叶片叶绿素含量的快速无损估算方法对研究植被生长和环境胁迫都具有重要意义。 传统叶绿素光谱估测方法， 主要是基于叶片正面光谱信息。 而在实际遥感观测中， 传感器不仅会接收植被叶片正面光谱信息， 植被叶片反面光谱信息也会同时被接收。 该研究主要目的是找到在同时考虑叶片正反面光谱信息时也能精确估算叶片叶绿素含量的分析方法。 对比了简单差值植被指数（SD）， 简单比值植被指数（SR）， 归一化植被指数（ND）与偏最小二乘（PLS）建模方法， 并对检验样本集进行了精度比较。 结果发现用PLS方法估算两种植被正反面叶片的叶绿素含量与真实叶片叶绿素含量的拟合精度更高， R2为0.91， RMSE为5.21 μg·cm-2。 因此可以认为PLS方法在同时考虑植被叶片的正反面光谱信息时对植被叶片叶绿素含量的估算更准确。

The rapid and nondestructive estimation of leaf chlorophyll content is significance of the monitoring of vegetation growth and environmental stress. The traditional method of chlorophyll estimation is mostly based on the spectral information of upper leaf side. However, in the actual remote sensing observation, the sensor not only receives the adaxial spectral information of leaves, but also receives the spectral information from the abaxial surface of leaf. The main purpose of this study is to find an accurate method to estimate the chlorophyll content of leaves when considering both the adaxial and abaxial spectral information of the blade. This paper compared the simple difference vegetation index (SD), simple ratio vegetation index (SR), normalized difference vegetation index (ND) and partial least squares (PLS) regression modeling method. It was found that PLS regression modeling method had the highest precision in all of the methods to estimate leaf chlorophyll content of two species with two surfaces reflectance. The R2 was 0.91 and the RMSE was 5.21 g·cm-2 Therefore, it can be concluded that the PLS method is more accurate in estimating leaf chlorophyll content when considering both adaxial and abaxial leaf spectral information.