Study on heavy metal content calculation and agricultural potential based on hyperspectral remote sensing image processing

Abstract

: Soil is the main influencing factor for plant growth, reproduction, and distribution. With the acceleration of industrialization and the intensification of human activities, the problem of heavy metal pollution in agricultural soil is becoming increasingly prominent. Heavy metals present in soil are toxic and readily absorbed by plants, posing a significant threat to human health when contaminated crops are consumed. Therefore, monitoring the content of heavy metals (referred to as HMs) in soil is imperative. Hyperspectral remote sensing (HRS), owing to its ultrahigh spectral resolution, holds significant promise for acquiring quantitative information on soil organic matter, minerals, and other components. In comparison with traditional detection methods, soil heavy metal inversion based on HRS offers advantages such as rapid, convenient, and large-scale on-site monitoring, demonstrating considerable practical value. This study investigates the monitoring mechanism and feature extraction of HRS technology by analyzing the calculation methods for soil and HM content. In the experimental phase, the HM content in rice and corn crops, paddy soil, and lime soil from 2017 to 2020 was analyzed. Through experimental comparative analysis, it was observed that the HMs enrichment coefficients were 0.987, 1.154, and 0.186 in 2017, 2018, and 2020, respectively. Notably, the smallest HMs enrichment coefficient was recorded in 2020, while the highest was in 2018. The utilization of HRS image processing enhances the accuracy of HM content determination, thus bearing significant implications for assessing soil agricultural potential.