Study on Hyperspectral Characteristics and Estimation Model of Soil Mercury Content

1st International Conference on Frontiers of Materials Synthesis and Processing

材料科学;化学

Liu, Jinbao^1,3,4,5 ; Dong, Zhenyu^2 ; Sun, Zenghui^1,3,4,5 ; Ma, Hongchao^1,3,4,5 ; Shi, Lei^1,3,4,5

Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co. Ltd., Xian

710075, China^1

College of Forestry, Shenyang Agricultural University, Shenyang

110866, China^2

Key Laboratory of Degraded and Unused Land Consolidation Engineering, Ministry of Land and Resources, Xian

710075, China^3

Shaanxi Provincial Land Engineering Construction Group Co. Ltd, Xian

710075, China^4

Shaanxi Provincial Land Consolidation Engineering Technology Research Center, Xian

710075, China^5

关键词: Differential transformation;    First-order differentials;    Heavy metal elements;    Hyper-spectral characteristics;    Logarithmic transformations;    Partial least squares regression models;    Reflectance spectrum;    Reflection characteristics;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/274/1/012030/pdf
DOI  :  10.1088/1757-899X/274/1/012030

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In this study, the mercury content of 44 soil samples in Guan Zhong area of Shaanxi Province was used as the data source, and the reflectance spectrum of soil was obtained by ASD Field Spec HR (350-2500 nm) Comparing the reflection characteristics of different contents and the effect of different pre-treatment methods on the establishment of soil heavy metal spectral inversion model. The first order differential, second order differential and reflectance logarithmic transformations were carried out after the pre-treatment of NOR, MSC and SNV, and the sensitive bands of reflectance and mercury content in different mathematical transformations were selected. A hyperspectral estimation model is established by regression method. The results of chemical analysis show that there is a serious Hg pollution in the study area. The results show that: (1) the reflectivity decreases with the increase of mercury content, and the sensitive regions of mercury are located at 392 ∼ 455nm, 923nm ∼ 1040nm and 1806nm ∼ 1969nm. (2) The combination of NOR, MSC and SNV transformations combined with differential transformations can improve the information of heavy metal elements in the soil, and the combination of high correlation band can improve the stability and prediction ability of the model. (3) The partial least squares regression model based on the logarithm of the original reflectance is better and the precision is higher, Rc2 = 0.9912, RMSEC = 0.665; Rv2 = 0.9506, RMSEP = 1.93, which can achieve the mercury content in this region Quick forecast.