Diagnosis of sensitive leaf types and spectral segments during maize heading stage under simulated copper stress environment in mining areas

This paper designed CSSDM (Copper Stress Spectral Diagnosis Method) from the perspective of frequency domain, combined with time-frequency analysis, to obtain sensitive leaf types and spectral segments under heavy metal copper stress, providing technical support for heavy metal monitoring in crops. Firstly, the hyperspectral reflectance data of maize leaves under copper stress were subjected to DD (Double Differentiation) and EE (Envelope Elimination), and transformed into the frequency domain. The d6 (Daubechies wavelet 6-layer) decomposition was performed using time-frequency analysis methods. Then, based on the signal anomaly points, wavelet high value points and DDEE (Double Differentiation Envelope Elimination) curve high value points, the SRVP (Spectral Reflectance Variation Parameters) of maize leaves are defined. Finally, by examining the correlation between spectral reflectance variation parameters and heavy metal content in maize leaves, we aim to explore the leaf types and spectral segments that are sensitive to copper pollution. The results showed that CSSDM can efficiently enhance weak information in maize leaves and accurately locate the spectral anomaly caused by heavy metal copper stress, with the anomaly range concentrated within 350 nm−800 nm. Spectral reflectance variation parameters can quantitatively measure the spectral anomalies of maize leaves under heavy metal copper stress. Under different copper stress gradients, maize new leaves exhibit sensitive leaf types, with sensitive spectral segments including Blue Edges, Green Peaks, Yellow Edges and Red Valleys.