We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48°–62°N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450–2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.

我们报告了一个新版本和基于光子重新碰撞概率 ( p )的森林反射模型的经验评估。第一次，一个p基于森林反射率建模的方法在来自不同生物群落的各种不同结构的森林中进行了测试。为了对模型进行参数化，我们测量了欧洲从北方到温带生物群落（北纬 48°–62°）的四个研究地点的 50 个森林地块的森林冠层结构和光谱特征。我们将模拟的森林反射光谱与波长为 450-2200 nm 的空中高光谱数据进行了比较。当模型参数化时仅考虑叶子或针叶作为植物元素并假设 Lambertian 冠层，发生了很大的高估，尤其是在近红外区域。针叶林、阔叶林和混交林的模型均方根误差 (RMSE) 分别平均为 80%、80%、54%。我们建议采用新的参数化方法，将冠层的最低点与半球反射率之比以及木质元素对森林反射率的贡献考虑在内。我们评估了基于模型反演的新参数化，这导致针叶林、阔叶林和混交林的平均 RMSE 为 20%、15% 和 11%。该模型只需要很少的结构参数和树叶、木质元素和森林地面的光谱作为输入。它可用于解释多光谱和高光谱遥感数据，以及地表和气候建模。总的来说，我们的结果还表明，即使针叶林和阔叶林之间的树叶光谱没有显着差异，