中文摘要：

选用南京市SPOT5 HRG图像的地物反射率（PAC）、表观反射率（TOA）、星上辐射率（SR）和灰度值（DN）影像,提取了2种植被指数（VI）,即归一化植被指数（NDVI）和比值植被指数（RVI）,与地面实测的植被叶面积指数（LAI）进行了相关分析,并建立了157个LAI-VI关系模型。结果显示：LAI与VI呈显著的正相关关系（r=0.303~0.927,p〈0.01）,对应不同植被的优选模型自变量包括了3个辐射校正水平的2种植被指数,可见基于不同辐射校正水平的植被指数在LAI遥感反演中具有一定的应用潜力。这些优选模型为：阔叶林：LAI=-3.345＋5.378RVISR＋7.329NDVISR（R2=0.818,RMSE=0.527）,针阔混交林：LAI=1.696＋17.076NDVIDN＋137.684（NDVIDN）2-288.240（NDVIDN）3（R2=0.919,RMSE=0.440）,灌木：LAI=-0.065＋19.112NDVISR-113.820（NDVISR）2＋184.207（NDVISR）3（R2=0.900,RMSE=0.448）,草地：LAI=-5.905＋6.446RVISR＋9.477NDVISR（R2=0.944,RMSE=0.378）,植被总体：LAI=-1.615＋7.199NDVIDN＋2.640NDVISR＋2.105RVIPAC（R2=0.801,RMSE=0.668）。研究表明,基于不同植被类型、不同辐射校正水平影像的LAI遥感估算有利于充分挖掘遥感影像信息,进而提高LAI估算的精度。

英文摘要：

Based on the images of post atmospheric correction reflectance（PAC）,top of atmosphere reflectance（TOA）,satellite radiance（SR） and digital number（DN） of a SPOT5 HRG remote sensing image of Nanjing,China,two vegetation indices（VI）,i.e.,normalized difference vegetation index（NDVI）,and ratio vegetation index（RVI） were derived,and compared with the leaf area index（LAI） data acquired from field measurement.A total of 157 LAI-VI relationship models were established.The results show that LAI was positively correlated with VI（r=0.303~0.927,p0.01）.Independent variables of the optimal models corresponding to various vegetations included 2 vegetation indices at 3 radiometric correction levels,indicating potentials of vegetation indices based on different radiometric correction levels in LAI remote sensing retrieval.These optimal models included broad-leaf forest：LAI=-3.345＋5.378RVISR＋7.329NDVISR（R2=0.818,RMSE=0.527）;conifer-broad-leaf forest：LAI=1.696＋17.076NDVIDN＋137.684（NDVIDN）2-288.240（NDVIDN）3（R2=0.919,RMSE=0.440）;shrub：LAI=-0.065＋19.112NDVISR-113.820（NDVISR）2＋184.207（NDVISR）3（R2=0.900,RMSE=0.448）;grass：LAI=-5.905＋6.446RVISR＋9.477NDVISR（R2=0.944,RMSE=0.378）;and total vegetation：LAI=-1.615＋7.199NDVIDN＋2.640NDVISR＋2.105RVIPAC（R2=0.801,RMSE=0.668）.The study demonstrates that LAI remote-sensing estimation of various types of vegetation based on images of different radiometric correction levels contributes to tapping of valuable information from remote sensing images,thus improving accuracy of LAI estimation.