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【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。
【Objective】 The objective of this study was to consider the problem of different degrees of saturation and susceptibility to soil background when LAI was retrieved using single vegetation index (VI) Good VI combination to improve the accuracy of LAI inversion. 【Method】 According to the simulation data of ACRM radiation transmission model and the spectral data of the ground, the commonly used vegetation index was selected for soil sensitivity analysis and saturation analysis to determine the LAI segmentation points. Based on this, the optimal vegetation index The formation of a combination of VI to achieve the final inversion of LAI, and Lee Landsat5TM carry out LAI inversion of winter wheat under regional conditions. 【Result】 LAI = 3 was the most suitable segmentation point. Using the best segmentation index of vegetation index, OSAVI (LAI≤3) + TGDVI (LAI> 3) could effectively overcome the soil influence factors and the saturation problems , The result of joint inversion is clearly better than the inversion accuracy of single vegetation index. 【Conclusion】 The formation of the joint VI by selecting the best vegetation index by stages can effectively improve the accuracy of LAI inversion.