Plumbum contamination detecting model for agricultural soil using hyperspectral data

Abstract

The issue of environmental pollution due to toxic heavy metals in agricultural land has caused worldwide growing concern in recent years. Being one of toxic heavy metals, the accumulation of Plumbum (Pb) may have negative effects on natural and agricultural vegetation growth, yield and quality. It can also constitute short-term and long-term health risks by entering the food chain. In this study, we analyze the relationships between physical and chemical characteristics, biological parameters of soil-vegetation system and hyperspectral spectrum responses systematically. The relation between hyperspectral data and the biological parameters of Pb polluted wheat canopy such as leaf pigments, leaf moisture, cell structure and leaf area index (LAI) are discussed. We detect the changes in the wheat biological parameters and spectral response associated with Pb concentration in soil. To reveal the impact mechanisms of Pb concentration on agricultural soil, six models including chlorophyll-leaf moisture model, chlorophyll-cell structure model, chlorophyll-LAI model, leaf moisture-cell structure model, leaf moisture-LAI model, cell structure- LAI model are explored. We find that changes in Pb concentration present various features in different models. Pb contamination in agricultural soil can be identified and assessed effectively while integrating the characteristics of those developed models.