A canopy radiative transfer model suitable for heterogeneous Agro-Forestry scenes

Yelu Zeng, Jing Li, Qinhuo Liu, Gaofei Yin, Baodong Xu, Weiliang Fan, Jing Zhao
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引用次数: 1

Abstract

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer models for predicting the surface reflectance. This paper developed an analytical Radiative Transfer model for heterogeneous Agro-Forestry scenes (RTAF). The scattering contribution of the non-boundary regions can be estimated from the SAILH model as homogeneous canopies, whereas that of the boundary regions is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multi-angular airborne observations and Discrete Anisotropic Radiative Transfer (DART) model simulations were used to validate and evaluate the RTAF model over an agro-forestry scene in Heihe River Basin, China. The results suggest the RTAF model can accurately simulate the hemispherica-directional reflectance factors (HDRFs) of the heterogeneous scenes in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band, and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g. leaf area index) from remote sensing data over heterogeneous agro-forestry scenes.
适用于异质农林场景的冠层辐射转移模型
景观异质性是一种常见的自然现象,但目前用于预测地表反射率的辐射传输模型很少考虑景观异质性。本文建立了一个非均质农林场景辐射转移分析模型。非边界区域的散射贡献可以通过SAILH模型作为均匀冠层来估计,而边界区域的散射贡献则是基于考虑不同斑块之间相互作用和相互遮蔽效应的双向间隙概率来计算的。利用多角度航空观测和离散各向异性辐射传输(DART)模型模拟,对黑河流域农林业场景的RTAF模型进行了验证和评价。结果表明,RTAF模型能较准确地模拟非均匀场景在红、近红外波段的半球定向反射因子(hdrf)。边界效应会显著影响HDRF的角度分布,从而扩大HDRF在前后方向上的变化。与目前广泛使用的优势覆盖型(DCT)和光谱线性混合(SLM)模型相比,RTAF模型在红光波段的最大相对误差从25.7% (SLM)和23.0% (DCT)降低到9.8%,在近红外波段的最大相对误差从19.6% (DCT)和13.7% (SLM)降低到8.7%。RTAF模型为改进异质农林业场景遥感数据中生物物理参数(如叶面积指数)的检索提供了一种有希望的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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