In the interdisciplinary field of modern agriculture and environmental science, the study of changes in the spectral characteristics of crops contaminated with heavy metals is gradually becoming a hot topic. When crops are contaminated with heavy metals, their internal physiological structure and biochemical composition change, which is directly reflected in their spectral characteristics. The variation information generated by spectral changes becomes a crucial basis for monitoring heavy metal pollution. This study conducted pot experiments on maize plants contaminated with different concentrations of heavy metals, specifically copper and lead, in the laboratory. It measured the reflectance spectra of maize leaves under various concentration gradients of copper and lead pollution, as well as key data such as the copper and lead content in maize leaves. A comprehensive, detailed, and specialized dataset was constructed for maize plants contaminated with heavy metals copper and lead. And focusing on the spectrum of maize leaves- from a unique perspective in the frequency domain, we will conduct an in-depth exploration of its Full spectral range and sub-spectral range. By innovatively combining time-frequency analysis methods, a method called leaf-sensitive Spectral Interval Detection Method (SIDM) was proposed. Based on SIDM, spectral Variation Characteristic Parameters (SVCP) for leaf spectra were further proposed, which serve as “biomarkers” for crop contamination status and are of great significance for studying the intrinsic correlation between variation characteristic parameters and leaf heavy metal content. Meanwhile, compare it with conventional spectral indices to explore the spectral range sensitive to copper and lead pollution. On this basis, a leaf Spectral Transformation Method (STM) was ingeniously constructed by combining a nonlinear time-frequency distribution. Through experimental verification, STM can clearly distinguish different types of copper and lead pollution. SIDM has successfully enhanced and accurately extracted weak information on copper and lead pollution in leaves, making the originally weak and difficult-to-detect pollution signals visible. More importantly, a highly specific spectral range for copper and lead pollution has been identified, laying a solid foundation for the development of more accurate and efficient heavy metal pollution monitoring technologies in the future. STM has advantages in distinguishing spectral differences between samples with and without heavy metal pollution, and can intuitively categorize the element types of maize contaminated with copper and lead, effectively promoting the development of spectral technology for monitoring heavy metal pollution in crops.