研究植被指数与光合有效辐射吸收比FPAR的定量关系对于提高FPAR反演精度与指导生产实践具有一定的参考价值。研究在三维辐射传输模型LESS基础上发展了一个兼具一维模型简洁和三维模型精度优势的LESS1D模块（已随LESS模型正式发布，www.lessrt.org）；探究随机均匀场景和三维异质场景中植被冠形、盖度等7种因素对6种植被指数与FPAR_green关系的影响。结果表明：①在均质性场景中，NDVI、SAVI、EVI对FPAR_green拟合相对最优，而在异质性场景中，则为NDVI和RVI。②在异质性场景中，不同冠形下FPAR_green与植被指数的拟合精度为圆柱形>椭球形>圆锥形；植被盖度较低时，植被指数对FPAR_green拟合精度较差；随着太阳天顶角增大，RVI与FPAR_green由线性关系变为指数关系。 结论 树冠体积和树冠几何结构是不同冠形影响FPAR_green大小的关键因素，而叶片聚集度、植被盖度和植被指数类型则是影响植被指数饱和效应的相关因素。

The quantitative relationship between FPAR（Fraction of Absorbed Photosynthetically Active Radiation）and vegetation indices has certain reference value for improving FPAR inversion accuracy and guiding production practice. Based on the three-dimensional radiative transfer model LESS， a module named LESS1D （formally released with LESS though www.lessrt.org） with advantages of simplicity of 1D model and accuracy of 3D model is proposed. Based on this model， the influences of vegetation canopy， coverage and other factors on the relationship between FPAR_green and 6 vegetation indices were explored in random homogeneous scenes and 3D heterogeneous scenes. The results showed that in homogeneous scenarios， NDVI， SAVI and EVI fit FPAR_green best in homogeneous scenarios， while NDVI and RVI fit FPARgreen best in heterogeneous scenarios. In heterogeneous scenes， the fitting accuracy of FPAR_green and vegetation index under different crown shapes is cylindrical > ellipsoidal > conical； When the vegetation coverage is low， the fitting accuracy of vegetation indices to FPARgreen is poor； As the solar zenith angle increases， the relationship between RVI and FPAR_green changes from linear to exponential. Canopy volume and canopy geometry are the key factors affecting the size of FPAR_green with different crown shapes， while leaf aggregation， vegetation coverage and vegetation index type are the relevant factors affecting the saturation effect of vegetation index.