Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

文献类型： 外文期刊

第一作者： Zhao, Li

作者： Zhao, Li;Hu, Yue-Ming;Zhou, Wu;Liu, Zhen-Hua v;Wang, Lu;Zhao, Li;Hu, Yue-Ming;Zhou, Wu;Liu, Zhen-Hua v;Wang, Lu;Wang, Guang-Xing;Zhao, Li;Hu, Yue-Ming;Zhou, Wu;Liu, Zhen-Hua v;Wang, Lu;Wang, Guang-Xing;Zhao, Li;Hu, Yue-Ming;Zhou, Wu;Liu, Zhen-Hua v;Wang, Lu;Wang, Guang-Xing;Hu, Yue-Ming;Pan, Yu-Chun;Shi, Zhou;Wang, Guang-Xing

作者机构：

关键词： soil heavy metal mercury content; hyperspectral remote sensing; MLR; BPNN; GA-BPNN

期刊名称：SUSTAINABILITY （ 影响因子：3.251； 五年影响因子：3.473 ）

ISSN： 2071-1050

年卷期： 2018 年 10 卷 7 期

页码：

收录情况： SCI

摘要： Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R-2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R-2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

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