利用PROSAIL前向模型模拟的植被冠层光谱, 在植被指数构造时, 引入修正大气、 土壤背景影响的蓝、 绿波段, 构建了避免过早饱和的环境植被指数(environmental vegetation index, EVI)。 基于多个典型冬小麦生育期的地面观测数据, 建立基于EVI-LAI长时间序列反演模型, 并对模型进行不同品种间的交叉检验。 研究表明, EVI建立的叶面积指数(leaf area index, LAI)反演模型精度优于同类植被指数模型, 并具有较好的普适性, 能应用于冬小麦遥感多时相长势监测及LAI反演。

The present study used the PROSAIL forward model to simulate vegetation canopy spectrum, introducing blue and green bands to amend the effects of atmosphere and soil background, and constructing HuanJing vegetation Index (HJVI) to avoid premature saturation. Based on ground observation data of different typical winter wheat, we established HJVI-LAI long time series inversion models and implemented different varieties cross-validation to the models. The results show that the LAI inversion model of HJVI has higher precision than similar vegetation index model, has good universality, and can be applied to remote sensing multi-temporal winter wheat growth monitoring and LAI inversion.