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Zhen-Hua Liu
Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China

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Journal article
Published: 15 July 2018 in Sustainability
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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 R2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R2 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.

ACS Style

Li Zhao; Yue-Ming Hu; Wu Zhou; Zhen-Hua Liu; Yu-Chun Pan; Zhou Shi; Lu Wang; Guang-Xing Wang. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability 2018, 10, 2474 .

AMA Style

Li Zhao, Yue-Ming Hu, Wu Zhou, Zhen-Hua Liu, Yu-Chun Pan, Zhou Shi, Lu Wang, Guang-Xing Wang. Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability. 2018; 10 (7):2474.

Chicago/Turabian Style

Li Zhao; Yue-Ming Hu; Wu Zhou; Zhen-Hua Liu; Yu-Chun Pan; Zhou Shi; Lu Wang; Guang-Xing Wang. 2018. "Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing." Sustainability 10, no. 7: 2474.

Journal article
Published: 01 May 2006 in Science in China Series D: Earth Sciences
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In order to improve accuracy of soil moisture inversion using remote sensing, a new thermal inertia model is proposed in this paper. The improved model needs only surface maximum temperature as the temperature parameter input instead of input of the surface temperature difference, as well as the surface sensible and latent fluxes are introduced into boundary conditions of thermal conductivity equation. Furthermore, surface soil conductive heat transfer equation of two-layer model is used to solve the soil thermal inertia so that the remote sensing thermal inertia method can be applied to regions with better-covered vegetation, but usually only for the bare areas or worse vegetation covered areas. The model has been tested at several locations in the area of west Inner Mongolia. Comparing the simulation of the new model with the measurements obtained by apparent thermal inertia and by field test, the result shows that the inertia thermal model can be used to estimate soil moisture in more reasonable accuracy.

ACS Style

Zhenhua Liu; Yingshi Zhao. Research on the method for retrieving soil moisture using thermal inertia model. Science in China Series D: Earth Sciences 2006, 49, 539 -545.

AMA Style

Zhenhua Liu, Yingshi Zhao. Research on the method for retrieving soil moisture using thermal inertia model. Science in China Series D: Earth Sciences. 2006; 49 (5):539-545.

Chicago/Turabian Style

Zhenhua Liu; Yingshi Zhao. 2006. "Research on the method for retrieving soil moisture using thermal inertia model." Science in China Series D: Earth Sciences 49, no. 5: 539-545.