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There exists serious heavy metal contamination of agricultural soils in China. It is not only time- and labor-intensive to monitor soil contamination, but it also has limited scope when using conventional chemical methods. However, the method of the heavy metal monitoring of soil based on vegetation hyperspectral technology can break through the vegetation barrier and obtain the heavy metal content quickly over large areas. This paper discusses a highly accurate method for predicting the soil heavy metal content using hyperspectral techniques. We collected leaf hyperspectral data outdoors, and also collected soil samples to obtain heavy metal content data using chemical analysis. The prediction model for heavy metal content was developed using a difference spectral index, which was not highly satisfactory. Subsequently, the five factors that have a strong influence on the content of heavy metals were analyzed to determine multiple regression models for the elements As, Pb, and Cd. The results showed that the multiple regression model could better estimate the heavy metal content with stable fitting that has high prediction accuracy compared with the linear model. The results of this research provide a scientific basis and technical support for the hyperspectral inversion of the soil heavy metal content.

Inversion of Soil Heavy Metal Content Based on Spectral Characteristics of Peach Trees