Hovi Aarne, Forsström Petri, Ghielmetti Giulia, Schaepman Michael E, Rautiainen Miina
Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00076 Aalto, Finland.
University of Zürich, Department of Geography, Remote Sensing Laboratories, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
ISPRS J Photogramm Remote Sens. 2020 Nov;169:57-72. doi: 10.1016/j.isprsjprs.2020.08.027.
Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require rather detailed informative priors on various aspects of vegetation and atmospheric status. Spectral invariants and photon recollision probability theories provide a solid theoretical framework for developing relatively simple models of forest canopy reflectance. Empirical validation of these theories is, however, scarce. Here we present results of a first empirical validation of a model based on photon recollision probability at the level of individual trees. Multiangular spectra of pine, spruce, and oak tree seedlings (height = 0.38-0.7 m) were measured using a goniometer, and tree hemispherical reflectance was derived from those measurements. We evaluated the agreement between modeled and measured tree reflectance. The model predicted the spectral signatures of the tree seedlings in the wavelength range between 400 and 2300 nm well, with wavelength-specific bias between -0.048 and 0.034 in reflectance units. In relative terms, the model errors were the smallest in the near-infrared (relative RMSE up to 4%, 7%, and 4% for pine, spruce, and oak seedlings, respectively) and the largest in the visible wavelength region (relative RMSE up to 34%, 20%, and 60%). The errors in the visible region could be partly attributed to wavelength-dependent directional scattering properties of the leaves. Including woody parts of tree seedlings in the model improved the results by reducing the relative RMSE by up to 10% depending on species and wavelength. Spectrally invariant model parameters, i.e. total and directional escape probabilities, depended on spherically averaged silhouette to total area ratio (STAR) of the tree seedlings. Overall, the modeled and measured tree reflectance mainly agreed within measurement uncertainties, but the results indicate that the assumption of isotropic scattering by the leaves can result in large errors in the visible wavelength region for some tree species. Our results help increasing the confidence when using photon recollision probability and spectral invariants -based models to interpret satellite images, but they also lead to an improved understanding of the assumptions and limitations of these theories.
在监测植被生物物理特征方面，基于物理的遥感方法比统计方法更具优势。然而，基于物理的模型仍然需要大量的计算资源，并且通常需要关于植被和大气状况各方面相当详细的信息先验知识。光谱不变量和光子再碰撞概率理论为开发相对简单的森林冠层反射率模型提供了坚实的理论框架。然而，这些理论的实证验证却很少。在此，我们展示了基于单个树木水平上光子再碰撞概率的模型的首次实证验证结果。使用测角仪测量了松树、云杉和橡树幼苗（高度 = 0.38 - 0.7米）的多角度光谱，并从这些测量中得出树木半球反射率。我们评估了模型预测的和实测的树木反射率之间的一致性。该模型在400至2300纳米波长范围内很好地预测了树木幼苗的光谱特征，反射率单位下特定波长的偏差在 -0.048至0.034之间。相对而言，模型误差在近红外区域最小（松树、云杉和橡树幼苗的相对均方根误差分别高达4%、7%和4%），在可见光波长区域最大（相对均方根误差高达34%、20%和60%）。可见光区域的误差部分可归因于叶片与波长相关的方向散射特性。在模型中纳入树木幼苗的木质部分，根据物种和波长的不同，相对均方根误差最多可降低10%，从而改善了结果。光谱不变量模型参数，即总逃逸概率和方向逃逸概率，取决于树木幼苗的球形平均轮廓与总面积之比（STAR）。总体而言，模型预测的和实测的树木反射率在测量不确定度范围内基本一致，但结果表明，叶片各向同性散射的假设可能会在可见光波长区域给某些树种带来较大误差。我们的结果有助于在使用基于光子再碰撞概率和光谱不变量的模型解释卫星图像时增强信心，但同时也有助于更好地理解这些理论的假设和局限性。
