Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis

Remote sensing allows monitoring heavy metal pollution in crops for agricultural production and food security. This paper presents an approach to wavelet-fractal analysis for exploring a set of sensitive spectral parameters to monitor the heavy metal pollution levels in rice crops from hyperspectral reflectance data. Hyperspectral and biochemical data were collected from three study farms in Changchun, Jilin Province, China. Our study explored the fractal dimension of reflectance with wavelet transform (FDWT) that demonstrated a better performance than other existing methods. Our results obtained in this study show that the red edge position (REP) was the most sensitive indicator for monitoring the heavy metal pollution levels in rice crops among common indices. As compared with REP, the FDWT is more sensitive to biochemical composition, namely with respect to chlorophyll concentrations, N, Cu and Cd. The established linear models showed a correlation coefficient (R2) above 0.70, model efficiency (ME) above 0.65 and a root mean square error (RMSE) below 3.5. Minimum FDWT values occurred in rice with Level II pollution followed by Level I pollution, and finally the safe level. This study suggests that wavelet transform is well suited as a spectral analysis method to eliminate noise and amplify the stress information from heavy metals. The wavelet transform in conjunction with fractal analysis is promising for detecting heavy metal-induced stress in rice crops.

Research highlights▶ Accurate detection of heavy metal-induced stress on the growth of crops is essential for agricultural ecological environment and food security. How to explore sensitive spectral parameter for monitoring stress levels of crop under heavy metal from hyperspectral remote sensing is a challenging issue. ▶ In our manuscript, wavelet transform in conjunction with fractal analysis were adopted to extract and quantify subtle stress information of rice under heavy metal. The method of detecting stress levels of rice under heavy metal contamination may be a significance contributing to precision agriculture. ▶ Compared with REP in identifying the stress levels of crop under heavy metal, FDWT (fractal dimension of reflectance with wavelet transform) spectral parameter that explored in our manuscript was more sensitive to stress levels of rice under heavy metal with higher R2 against biochemical composition.