College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China.
College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, China.
Environ Pollut. 2022 May 1;300:118981. doi: 10.1016/j.envpol.2022.118981. Epub 2022 Feb 9.
Soil heavy metals pollution has been becoming one of the severely environmental issues globally. Previous studies reported laboratory-measured spectra could be used to infer soil heavy metals concentrations to some extent. However, using field-obtained spectra to estimate soil heavy metals concentrations is still a great challenge due to the low precision and weak efficiency at large scales. The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy metals concentrations of samples were measured under laboratory conditions. The direct standardization (DS) algorithm was introduced to calibrate the Gaofen-5 (GF-5) hyperspectral image based on the measured spectra of samples. The spectral reflectance of the GF-5 hyperspectral image was reconstructed using continuous wavelet transform (CWT) at different scales. The characteristic bands of GF-5 for estimating heavy metals concentrations were selected by the Boruta algorithm. Finally, the random forest (RF), the extreme learning machine (ELM), the support vector machine (SVM), and the back-propagation neural network (BPNN) algorithms were used to predict the heavy metals concentrations. Some findings were achieved. First, CWT can effectively eliminate the noise of satellite hyperspectral data. The characteristic bands of Zn (480-677, 827-1029, 1241-1334, 1435-1797, and 1949-2500 nm), Ni (514-630, 835-985, 1258-1325, 1460-1578, and 1949-2319 nm), and Cu (822-831; 1029-1300, 1486-1595, and 1730-2294 nm) can be effectively retrieved via the Boruta algorithm. Second, the estimation accuracy was significantly improved by using the DS algorithm. For zinc (Zn), nickel (Ni), and copper (Cu), the determination coefficients of the validation dataset (R) were 0.77 (RF), 0.62 (RF), and 0.56 (ELM), respectively. Third, the distribution trends of heavy metals were almost consistent with the results of actual ground measurements. This paper revealed that the GF-5 can be one of the reliable satellite hyperspectral imagery for mapping soil heavy metals.
土壤重金属污染已成为全球范围内严重的环境问题之一。先前的研究表明，实验室测量的光谱在一定程度上可用于推断土壤重金属浓度。然而，由于在大范围内精度低且效率低，使用野外获得的光谱来估算土壤重金属浓度仍然是一个巨大的挑战。
本研究从中国内蒙古鄂尔多斯露天煤矿采集了 110 个表层土壤样本。然后，在实验室条件下测量了样本的光谱和土壤重金属浓度。基于样本的实测光谱，引入直接标准化（DS）算法对高分五号（GF-5）高光谱图像进行定标。使用连续小波变换（CWT）在不同尺度上重建 GF-5 高光谱图像的光谱反射率。使用 Boruta 算法选择用于估算重金属浓度的 GF-5 特征波段。最后，使用随机森林（RF）、极限学习机（ELM）、支持向量机（SVM）和反向传播神经网络（BPNN）算法预测重金属浓度。得到了一些发现。
首先，CWT 可以有效地消除卫星高光谱数据的噪声。Zn（480-677、827-1029、1241-1334、1435-1797 和 1949-2500nm）、Ni（514-630、835-985、1258-1325、1460-1578 和 1949-2319nm）和 Cu（822-831；1029-1300、1486-1595 和 1730-2294nm）的特征波段可通过 Boruta 算法有效提取。
其次，通过使用 DS 算法，估算精度显著提高。对于锌（Zn）、镍（Ni）和铜（Cu），验证数据集的决定系数（R）分别为 0.77（RF）、0.62（RF）和 0.56（ELM）。
最后，重金属的分布趋势与实际地面测量结果基本一致。本文揭示了 GF-5 可以成为一种可靠的卫星高光谱图像，用于绘制土壤重金属。
