冠层反射率在森林植被类型精确解译、森林碳同化关键参数如叶面积指数(LAI)、叶绿素等遥感反演等方面具有重要意义.本研究以亚热带毛竹林、雷竹林和常绿落叶阔叶混交林3种典型森林类型为研究对象,通过耦合PROSPECT5和4SAIL模型模拟其冠层反射率时间序列.首先,对PROSPECT5和4SAIL模型参数进行敏感性分析,探讨模型参数对冠层反射率的影响;其次,利用实测反射率对不敏感参数进行优化,并确定其参数值;最后,耦合PROSPECT5和4SAIL模型模拟3种亚热带森林冠层反射率,并与MODIS反射率进行对比.结果表明:LAI对第1、2、3、5、7波段最敏感,各波段的总敏感指数分别为0.80、0.83、0.94、0.66、0.47;叶绿素含量对第4波段最敏感,总敏感指数为0.59;叶片含水量对第6波段的敏感性最大,总敏感性指数为0.54;叶子结构参数、类胡萝卜素、热点参数、干物质含量和土壤干湿比等参数对各个波段都不敏感或敏感性较小.优化后的PROSPECT5和4SAIL模型模拟得到的冠层反射率能够真实反映3种典型森林的季节性变化规律,通过与MODIS反射率对比分析发现,模拟冠层反射率和MODIS反射率之间具有较高的决定系数,分别为0.86、0.90、0.93,均方根误差(RMSE)也较小,分别为0.09、0.07、0.05,且模拟反射率能在一定程度上解决MODIS反射率数据冬季易受雨雪、混合像元影响等问题. Canopy reflectance is of great importance in the precise interpretation of the types of forest vegetation, the key parameters of forest carbon assimilation such as leaf area index (LAI), chlorophyll and other remote sensing.In this study, the subtropical moso bamboo forests, Phyllostachys pubescens forest and evergreen deciduous Leaf mixed forest, the canopy reflectance time series was simulated by coupling the PROSPECT5 and 4SAIL models.Firstly, the sensitivity of PROSPECT5 and 4SAIL model parameters was analyzed to explore the influence of model parameters on canopy reflectance Secondly, the insensitive parameters were optimized and the parameters were determined by the measured reflectivity. Finally, the canopy reflectance of three subtropical forests was simulated by using the coupled PROSPECT5 and 4SAIL models, and compared with the MODIS reflectivity. The results showed that: The first, second, third, fifth and seventh bands were the most sensitive, the total sensitive index of each band was 0.80,0.83,0.94,0.66,0.47 respectively; chlorophyll content was the most sensitive to the fourth band, the total sensitive index was 0.59; leaf water content The sensitivity to the sixth wave band was the highest, with a total sensitivity index of 0.54. The leaf structure parameters, carotenoids, hotspot parameters, dry matter content, Neither the sensitivity nor the sensitivity is smaller.The canopy reflectance simulated by optimized PROSPECT5 and 4SAIL models can truly reflect the seasonal variation of three typical forests.By comparing with MODIS reflectance, it is found that canopy reflectance There was a high coefficient of determination between MODIS reflectance and MODIS, which were respectively 0.86, 0.90 and 0.93. RMSE was also smaller, which were 0.09, 0.07 and 0.05 respectively, and the simulated reflectivity could to a certain extent Solve the MODIS reflectivity data in winter vulnerable to rain and snow, the impact of mixed pixels and other issues.