Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing
1
College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
2
Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
3
Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China
4
Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
5
School of Resources and Environment, University of Electronic Science of China, Chengdu 610054, China
6
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
7
Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China
8
Department of Geography and Environmental Resources, College of Liberal Arts, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2018, 10(7), 2474; https://doi.org/10.3390/su10072474
Received: 28 June 2018 / Revised: 12 July 2018 / Accepted: 13 July 2018 / Published: 15 July 2018
(This article belongs to the Special Issue Sustainable Management of Heavy Metals)
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
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Zhao, L.; Hu, Y.-M.; Zhou, W.; Liu, Z.-H.; Pan, Y.-C.; Shi, Z.; Wang, L.; Wang, G.-X. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability 2018, 10, 2474. https://doi.org/10.3390/su10072474
AMA Style
Zhao L, Hu Y-M, Zhou W, Liu Z-H, Pan Y-C, Shi Z, Wang L, Wang G-X. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability. 2018; 10(7):2474. https://doi.org/10.3390/su10072474
Chicago/Turabian StyleZhao, Li, Yue-Ming Hu, Wu Zhou, Zhen-Hua Liu, Yu-Chun Pan, Zhou Shi, Lu Wang, and Guang-Xing Wang. 2018. "Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing" Sustainability 10, no. 7: 2474. https://doi.org/10.3390/su10072474
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
