基于多个高光谱参数的玉米叶片叶绿素含量估测模型

采用Field Spec Pro3光谱仪和SPAD-502叶绿素仪分别测定玉米叶片的光谱与其相对应的叶绿素含量,通过分析红边位置、蓝边位置以及绿峰位置等高光谱参数与叶绿素含量的关系,建立叶绿素含量的单、双和多变量光谱预测模型。结果表明:在可见光区域,玉米叶绿素含量高,光谱反射率低,而进入近红外区则刚好相反,叶绿素含量高,光谱反射率高;红边位置、绿峰位置及蓝边位置各高光谱参数与叶绿素含量的相关性均达极显著。其中红边位置与叶绿素含量的相关性最高,相关系数达0.84;利用所选的3个高光谱参数分别建立的单、双以及三变量模型,虽然大多数模型的精度R~2大于0.71,但分析对比得出利用红边、蓝边及绿峰位置3个变量建立的模型具有最大模型精度R~2、最小标准误差(S)和均方根误差(RMSE),因此其模型预测能力较优。

Prediction model on chlorophyll content in maize leaf based on several high spectral parameters

In this research, FieldSpecPro3 spectroscopy and SPAD-502 chlorophyll meter were used to measure the spectrum of corn leaf chlorophyll content and its amount respectively. Through the analysis on the relationships between chlorophyll content and parameters of red, blue, and green edge positions and spectral peak positions, single, double and multivariate spectral prediction models about the chlorophyll content were established. The results showed that in the visible spectrum, the higher corn chlorophyll content, the lower reflectance spectrum. However, exact opposite was observed in the infrared spectrum. Spectral parameters of red edge, green peak and blue edge positions were in significant correlations with the chlorophyll content, reaching up to 0.84 between red edge position and the chlorophyll content. As a result, single, double and three-variable models using three high spectral parameters were established. Although the accuracy of R² were mostly greater than 0.71, it was further suggested that three-variable model had the best accuracy in R², the minimal standard deviation (S) and root mean square error (RMSE), which might provide better prediction results than the other two models.