Abstract
Visible and near-infrared reflectance (Vis-NIR) spectroscopy can provide low-cost and high-density data for mapping various soil properties. However, a weak correlation between the spectra and measurements of soil heavy metals makes spectroscopy difficult to use in predicting incipient risk areas. In this study, we introduce a new spectral index (SI) based on Vis-NIR spectra and use it as a covariate in ordinary cokriging (OCK) to improve the mapping of soil heavy metals. The SI was defined from the highest covariance between spectra and heavy metal content in the partial least squares regression (PLSR) model. The proposed mapping approach was compared with an ordinary kriging (OK) predictor that uses only soil heavy metal data and an OCK predictor that uses soil organic matter (SOM) and Fe as covariates. To this end, a total of 100 topsoil (0-20 cm) samples were collected in an agricultural area near Longkou City, and the contents of As, Pb and Zn in the soil were determined. The results showed that OCK with the SI provided better results in terms of unbiasedness and accuracy compared to other comparative methods. Additionally, we explored the SI through simple strategies based on spectral analysis and correlation statistics and found that the SI synthesized most of the soil properties affected by heavy metals and was not limited to Fe and SOM. In summary, the SI method is cost-effective for improving soil heavy metal mapping and can be applied to other areas.
Highlights
Heavy metals are well known for their toxicity and persistence and their ability to directly affect agricultural ecology and food safety in cultivated soils [1]–[3]
The average heavy metal contents in all samples were below level II of the Environmental Quality Standard for Soils (EQSS) of China [44] but exceeded the corresponding background values [45] by 1.28, 1.44 and 1.12 times, respectively
multiplicative scatter correction (MSC) and standard normal variate (SNV) can be used to reduce the estimation error caused by spectral baseline drift [26], and the unexceptional results for the spectral index (SI) based on the SNC and MSC spectra may be due to the negligible change in the baseline
Summary
Heavy metals are well known for their toxicity and persistence and their ability to directly affect agricultural ecology and food safety in cultivated soils [1]–[3]. The accumulation of heavy metals threatens human health because metals can enter the body through the food chain [4]. Excessive heavy metals in farmlands mainly originate from anthropogenic activities, such as industrial activities, sewage irrigation and pesticide and fertilizer overuse [5]. The high density of human activities leads to a sharp increase in soil heavy metals at the local scale, which is directly manifested by the complex spatial variability in soil heavy metals. Obtaining efficient and accurate spatial variation information about soil heavy metals has become increasingly. Important because such information is crucial to soil contamination remediation [6], [7]
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