Estimation of Soil Heavy Metal Content Using Hyperspectral Data

Zhenhua Liu,Yiping Peng,Ying Lu,Li Zhao,Guangxing Wang,Yueming Hu

doi:10.3390/rs11121464

Abstract

Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the Boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sample level and 51.55% and 36.34% at the regional scale. Moreover, the relationship of the DSSR/MSSR ratio with soil moisture content varied greatly before the wavelength of 1029 nm and became stable after that, which linked DSSR with MSSR and provided the possibility of applying the DSSR-based models to map the soil heavy metal content at the regional scale using the HJ-1A images. In addition, it was found that overall there were only a few soil samples with the content of heavy metals exceeding the health standards in Guangdong province, while in Conghua the seriously polluted areas were mainly distributed in the cities and croplands. This study implies that the new approach provides the potential to map the content of heavy metals in soil, but the estimation model of Cd was more accurate than those of As and Hg.

Highlights

Soil heavy metal pollution is of great concern
Because soil spectral reflectance is a cumulative property which derives from the inherent spectral behaviors of the heterogeneous combination of soil’s physical and chemical properties, hyperspectral visible and near-infrared reflectance (VNIR) spectroscopy coupled with calibration techniques has been developed to predict various soil properties and soil heavy metal content [9,10,11,12]
To extend the estimation models for dry soil to moisture soil conditions, we explored the relationship of the spectral ratio of dry soil spectral reflectance (DSSR) to moisture soil spectral reflectance (MSSR) with soil moisture content by comparing a linear model, a logarithm model, a power model and an exponential model

Summary

Introduction

Soil heavy metal pollution is of great concern. In China, the last two decades of anthropogenic activities, such as industrial pollution, livestock wastewater, mine drainage, and chemical pesticides, have led to heavy metal pollution in soil. In contrast with traditional in situ measurements of soil heavy metal content, hyperspectral remote sensing techniques provide advantages in rapidly monitoring soil heavy metal content at a regional scale, such as near real-time detection, relatively low cost, and environmental friendliness [9,10,11,12,13,14,15,16,17] These methods mainly use a 0–5 cm soil sampling depth, which does not result in a reliable measurement of soil heavy metal content in the studied areas because the distributions of heavy metals in soils are not homogenous from the surface (0 m) to the depth (1 m) [18,19]. The smoothed curves of spectral reflectance for the moisture and dry soil samples are shown in Figure 3a,b, respectively

Methods

Results

Conclusion