基于三维光谱指数的春小麦SPAD高光谱估算

SPAD hyperspectral estimation of spring wheat based on three dimensional spectral index

为探讨三维光谱指数(TBI)对春小麦(Triticum aestivum L.)SPAD(Soil and plant analyzer development)估算的可行性,在田间尺度上以春小麦为目标,采集抽穗期冠层高光谱数据并计算任意波段组合的三维光谱指数,构建基于最优三维光谱指数的春小麦SPAD估算模型。结果表明,在400~1300 nm波段处三维光谱指数TBI-1_((849、850、850 nm))、TBI-2_((849、850、997 nm))、TBI-3_((850、849、850 nm))、TBI-4_((849、849、850 nm))分别与SPAD呈极显著相关(P<0.01);采用人工神经网络(ANN)、K近邻(KNN)、支持向量机(SVM)3种机器学习算法,建立春小麦SPAD估算模型,通过模型的估算对比发现,KNN算法构建的模型估算效果(R^(2)=0.79,RMSE=2.68,RPD=2.25)优于ANN、SVR算法。

To explore the feasibility of estimating SPAD(Soil and plant analyzer development)of spring wheat(Triticum aestivum L.)using the three dimensional spectral index(TBI),this study focused on spring wheat at the field scale,collected canopy hyperspectral data at the heading stage,and calculated the three dimensional spectral index for any band combination to construct a SPAD estimation model for spring wheat based on the optimal three dimensional spectral index.The results showed that the three-dimensional spectral indexes TBI-1_((849,850,850 nm)),TBI-2_((849,850,997 nm)),TBI-3_((850,849,850 nm)),and TBI-4_((849,849,850 nm))were significantly correlated with SPAD at the range of 400~1300 nm(P<0.01);a spring wheat SPAD estimation model was established using three machine learning algorithms:artificial neural network(ANN),k-nearest neighbor(KNN),and support vector machine(SVM).By comparing the estimation results of the models,it was found that the model constructed using KNN algorithm had better estimation performance(R^(2)=0.79,RMSE=2.68,RPD=2.25)than ANN and SVR algorithms.