中文摘要：

植被覆盖度是重要的生态学参数，对水文、生态、全球变化等研究具有重大意义。目前使用的目测估算法和数码照相法都具有一定的主观性，另外通过自然界中相似样方的大量测量获得稳定的统计规律具有很大的难度，因此建立叶面积指数和植被覆盖度之间的统计模型是估算植被覆盖度的有效方法。本文以大豆为例，利用椭圆来模拟大豆的叶片，选取大豆植株结构的关键参数，通过随机分布函数来模拟植株叶片位置、倾角和大小的分布，获得不同植被结构参数下单位面积上的植被覆盖度，建立植被覆盖度计算机模拟模型。通过实测数据和理论研究结论来验证模拟结果。对模型的参数敏感性进行分析结果表明，叶半短轴是比叶半长轴更为敏感的植被结构参数。该模型为植被覆盖度的研究提供了一种新的思路和方法。

英文摘要：

Fractional Vegetation Cover （FVC） is an important ecology parameter, which is essential in the studies of hydrology, ecology, and global variation. Currently, the estimation methods used for FVC, including eyeballing method and digital camera imagery interpretation method, are obviously subjective and uncertain. Furthermore, it is rather difficult for the statistical rela- tionship between FVC and leaf area index （LAI） to establish by measurement of millions of samples that have similar vegetation structure parameters. Thus, it is an effective way for the estimation of FVC to develop a statistical model between FVC and LAL In the paper, we simulated the soybean leaves using ellipses, and determined the position, orientation and size with random distribution function by choosing the key parameters in the soybean structure to obtain the FVC per area under different vegetation structure parameters. The model was validated with data measured in situ and the theoretical conclusion. The analysis of parameter sensitivity of the simulation model showed that the length of stem is not a sensitive parameter when it was longer than foliage interval; wheresa the angle of stem is not a sensitive parameter until it reache a threshold. The leaf tilt angle and foliage interval were in inverse portion to the fractional vegetation cover, while the semimajor and semiminor axis of leaf were in direct portion to the fractional vegetation cover. The semiminor axis of leaf was a much more sensitive parameter than semimajor axis of leaf. It suggested that it was a novel and feasible way for FVC.