Combining radiative transfer model with statistical model for distinguishing heavy metal stress level in rice from Sentinel-2 satellite images

Heavy metal stress can lead to morphological and physiological variations in crops. We aimed to distinguish heavy metal stress levels based on the variations of morphological and physiological parameters from radiative transfer and statistical models. Sentinel-2 satellite images and in situ measured data were collected from heavy metal-contaminated soils of rice growing areas in Zhuzhou City, Hunan Province, China. The chlorophyll content (chlorophyll a + chlorophyll b, Cab) and leaf area index (LAI) were calculated using a PROSAIL radiative transfer model and the multilayer perceptron algorithm. A two-dimensional feature space was established from Cab-LAI. Furthermore, a normalized heavy metal stress index (HMSI) from the established Cab-LAI theoretical triangular model was explored to distinguish heavy metal stress levels in rice. The results indicated that (i) the PROSAIL and artificial neural network algorithm were successful at deriving physiological parameters with high estimation accuracy. Pearson’s correlation coefficient between the predicted and measured Cab was 0.85; (ii) the correlation between the measured concentration of cadmium in the soil and the HMSI was 0.84, indicating that it is a good indicator of rice damage caused by heavy metal stress, with the maximum HMSI occurring in rice subjected to high pollution; and (iii) high pollution occurred on both sides of the Xiangjiang River, whereas moderate pollution mainly existed around the heavily polluted areas. Areas with non-pollution and mild pollution were distributed over most of the study area. Combining rice Cab with LAI is a feasible method to determine the distribution of rice heavy metal stress levels over a large area.