Estimation of heavy metal concentration in rice leaves of farmland by hyperspectral leaf reflectance and partial least square regression

Abstract

Protecting people from heavy metal contamination is an important public-health concern and a major national environmental issue in China. The objective of this study was to quantitatively estimate the heavy metal concentration in rice leaves using leaf hyperspectral data and partial least squares regression (PLSR) models. 21 rice leaf samples and spectrum were gathered from farmlands in Zhangjiagang area, China. Copper (Cu), Cadmium (Cd) concentrations of rice leaves were measured within the lab. Firstly, the spectral data were treated by some methods, including, original reflectance (OS), First Derivative (FD) and Second Derivative (SD). Secondly, in order to select input variables for PLSR models, the correlation analysis between heavy metal concentration and spectral bands (OS, FD and SD), spectral indices were performed. Finally, we constructed the PLSR models between heavy metal concentration and spectrum. The results showed that correlation coefficients between Cu concentration and spectral data were higher than Cd. And that the bands significant correlation (P<0.05) with Cu concentration were far more than Cd. Ultimately, we selected 453 variables (442 bands and 11 spectral indices) and 19 variables (18 bands and 1 spectral index) as input variables of PLSR model for Cu and Cd, respectively. Moreover, we found that the Cu and Cd concentrations significantly correlated with spectral variables for (R2=0.41, RMSE=1.93) and (R2=0.38, RMSE=0.018) of PLSR models, respectively. These results indicated that they were good predicting models for estimating heavy metal concentration in rice leaves.