Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance
Autor: | Zhao Liu, Rui Chen, Yonghua Zhao, Lei Han, Huili Zhu, Hong Huo |
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Rok vydání: | 2020 |
Předmět: |
Soil test
lcsh:TJ807-830 Geography Planning and Development lcsh:Renewable energy sources Inverse transform sampling Soil science 010501 environmental sciences Management Monitoring Policy and Law Residual estimation mechanism 01 natural sciences Resampling Partial least squares regression back propagation neutral network lcsh:Environmental sciences 0105 earth and related environmental sciences lcsh:GE1-350 soil AS contents Renewable Energy Sustainability and the Environment lcsh:Environmental effects of industries and plants Hyperspectral imaging 04 agricultural and veterinary sciences Soil contamination Support vector machine lcsh:TD194-195 hyperspectral 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science |
Zdroj: | Sustainability Volume 12 Issue 4 Sustainability, Vol 12, Iss 4, p 1476 (2020) |
ISSN: | 2071-1050 |
DOI: | 10.3390/su12041476 |
Popis: | Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation R v 2 is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring. |
Databáze: | OpenAIRE |
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