Abstract
Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R v 2 is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring.
Highlights
Arsenic (AS) is a kind of metalloid that is widely found in nature [1]
We explored the feasibility and best estimation method for estimating soil AS content by back propagation neural network (BPNN) and visible and neat infrared (VNIR) hyperspectral
(2) S–G smoothing was performed on the spectral data after 10 nm interval resampling, FD, second derivative (SD), and multiplicative scatter correction (MSC) transformation were performed, and the estimation model was established by the partial least squares regression (PLSR) method
Summary
Arsenic (AS) is a kind of metalloid that is widely found in nature [1]. Atmospheric deposition, industrial production, sewage irrigation, and soil arsenic -containing pesticides cause soil AS pollution [2,3]. Even though the on-site rapid monitoring method has the advantages of fast, continuous, and high-density information acquisition, it is mostly in the qualitative or semi-quantitative experimental stage and is susceptible to surrounding factors [8] Both laboratory and on-site monitoring methods have some limitations in obtaining surface pollution characteristics quickly and accurately. Visible/near-infrared [11], thermal infrared, and even ultraviolet spectroscopy can be used to estimate soil element contents because it is rapid, non-destructive, environmental friendly, and cost effective. It can quickly obtain the soil AS pollution by establishing the soil AS pollution parameter model based on spectral analysis principles. Because of the nonlinearity between soil AS content and spectral reflectance, it is difficult to estimate the soil AS content accurately using linear regression methods
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