有效波段，表现出最佳的 V cmax ( R ₂ = ^0.66  , RMSE = 9.73 μmol m ⁻² s ⁻¹，RPD = 1.72，AIC = 524.86) 和J_max（R ²  = 0.65，RMSE = 18.33 μmol m ⁻² s ⁻¹，RPD = 1.68，AIC = 567.39），表明 LASSO 方法可以有效地从高光谱数据中定位重要的光化学变量。当基于一阶导数光谱而不是原始或表观吸收光谱时，模型也表现更好。此外，结果还表明，NIR 和 SWIR 光谱区域对于估算叶片光合能力很重要。该研究为从全光谱（可见光到短波红外）高光谱反射率数据估算叶片光合能力提供了有用的参考。