Objective:To determine the optimal inversion model of Cu2+ content in corn leaves under Cu2+ pollution stress.Methods:Indoor potted corn was taken as the research object.On the basis of collecting spectra of corn leaves under different stress gradients and Cu2+ content in corn leaves at the same period,and the original spectral vegetation index of two bands was traversed and the correlation between the vegetation index and Cu2+ content in leaves was analyzed.Twenty-two kinds of spectral differential pretreated resampling spectral data of order 0.1-0.9,order 1.1-1.9 and order 1-4 were used to analyze the correlation between the wavelet coefficients and Cu2+ content in leaves by continuous wavelet transform.Based on correlation analysis,the optimal vegetation index and wavelet coefficients were extracted and the inversion model was established.Results:There was a significant correlation between vegetation index and Cu2+ content in leaves.The optimal band combinations with the spectral features of DI(621.5 nm,1889.2 nm),RI(482.2 nm,1418.5 nm),NDVI(666.3 nm,1917.2 nm),RNDVI(621.5 nm,1889.2 nm)were concentrated in the visible and near infrared bands.The wavelet coefficient also had a good correlation with Cu2+ content in leaves,and its sensitive bands were located around 400,600,900,1200,2400 nm,which was consistent with the sensitive bands of optimal vegetation index.The correlation coefficient between the Cu2+ content and the wavelet coefficients obtained by the 0.9 order spectral differentiation pretreatment was 0.88 at most.The optimal vegetation index and the optimal wavelet coefficients were extracted by correlation analysis,and the wavelet coefficients extracted by the vegetation index and the continuous wavelet coefficients of different differential transforms were used as independent variables to establish a linear inversion model.The inversion model established by using the optimal vegetation index had the highest accuracy and the most stable model,with a RMSE of 4.97 μg/g.Conclusion:The results showed that vegetation index and continuous wavelet transform had important reference value in monitoring heavy metal pollution of crops and had broad application prospects.
关键词
高光谱遥感/铜污染胁迫/植被指数/连续小波变换/反演模型
Key words
Hyperspectral remote sensing/Copper pollution stress/Vegetation index/Continuous wavelet transform/Inversion model
万方数据