Abstract
For agricultural production and food safety, it is important to accurately and extensively estimate the heavy metal(loid) pollution contents in farmland soil. Remote sensing technology provides a feasible method for the rapid determination of heavy metal(loid) contents. In this study, the contents of Ni, Hg, Cr, Cu, and As in the agricultural soil of the Suzi River Basin in Liaoning Province were taken as an example. The spectral data, with Savitzky–Golay smoothing, were taken as the original spectra (OR), and the spectral transformation was achieved by continuum removal (CR), reciprocal (1/R), root means square (R), first-order differential (FDR), and second-order differential (SDR) methods. Then the spectral indices were calculated by the optimal band combination algorithm. The correlation between Ni, Hg, Cr, Cu, and As contents and spectral indices was analyzed, and the optimal spectral indices were selected. Then, multiple linear regression (MLR), partial least squares regression (PLSR), random forest regression (RFR), and adaptive neuro-fuzzy reasoning system (ANFIS) were used to establish the estimation model based on the combined optimal spectral indices method. The results show that the combined optimal spectral indices method improves the correlation between spectra and heavy metal(loid), the MLR model produces the best estimation effect for Ni and Cu (R2=0.713 and 0.855, RMSE = 5.053 and 8.113, RPD = 1.908 and 2.688, respectively), and the PLSR model produces the best effect for Hg, Cr, and As (R2= 0.653, 0.603, and 0.775, RMSE = 0.074, 23.777, and 1.923, RPD = 1.733, 1.621, and 2.154, respectively). Therefore, the combined optimal spectral indices method is feasible for heavy metal(loid) estimation in soils and could provide technical support for large-scale soil heavy metal(loid) content estimation and pollution assessment.
Highlights
Soil is an important resource in the natural environment and for agricultural production; healthy soil is a basic requirement in realizing the goal of sustainable agricultural development [1,2]
The coefficient of variation (CV) is an important index reflecting the fluctuation in environmental variables: the CV of Cr is smaller than that of Ni, Hg, Cu, and As, which reflects that human activities have a stronger influence on the contents of Ni, Hg, Cu, and As than on Cr in agricultural soil [54]
Through proximity analysis (Figure 1 and Table 2), we found that the samples with high heavy metal(loid) contents were all obtained near mining areas and roads, indicating that the agricultural soil in this area is severely polluted by heavy metal(loid)s due to the processes of mineral mining and transportation
Summary
Soil is an important resource in the natural environment and for agricultural production; healthy soil is a basic requirement in realizing the goal of sustainable agricultural development [1,2]. In the development and use of mineral resources, heavy metal(loid) pollution of agricultural soil is one of the most serious problems caused by mining agricultural production [3]. Heavy metal(loid) pollution can reduce the activity of microorganisms in soils, affect the yield and quality of crops, degrade water quality, and seriously endanger human health through the food chain [5], and further affect the sustainable development of agriculture. Heavy metal(loid)s can damage water quality and seriously harm human health through the food chain. It is necessary to monitor heavy metal(loid) pollution in agricultural soil [6] Heavy metal(loid)s can damage water quality and seriously harm human health through the food chain. [5].
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