中文摘要：

【目的】考虑到利用单一植被指数（VI）反演叶面积指数（LAI）时，存在着不同程度的饱和性和易受土壤背景影响的问题，提出通过分段的方式选择敏感植被指数形成最佳VI组合以提高LAI反演的精度。【方法】通过ACRM辐射传输模型模拟数据，结合地面实测光谱数据，选择常用的植被指数进行土壤敏感性分析以及饱和性分析确定LAI的分段点，并在此基础上分段选择最佳植被指数形成组合VI来实现LAI的最终反演，并利Landsat5TM开展区域条件下冬小麦LAI反演应用。【结果】以LAI=3是较为适宜的分段点，利用植被指数最佳分段组合OSAVI（LAI≤3）＋TGDVI（LAI〉3）可在一定程度上有效克服土壤影响因素以及饱和性问题，联合反演的结果明确优于单一植被指数反演精度。【结论】通过分段选择最佳植被指数形成联合VI可以有效提高LAI反演精度。

英文摘要：

[Objective] The method of inversion leaf area index （LAI） using a single vegetation index （VI） is influenced by different degrees of saturability and soil background. This paper proposed a method choosing sensitive vegetation index by the segmentation method to form optimal VI combination, and to improve the accuracy of LAI inversion. [Method] In this study the ACRM radiation transmission model was used to simulate data, and the ground measured spectrum data were obtained. The study analyzed soil sensitivity and saturability about the common vegetation index to determine the segment point of LAI, and chose the best vegetation index based on segment point of LAI to form a combination VI for achieving the final inversion of the LAI. This method was also used in the regional winter wheat LAI inversion application with the Landsat5 TM data. [Result] The analysis showed that, LAI - 3 was the more appropriate segment point, and the use of vegetation index segment combination OSAVI （LAI 43） ＋ TGDVI （LAI〉3） partly overcame soil factors and the saturation problems. The joint inversion results were significantly superior to the single vegetation index retrieval accuracy. [Conclusion] LAI was effectively inversed with the higher accuracy by choosing the best vegetation index through the segmentation method.