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Assessing the effects of the clumping phenomenon on BRDF of a maize crop based on 3D numerical scenes using DART model

机译:使用DART模型基于3D数值场景评估成团现象对玉米作物BRDF的影响

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Inverting radiative transfer (R-T) models against remote sensing observations to retrieve key biogeophysical parameters such as leaf area index (LAI) is a common approach. Even if new inversion techniques allow the use of three-dimensional (3D) models for that purpose, one-dimensional (1D) models are still widely used because of their ease of implementation and computational efficiency. Nevertheless, they assume a random distribution of foliage elements whereas most canopies show a clumped organization. Due to that crude simplification in the representation of the canopy structure, sizeable discrepancies can occur between 1D simulations and real canopy reflectance, which may further lead to false LAI values. The present investigation aims to appraise to which extent the incorporation of a clumping index (noted lambda) into 1D R-T model could improve the simulations of Bidirectional Reflectance Distribution Function (BRDF). Canopy BRDF is simulated here for three growth stages of a maize crop with the Discrete Anisotropic Radiative Transfer (DART) model in the visible and near infrared spectral bands, for two contrasted soil types (dark and bright) and different levels of heterogeneity to represent the canopy structure. 3D numerical scenes are based on in-situ structural measurements and associated BRDF simulations are thus considered as references. 1D scenarios assume either that leaves are randomly distributed (lambda=1) or clumped (lambda<1). If BRDF simulations seem globally reliable under the assumption of a random distribution in near infrared, it can also lead to relative errors on the total BRDF up to 30% in the red spectral band. It comes out that the use of a clumping index in a 1D reflectance model generally improves BRDF simulations in the red considering a bright soil, which seems relatively independent of LAI. In the near infrared, best results are usually obtained with homogeneous canopies, except with the dark soil. Clearly, influent factors are mainly the LAI and the spectral contrast between soil and leaves.
机译:逆转遥感观测的辐射转移(R-T)模型以检索关键的生物地球物理参数(例如叶面积指数(LAI))是一种常见的方法。即使新的反演技术允许为此目的使用三维(3D)模型,由于其易于实现和计算效率,一维(1D)模型仍被广泛使用。然而,它们假定叶子元素是随机分布的,而大多数冠层显示出结块的组织。由于冠层结构表示中的粗略简化,一维模拟与实际冠层反射率之间可能会出现相当大的差异,这可能进一步导致错误的LAI值。本研究旨在评估将聚集指数(记为lambda)纳入一维R-T模型的程度可以改善双向反射分布函数(BRDF)的仿真。这里使用可见光和近红外光谱带中的离散各向异性辐射转移(DART)模型对玉米作物的三个生长阶段模拟冠层BRDF,针对两种对比的土壤类型(暗和亮)和不同水平的异质性来表示冠层结构。 3D数字场景基于现场结构测量,因此将相关的BRDF模拟视为参考。一维方案假设叶子是随机分布的(lambda = 1)或成簇的(lambda <1)。如果在近红外随机分布的假设下,BRDF模拟在全球范围内似乎是可靠的,那么它也可能导致总BRDF的相对误差高达红色光谱带的30%。结果表明,在一维反射模型中使用聚集指数通常会改善考虑到明亮土壤的红色BRDF模拟,这似乎相对独立于LAI。在近红外条件下,除了深色土壤外,通常使用均质冠层可获得最佳效果。显然,影响因素主要是LAI和土壤与叶片之间的光谱对比。

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