The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system.

A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

冠层双向反射分布函数 (BRDF) 在估计植物的生物物理参数方面发挥着关键作用，而土壤背景各向异性为其检索带来了挑战。土壤光学特性影响冠层各向异性特征，尤其是在开放冠层区域。然而，由于复杂森林生态系统中信息提取的困难和不同的光照条件，背景各向异性的遥感具有挑战性。本研究开发了一种有效的摄影测量技术，从基于无人机 (UAV) 的多角度图像中提取背景土壤双向反射系数 (BRF)，并验证在冠层辐射传输建模中对准确土壤各向异性信息的需求。土壤 BRF 剖面是在三个开放冠层样本地块上测量的，这些样地来自用六轴飞行器收集的多角度遥感多光谱图像。作为验证，参考土壤 BRF 剖面由基于地面的多角度成像系统同步获取。

观测到的地面和无人机测量的土壤BRF之间的一致性很高，RMSE小于0.012。测量的土壤 BRF 的不确定性分析表明，大太阳斑中阳光照射的土壤和树叶元素之间的多重散射贡献不到 5%。这两个结果都表明，可以从无人机多角度测量中准确地提取土壤各向异性。为了明确证明使用土壤各向异性可以减少冠层辐射传递模拟中的不确定性，我们使用三维（3D）辐射传递模型，在不同土壤水分含量（SMC）水平下，用朗伯土壤和各向异性土壤对冠层BRF进行了模拟。 , 冠层覆盖 (CC) 水平和太阳天顶角 (SZA) 与模拟逼真的森林场景。我们发现 CC 越少，较低的 SZA 和较少的 SMC 导致土壤各向异性对冠层反射率的影响更显着；例如，在热点方向反射偏差达到 0.3。这说明忽略土壤各向异性会导致开放林冠层 BRF 建模中出现相当大的错误（即 CC 水平小于 0.5）。所提出的技术有助于表征各向异性森林背景土壤，这对于推进冠层辐射传输建模和验证以及植被参数的检索非常重要。CC 水平小于 0.5)。所提出的技术有助于表征各向异性森林背景土壤，这对于推进冠层辐射传输建模和验证以及植被参数的检索非常重要。CC水平小于0.5）。所提出的技术有助于表征各向异性森林背景土壤，这对于推进冠层辐射传输建模和验证以及植被参数的检索非常重要。