植物的叶绿素含量指示了其健康状况。 大区域范围内植被叶绿素含量信息的提取可以用于评价植被的生长状况, 实现对生态环境的监测。 对于农田系统而言, 作物叶绿素含量的估测还可以对施肥等田间操作提供支持。 文章利用辐射传输模型模拟多组不同状态下的植被冠层光谱反射率, 通过对模拟数据的冠层叶绿素含量以及冠层光谱之间关系的分析, 构建了估测植被冠层水平叶绿素含量的光谱指数模型。 该模型对冠层叶绿素含量的方差解释量达到了75%以上。 分别使用野外实测冠层光谱和Hyperion高光谱遥感影像对试验区进行验证。 结果证明该模型对冠层水平的叶绿素含量估测效果较好, 具有应用价值。

Many researches have developed models to estimate chlorophyl content at leaf and canopy level, but they were species-specific. The objective of the present paper was to develop a new model. First, canopy reflectance was simulated for different species and different canopy architecture using radiative transfer models. Based on the simulated canopy reflectance, the relationship between canopy reflectance and canopy chlorophyll content was studied, and then a chlorophyll estimation model was built using the method of spectral index. The coefficient of determination (Ｒ2) between spectral index based model and canopy chlorophyll content reached 0.75 for simulated data. To investigate the applicability of this chlorophyll model, the authors chose a field sample area in Gansu Province to carry out the measurement of leaf chlorophyll content, canopy reflectance and other parameters. Besides, the authors also ordered the synchronous Hyperion data, a hyperspectral image with a spatial resolution of 30 m. Canopy reflectance from field measurment and reflectance from Hyperion image were respectively used as the input parameter for the chlorophyll estimation model. Both of them got good results, which indicated that the model could be used for accurate canopy chlorophyll estimation using canopy reflectance. However, while using spaceborne hyperspectral data to estimate canopy chlorophyll content, good atmospheric correction is required.