• Acta Optica Sinica
  • Vol. 42, Issue 22, 2230002 (2022)
Hailong Zhao1, Shu Gan1、2、*, Xiping Yuan2、3, Lin Hu1, Shuai Liu1, and Junjie Wang1
Author Affiliations
  • 1Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, Yunnan , China
  • 2Yunnan Institute of Engineering Research and Application of Plateau Mountain Spatial Information Surveying and Mapping Technology, Kunming 650093, Yunnan , China
  • 3West Yunnan University of Applied Sciences, Dali671000, Yunnan , China
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    DOI: 10.3788/AOS202242.2230002 Cite this Article Set citation alerts
    Hailong Zhao, Shu Gan, Xiping Yuan, Lin Hu, Shuai Liu, Junjie Wang. Inversion of Soil Iron Oxide Based on Multi-Scale Continuous Wavelet Decomposition[J]. Acta Optica Sinica, 2022, 42(22): 2230002 Copy Citation Text show less

    Abstract

    In order to predict the content of iron oxide in soil accurately and quickly, 135 soil samples are collected from the southern edge of Lufeng Dinosaur Valley, and soil spectral data and iron oxide content are measured in the laboratory. The original spectrum is smoothed by the Savitzky-Golay filter, and then conventional spectral transform and continuous wavelet transform are performed. The correlation coefficient (CC) method is used to analyze the correlation between the transform spectrum and iron oxide content. The wavelengths that pass the 0.01 significance test in each scale are selected as the coarse wavelengths, and the wavelengths selected by the competitive adaptive reweighted sampling (CARS) are further used as the characteristic wavelengths. Finally, the support vector regression (SVR) optimized by the genetic algorithm is used for modeling. The results reveal that continuous wavelet transform can improve the correlation between the soil spectral reflectance and iron oxide content. The number of independent variables for modeling can be effectively reduced by the CC-CARS wavelength selection method. The model constructed by the fourth-scale continuous wavelet decomposition (L4-CC-CARS-SVR) has the best effect. The coefficient of determination R2 and root-mean-square error ERMSE of its calibration set is 0.760 and 5.236 g·kg-1,respectively. The R2, ERMSE and performance to interquartile range ratio RPIQ of its validation set is 0.663, 7.798 g·kg-1 and 2.598, respectively, which indicates that the model has good stability and predictive ability.
    Hailong Zhao, Shu Gan, Xiping Yuan, Lin Hu, Shuai Liu, Junjie Wang. Inversion of Soil Iron Oxide Based on Multi-Scale Continuous Wavelet Decomposition[J]. Acta Optica Sinica, 2022, 42(22): 2230002
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