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

Hailong Zhao, Shu Gan (), Xiping Yuan, Lin Hu, Junjie Wang and Shuai Liu
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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
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
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

Agriculture, 2022, vol. 12, issue 8, 1-20

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).

Keywords: soil; hyperspectral; iron oxide; spectra transform; fractional order differential (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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