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Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide

Author

Listed:
  • Hailong Zhao

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Shu Gan

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    Application Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province, Kunming 650093, China)

  • Xiping Yuan

    (Application Engineering Research Center of Spatial Information Surveying and Mapping Technology in Plateau and Mountainous Areas Set by Universities in Yunnan Province, Kunming 650093, China
    College of Geosciences and Engineering, West Yunnan University of Applied Sciences, Dali 671000, China)

  • Lin Hu

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Junjie Wang

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Shuai Liu

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

Abstract

Iron oxide is the main form of iron present in soils, and its accumulation and migration activities reflect the leaching process and the degree of weathering development of the soil. Therefore, it is important to have information on the iron oxide content of soils. However, due to the overlapping characteristic spectra of iron oxide and organic matter in the visible-near infrared, appropriate spectral transformation methods are important. In this paper, we first used conventional spectral transformation (continuum removal, CR; standard normal variate, SNV; absorbance, log (1/R)), continuous wavelet transform (CWT), and fractional order differential (FOD) transform to process original spectra (OS). Secondly, competitive adaptive reweighted sampling (CARS) was used to extract characteristic wavelengths. Finally, two regression models (backpropagation neural network, BPNN; support vector regression (SVR) were used to predict the content of iron oxide. The results show that the FOD can significantly improve the correlation with iron oxide compared with the CR, SNV, log (1/R) and CWT; the baseline drift and overlapping peaks decrease with increasing the order of FOD; the CARS algorithm based on 50th averaging can select more stable characteristic wavelengths; the FOD achieves better results regardless of the modelling method, and the model based on 0.5-order differential has the best prediction performance (R 2 = 0.851, RMSE = 5.497, RPIQ = 3.686).

Suggested Citation

  • Hailong Zhao & Shu Gan & Xiping Yuan & Lin Hu & Junjie Wang & Shuai Liu, 2022. "Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide," Agriculture, MDPI, vol. 12(8), pages 1-20, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1163-:d:880965
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    References listed on IDEAS

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    1. 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, MDPI, vol. 10(7), pages 1-14, July.
    2. Lei Han & Rui Chen & Huili Zhu & Yonghua Zhao & Zhao Liu & Hong Huo, 2020. "Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
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    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.

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