Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China.
Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China.
Sensors (Basel). 2020 Jul 21;20(14):4056. doi: 10.3390/s20144056.
With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.
随着含砷化学品的不断应用，土壤砷污染已成为全球性的严重问题。土壤砷污染的检测对于土壤的保护和修复具有重要意义。高光谱遥感能够有效地监测土壤重金属污染。然而，由于土壤砷（As）含量与光谱之间可能存在复杂的非线性关系以及数据冗余，因此迫切需要一种高效、准确的估计模型。针对这种情况，分别在湖北省大冶市和洪湖市采集了 62 个和 27 个样本。在实验室中进行了光谱测量和理化分析，以获得 As 含量和光谱反射率。在进行连续统去除（CR）后，采用稳定竞争自适应重加权采样算法与连续投影算法（sCARS-SPA）进行特征波段选择，有效解决了数据冗余和共线性问题。在特征波长上建立了偏最小二乘回归（PLSR）、径向基函数神经网络（RBFNN）和基于蛙跳算法优化的 RBFNN（SFLA-RBFNN），以预测土壤 As 含量。结果表明，sCARS-SPA-SFLA-RBFNN 模型在不同土地利用类型下具有最好的通用性和较高的预测精度，是一种估计土壤 As 含量的科学有效的方法。
