A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD

Monitoring heavy metal stress in crops via hyperspectral remote sensing technology is an effective way. A new approach, namely the ratio difference of autocorrelation function first derivative (RDA), is proposed to extract characteristic regions of maize leaves spectra for the initially identification on contaminated category of copper (Cu) and lead (Pb). Simultaneously, empirical mode decomposition (EMD) and power spectral density (PSD) are integrated to construct EMD-PSD to visually discrimination on Cu and Pb stress from frequency domain perspective. In our work, pot experiment contaminated by Cu and Pb were designed and carried out, and corresponding chemical data, chlorophyll and spectra of maize leaves were collected. Based on acquired spectra, RDA is used to obtain indicators and characteristic intervals of spectra, and then EMD-PSD is applied to obtain intrinsic mode functions (IMFs) from spectra and PSD maps. Through experimental analysis, the following conclusions are drawn: 1) the red edge and red shoulder region of spectra can be used as candidate on indicator to find the characteristic regions of spectra, and integrated intersection spectral intervals are considerable to distinguish Cu and Pb; 2) PSD maps extracted by EMD-PSD significantly can discriminate stress of Cu and Pb with three-dimensional visualization. This study takes the combination of spectral domain and frequency domain as the exploration point, the stress of Cu and Pb was distinguished by mutual verification based on spectra (group I and group II and 2014 experiment). In summary, the proposed method can identify the weak differences of spectra and distinguish the stress of Cu and Pb qualitatively, which provides a new perspective for the identification of heavy metal stress categories.