Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (N_mass) and phosphorus concentrations (P_mass) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of N_mass and P_mass. We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for N_mass (R²_cv 0.31 vs. 0.41, % RSME_cv 3.3 vs. 3.0) and P_mass (R²_cv 0.45 vs. 0.63, % RSME_cv 15.3 vs. 12.5). The predictive performances of N_mass models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R²_cv values ranged from 0.26 for N_mass to 0.54 for P_mass indicating considerable covariation between biochemical traits and forest structural properties. N_mass was negatively related to the spatial heterogeneity of canopy density, whereas P_mass was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents.

成像光谱是在冠层水平上绘制化学叶片性状的强大工具。但是，与结构冠层特性的协方差妨碍了预测结构异类森林中叶片生化特性的能力。在这里，我们使用成像光谱数据绘制了温带混交林的冠层水平叶片氮（N _mass）和磷浓度（P _mass）的图。通过整合来自机载激光扫描（LiDAR）的预测变量，捕获冠层的生物物理复杂性，我们旨在改善对N_质量和P_质量的预测。我们使用偏最小二乘回归（PLSR）模型将两个叶片成分的群落加权平均值与245个高光谱带（426–2425 nm）和38个LiDAR衍生变量联系起来。LiDAR衍生的变量改善了模型的N_质量（R ² _cv 0.31对0.41，％RSME _cv 3.3对3.0）和P_质量（R ² _cv 0.45对0.63，RSME _cv 15.3对12.5）的解释方差。N_质量的预测性能仅使用高光谱谱带的模型随着校准数据集中包含的结构异质性的增加而降低。为了测试冠层结构的独立贡献，我们仅使用LiDAR派生变量作为预测变量来拟合模型。所得的R ² _cv值范围从N_质量为0.26到P_质量为0.54，表明生化性状和森林结构特性之间存在很大的协变。N_质量与冠层密度的空间异质性负相关，而P_质量与林分高度和树冠的总覆盖率呈负相关。在这项研究的特定设置中，结构变量的重要性可以归因于两种树种的存在，它们具有不同于共生树种的结构和生化特性。尽管如此，在叶和冠层水平上结构与生物化学之间现有的功能联系表明，用作代理的冠层结构总体上可以支持在较宽的空间范围内绘制叶生物化学的图谱。