Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

Li Zhao, Yue-Ming Hu, Wu Zhou, Zhen-Hua Liu, Yu-Chun Pan, Zhou Shi, Lu Wang and Guang-Xing Wang
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Li Zhao: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Yue-Ming Hu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Wu Zhou: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Zhen-Hua Liu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Yu-Chun Pan: Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Zhou Shi: Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China
Lu Wang: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Guang-Xing Wang: Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China

Sustainability, 2018, vol. 10, issue 7, 1-14

Abstract: 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.

Keywords: soil heavy metal mercury content; hyperspectral remote sensing; MLR; BPNN; GA-BPNN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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