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Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant Neyraudia reynaudiana, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0–20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R2) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance.

applied sciences

A Back Propagation Neural Network Model Optimized by Mind Evolutionary Algorithm for Estimating Cd, Cr, and Pb Concentrations in Soils Using Vis-NIR Diffuse Reflectance Spectroscopy