中文摘要：

【目的】为利用遥感技术定量提取区域尺度的阔叶林叶面积指数前的大气校正模型选择提供科学依据。【方法】分别利用6S模型、FLAASH模型和ATCOR2模型对Landsat 8 OLI影像进行了大气校正，分析了3种模型下的阔叶林叶面积指数（ LAI）与多种植被指数（ VI）相关性，建立了LAI-VI的线性和非线性的回归模型，最后通过验证数据组LAI预测值（ Y）与LAI实测值（ X）的均方根误差（ RMSE）及线性相关性大小对阔叶林LAI遥感估算结果进行了精度对比。【结果和结论】ATCOR2模型不适于阔叶林LAI-VI的回归建模；除比值植被指数（ RVI）外，FLAASH模型与6S模型下的阔叶林LAI与增强型植被指数（EVI）、修正土壤调节植被指数（MSAVI）有较好的相关性，其中FLAASH模型下的阔叶林LAI-MSAVI幂函数模型拟合优度最佳；FLAASH模型的阔叶林LAI估算精度优于6S模型；借助遥感技术定量提取植被生理参数时，应慎重选择适宜的大气校正模型。

英文摘要：

[Objective]This study aimed to provide a scientific basis for selecting the atmospheric correc-tion model prior to the quantitative extraction of leaf area index of broadleaved forest at a regional scale using remote sensing.[Method]6S model, FLAASH model, and ATCOR2 model were used respectively on Landsat 8 OLI image for the atmospheric correction to analyze the correlation of these three kinds of leaf area index （ LAI） of broadleaved forest and a variety of vegetation index （ VI） , establishing the line-ar and nonlinear regression model of LAI-VI.The root mean square error and correlation of validation da-ta set of LAI predicted value （ Y） and the LAI measured values （ X） were calculated .[Result and con-clusion]The ATCOR2 model was not suitable for building broadleaved forest LAI-VI regression model;in addition to the RVI, for FLAASH model and 6S model, LAI of broadleaved forest had a good correlation with EVI, MSAVI.Among them the power function model of LAI-MSAVI with FLAASH model yield the best goodness of fit .LAI estimation precision of FLAASH model was superior to the 6S model for broad-leaved forest .With the aid of remote sensing technology to quantitatively extract vegetation physiological parameters, suitable atmospheric correction model should be selected prudently .