Vegetation heavy metal pollution monitoring is important for hyperspectral remote sensing monitoring. In order to qualitatively use hyperspectral remote sensing technology in monitoring heavy metal pollution of vegetation, the reflectance spectrum data collected from potted experiment collection was studied. The potted maize experiment in the laboratory stressed by different Cu2+ and Pb2+ stress concentrations was set up. The reflectance spectra of maize leaves and the contents of Cu2+ and Pb2+ under different concentrations of Cu2+ and Pb2+ were measured by the basic data on copper and lead-contaminated maize. A complete set of data sets for the heavy metal copper and lead-contaminated maize plant was formed. This study proposed a copper lead detection index CLDI, which realized the monitoring of heavy metals copper and led stress in two varieties of maize with different cultivation periods. Experiments of copper and lead pollution with different concentrations were designed, and the measured spectral reflectance interval of 450~850 nm of maize leaves was processed by the first order differential (D) and continuum removal (CR), and the DCR (Differential Continuum Removal) spectral curve was obtained. The Pearson correlation coefficient (r) was used to analyze the DCR and the biochemical data, and select characteristic bands sensitive to heavy metal Cu (Copper). The calculated Pearson correlation coefficients suggested that the DCR value at 490~520 and 680~700 nm presented a positive linear correlation close to 1 with the Cu2+ (Copper ion) contents in soil and leaves and a negative linear correlation that was close to -1 was present in the range of 630~650 and 710~750 nm. We selected the DCR value of wavelengths 505, 640, 690 and 730 nm to establish CLDI and compared it with conventional vegetation indices (VIs) by calculating the Pearson correlation coefficient between them and Cu contents in soil and leaves. We used the spectral data of different varieties of maize leaves obtained in 2017 to compare CLDI with the conventional vegetation index (VIs). CLDI was applied to monitor the pollution degree of maize leaves under lead stress. The results suggested that CLDI showed a significant correlation with Cu2+ and Pb2+ (Lead ion) stress concentration, and the correlation of CLDI was much stronger than that of other vegetation indices. The proposed CLDI detects the pollution degree of maize with different varieties and in different periods under copper and leads stress with the advantages of straightforward calculation, robustness, high effectiveness, and universality. This study focused on the laboratory leaf scale; it can provide the theoretical basis for monitoring heavy metal stress on the canopy scale.