The definition of LAI (Leaf Area Index) is important when deriving it from reflectance observation for model application and validation. Canopy reflectance and the corresponding quantities of LAI, PAI (Plant Area Index), GAI (Green Area Index) and effective GAI (GAI_eff) are first calculated using a 3D radiative transfer model (RTM) applied to 3D wheat and maize architecture models. A range of phenological stages, leaf optical properties, soil reflectance, canopy structure and sun directions is considered. Several retrieval methods are compared, including vegetation indices (VIs) combined with a semi-empirical model, and 1D or 3D RTM combined with a machine learning inversion approach. Results show that GAI_eff is best estimated from remote sensing observations. The RTM inversion using a 3D model provides more accurate GAI_eff estimates compared with VIs and the 1D PROSAIL model with RMSE = 0.33 for wheat and RMSE= 0.43 for maize. GAI_eff offers the advantage to be easily accessible from ground measurements at the decametric resolution. It was therefore concluded that the most efficient retrieval approach would be to use machine learning algorithms trained over paired GAI_eff and the corresponding canopy reflectance derived either from realistic 3D canopy models or from experimental measurements.

LAI（叶面积指数）的定义在从反射率观测推导模型应用和验证时非常重要。首先使用应用于 3D 小麦和玉米结构模型的 3D 辐射传输模型 (RTM) 计算冠层反射率和 LAI、PAI（植物面积指数）、GAI（绿化面积指数）和有效 GAI（GAI _{eff ）的相应量。}考虑了一系列物候阶段、叶片光学特性、土壤反射率、冠层结构和太阳方向。比较了几种检索方法，包括植被指数 (VI) 与半经验模型相结合，以及 1D 或 3D RTM 与机器学习反演方法相结合。结果表明，GAI _eff最好从遥感观测中估计。与 VI 和 1D PROSAIL 模型相比，使用 3D 模型的 RTM 反演提供了更准确的 GAI _eff估计，其中小麦的 RMSE = 0.33，玉米的 RMSE = 0.43。GAI _eff提供的优势是可以从十米分辨率的地面测量中轻松访问。因此得出的结论是，最有效的检索方法是使用在配对 GAI _eff上训练的机器学习算法和从现实 3D 冠层模型或实验测量得出的相应冠层反射率。