سابِق|تال

Analysis of the sensitivity of the anisotropic flat index to vegetation parameters based on the two-layer canopy reflectance model

  • role： First author第一作者
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

  • Email：279085823@qq.com
  • Introduction：白冬妮(1991—    )，女，硕士研究生，研究方向为定量遥感、地表二向性反射和核驱动模型与ACRM辐射传输模型的耦合。E-mail：279085823@qq.com
BAI Dongni1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

    State Key Laboratory of Remote Sensing Science Beijing Normal University, Beijing 100875, China

    Key Laboratory of Environmental Remote Sensing and Digital City, Beijing 100875, China

  • Email：jiaozt@bnu.edu.cn
  • Introduction：焦子锑(1970—    )，男，副教授，研究方向为定量遥感、地表各向异性反射的建模与应用，多角度信号用于地表分类。E-mail： jiaozt@bnu.edu.cn
JIAO Ziti123*,  
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

DONG Yadong1,  
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

ZHANG Xiaoning1,  
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

LI Yang1,  
  • Affiliation：

    School of Geography, Beijing Normal University, Beijing 100875, China

HE Dandan1

ملخص

Bidirectional Reflectance Distribution Function (BRDF) contains directional information on surface reflectance and provides important orientation information in calculating the surface time-space variable elements. The analysis of the sensitivity of BRDF shapes to vegetation structure parameters contributes to understanding the laws of vegetation bidirectional reflectance and increasing the accuracy in inversing vegetation parameters. Numerous scholars have investigated different BRDF models to determine the relationship between vegetation structure parameters and bidirectional reflectance distribution effects of the vegetation surface. Among these models, the two-layer canopy reflectance model (ACRM model) and kernel-driven model are generally accepted as physical and semiempirical models, respectively.This study explores the sensitivity of BRDF shapes to vegetation structure parameters by coupling the ACRM model and kernel-driven Ross-Thick/Li-Sparse-Reciprocal model (RTLSR model). The adopted sensitivity analysis method is the extended Fourier amplitude sensitivity test, one of the global sensitivity analysis methods. The Anisotropic Flat indeX (AFX) is employed as the metric for BRDF shapes to explore the sensitivity change in AFX to vegetation parameters under the condition of different SKY Light ratios (SKYL) and sensitivity with SKYL=0.1. From these variables, this study explores the sensitivity parameters with SKYL=0.1. Then, the relationship between the main sensitivity parameters and AFX is analyzed.Overall, AFX can indicate the change in BRDF shapes. The kernel-driven RTLSR model can fit the data produced by the ACRM model well. Results of the analysis of sensitivity indicate that the change in sky light results in different degrees of sensitivity. Given the influence of sky light, parameter sensitivity at the canopy scale is larger than that at the leaf scale. Under sunny conditions (SKYL=0.1), the total order sensitivity index of the upper Leaf Area Index (LAI) is strongest in the red band, and lower LAI is observed in the near-infrared band. The first-order sensitivity indices of the upper LAI, upper leaf angle distribution, and lower leaf structure parameters are stronger in the red band. Meanwhile, the first-order sensitivity indices of the upper and lower LAI are stronger in the near-infrared band.AFX plays an important role in increasing inversion accuracy. The index is significant in understanding the relationship between AFX and the surface-covering physical parameters for the inversion of the parameters. This study analyzed the sensitivity of AFX to vegetation structure parameters. The results will help researchers understand the effects of changes in vegetation parameters on BRDF shapes.

مفهوم

ACRM model;kernel-driven model;AFX;vegetation structure parameter;sensitivity analysis

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