油菜叶片和冠层水平氮素含量的高光谱反射率估算模型

通过设计实验首先确定是否可以用油菜叶片反射率光谱预测其氮素含量,以及是否可以用预测的光谱特征值与氮素含量建立相关模型。实验结果表明,反射率光谱的转化形式R的一阶微分为预测油菜氮素含量的最佳形式,最佳波段选择与其他研究结果相似。短波红外光谱波段能较好预测氮素含量,但是选择的高相关性短波红外区域一般都较窄,因此可用高光谱和高辐射分辨率来准确描述油菜光谱特征。短波红外反射率的变化主要是由于植被化学信号的改变而引起的。直接太阳辐射率、观测设备、叶片振动以及大气湿度的细微变化都可能引起光谱一阶微分的变化。冠层反射率一阶微分R′建立的逐步回归模型拟合度较高。

Predicting Nitrogen Concentrations from Hyperspectral Reflectance at Leaf and Canopy for Rape

An experiment was designed to determine whether nitrogen concentrations could be predicted from reflectance(R) spectra of rape leaves in laboratory,and,if so,whether the predictive spectral features could be correlated with nitrogen concentration of simple canopies of rape.The best predictors for nitrogen in leaves appeared with first-difference transformations of R,and the bands selected were similar to those found in other studies.Shortwave infrared bands were best predictors for nitrogen.In the shortwave infrared region,however,the absolute differences in reflectance at critical bands were extremely small,and the bands of high correlation were narrow.High spectral and radiance resolution are required to resolve these differences accurately.Variability in canopy reflectance in shortwave infrared region was at least an order of magnitude beyond that necessary to detect signals from chemicals.The variability in first-difference R and log 1/R on canopy scales were related to the arrangement of trees with respect to direct solar radiation,instrument noise,leaf fluttering,and small change in atmospheric moisture.The first-difference of reflectance R based regressions prediction of nitrogen concentration at canopy level gets a good fitness.