Yan-hua FU, Jing LIU, Ya-chun MAO, Wang CAO, Jia-qi HUANG, Zhan-guo ZHAO. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1595
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 5, 1595 (2022)
Fig. 1. The location of the sampling points in the study area
Fig. 2. Visible-near-infrared spectra of the samples
Fig. 3. Correlation distribution of the manganese content and the difference index after three kinds of pretreatment
Fig. 4. Modeling flow chart of soil heavy metal content inversion
Fig. 5. Comparison of Mn content predicted by RBF neural network and measured Mn content
Fig. 6. Comparison of Co content predicted by RBF neural network and measured Co content
Fig. 7. Comparison of Fe content predicted by RBF neural network and measured Fe content
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Table 1. Basic parameters of SVC HR-1024 portable ground-object spectrometer
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Table 2. Descriptive statistics for heavy metal concentrations in soil samples (mg·kg-1)
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Table 3. Principle of the optimal selection of spectral indices of heavy metal elements
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