Hyperspectral estimation model of soil Pb content and its applicability in different soil types

Abstract

In order to obtain Pb content in soil quickly and efficiently, a multivariate linear regression (MLR) and a principal component regression (PCR) Pb content estimation model were established on the basis of hyperspectral techniques, and their applicability in different soil types was evaluated. Results indicated that Pb exhibited strong spatial heterogeneity in the study area, and more than 82% of the samples exceeded the background value. In addition, the pollution range was large. Pb was sensitive in the near-infrared band, and the correlation of absorbance (AB) was most significant of all the transformed forms. Both models achieved optimal stability and reliability when AB was used as an independent variable. Compared with the PCR model, the stability, fitting accuracy, and predictive power of the MLR model were superior with a coefficient of determination, root mean square error, and mean relative error of 0.724%, 24.92%, and 28.22%, respectively. Both models could be applied to different soil types; however, MLR had better applicability compared with PCR. The PCR model that distinguished different soil types had better reliability than one that did not. Thus, the model established via hyperspectral techniques can achieve large-area, rapid, and efficient soil Pb content monitoring, which can provide technical support for the treatment of heavy metal pollution in soil.