Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (C_ab) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to C_ab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to C_ab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating C_ab, while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables’ spectral response, leading to inconsistencies in the VI- C_ab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

了解生物物理和生化变量如何影响植被冠层的光谱特征对于监测至关重要。然而，由于一些外部因素，例如冠层背景材料的光谱变异性，包括土壤/作物残留物水分、土壤类型和非光合植被（NPV），量化这些贡献仍然很困难。本研究重点通过对类小麦冠层光谱的全局敏感性分析，探索不同冠层背景下两个重要农艺变量（1）叶片叶绿素含量（C _ab ）和（2）叶面积指数（LAI）的光谱响应基于物理的 PROSAIL 辐射传输模型。我们的结果揭示了以下一般发现：（1）每个农艺变量对模拟冠层光谱特征的贡献随背景光学特性变化很大；(2) 土壤类型和NPV对冠层对Cab和LAI光谱响应的影响比土壤/作物残茬水分的影响更显着_；(3) 560 和 704 nm 的光谱带对C _ab仍然敏感，同时受 NPV、土壤类型和湿度变化的影响最小；(4) 近红外 (NIR) 光谱带仅在土壤/农作物残留物湿度的情况下对 LAI 表现出较高的敏感性和较低的背景效应，但受土壤类型和 NPV 的影响相对较强。对 12 个广泛使用的植被指数与农艺变量的相关性进行比较分析表明，LICI（LAI 不敏感叶绿素指数）和 Macc01（Maccioni 指数）在估计C _ab方面更有效，而 OSAVI（优化土壤调整植被指数）和 MCARI2（修改后的叶绿素吸收比指数 2) 是模拟背景变异下更好的 LAI 预测因子。总体而言，我们的结果强调背景反射率变异性在农艺变量的光谱响应中引入了相当大的差异，导致 VI- C _ab /-LAI 关系的不一致。进一步的研究应该整合这些光谱响应度的结果来开发特定性状的高光谱反演模型。