Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R2 = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R2 = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R2 = 0.89, RMSE = 12.83 μg/cm2), equivalent water thickness (R2 = 0.90, RMSE = 0.0032 g/cm2), and leaf mass per area (R2 = 0.38, RMSE = 0.0031 g/cm2), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R2 = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.

中文翻译：

---

用于表征叶片反射光度和偏振特性的辐射传输模型：PROSPECT 和偏振反射函数的组合


光光度和偏振特性对于描述叶片反射的光学特性至关重要，在研究植被与大气系统之间的生化和表面结构性状反转和辐射平衡中起着至关重要的作用。尽管有几种物理模型可用，但对同时考虑光度和偏振特性并结合生化和表面结构特征的综合模型的研究仍然不足。在这项研究中，我们介绍了 PROPOLAR，这是一种叶模型，它根据偏振和非极化成分考虑叶反射，并将叶反射与叶性状联系起来。PROPOLAR 采用 PROSPECT 模拟与生化特性相关的非极化成分，同时使用三参数函数 （线性系数、折射率因子和叶表面粗糙度） 来模拟极化成分。该模型使用从各种照明观察几何结构下的 13 种植物物种的 533 个样本中收集的数据集（由光度和偏振测量组成）进行验证。结果表明，PROPOLAR 在模拟光强度 （R2 = 0.98） 方面优于 PROSPECT 和 PROSPECULAR（表征 BRF 的叶子模型），并在较宽的光谱范围 （450-2300 nm） 和物种上有效模拟双向偏振反射因子 （BPRF） 和线性偏振度 （Dolp），R2 = 0.92 和 0.80。此外，PROPOLAR 提高了 PROSPECT 的准确性，并在多角度极化测量的生化特性反转方面显示出与 PROSPECULAR 相当的准确性，包括叶绿素 （R2 = 0.89，RMSE = 12.83 μg/cm2）、等效水厚度 （R2 = 0.90，RMSE = 0.0032 g/cm2）和单位面积叶片质量（R2 = 0.38，RMSE = 0.0031 g/cm2），这是由于在校准过程中加入了偏振反射和线性系数。值得注意的是，PROPOLAR 可以反转粗糙度，并与测量的粗糙度显示出合理的一致性 （R2 = 0.61）。这些结果表明 PROPOLAR 在模拟叶片反射的光度和极化特性方面的有效性，以及其生化和表面结构性状反转的潜力。PROPOLAR 可以通过整合光度和极化特性来推进遥感在植被管理中的应用。