The increasing pollution of soil with heavy metals has greatly damaged the ecological environment and endangered human health. Heavy metal stress affects not only biochemical constituents (i.e., chlorophyll) but also physiological functions (i.e., photosynthesis). We aimed to detect heavy metal stress in rice by integrating these two aspects. We selected Zhuzhou, Hunan Province as the study area and collected Sentinel-2 images from 2019 to 2021 and situ-measured data. First, two indicators were computed from Sentinel-2 images characterizing biochemical components and physiological functions, respectively. Second, the original time series were decomposed using the complete ensemble empirical mode decomposition with adaptive noise. Finally, a time-spectrum feature space (TSFS) model based on signal decomposition in critical periods sensitive to heavy metal stress was developed to determine the level of heavy metal stress. It showed that, regardless of individuals years, the overall accuracy and kappa coefficients of the TSFS model were >85 % and 0.8, and compared with a single indicator, combining two indicators with the signal-decomposition method significantly improved the accuracy of stress detection. This demonstrates that the integration of biochemical components with physiological functions and signal decomposition techniques can provide an important reference for crop heavy metal stress monitoring.