Hyperspectral inversion of total nitrogen content in soils based on fractional order differential

doi:10.6046/zrzyyg.2022376

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Abstract

This study aims to determine the total nitrogen content (TNC) in soils in Tuoketuo County quickly and nondestructively, thus meeting the requirements of precision agriculture. With the soil TNC and hyperspectral data of 120 sampling sites in the study area as the data source, this study processed the hyperspectral data using the 1~2 orders fractional order differential (FOD) interval of 0.1 to screen the sensitive wavebands. Then, this study built 24 inversion models for soil TNC using the support vector machine (SVM) and the back propagation neural network (BPNN). The results are as follows: ① After FOD processing, the information at the wave crests and troughs of the spectra was amplified, and the reflectance of the remaining wavebands approached zero gradually with an increase in the decomposition scale. ② The Pearson correlation coefficient between original spectra and soil TNC was r = 0.61. This correlation coefficient was up to a maximum of 0.67 at 1.1- order after FOD processing, increasing by 0.06. ③ The BPNN prediction models outperformed the SVM prediction models. The optimal soil TNC prediction model was the BPNN model built after 1.1-order differential processing. This model yielded an R² of 0.75 and a root mean square error (RMSE) of 0.16 for the modeling set and an R² of 0.71 and an RMSE of 0.16 for the verification set, with a relative percent deviation (RPD) of 2.06. This model produced effective inversion results of the soil TNC in the study area, with a much higher accuracy than the BPNN model built using original spectra. Therefore, the BPNN model built using hyperspectral data through 1.1-order differential processing allows for the inversion-based prediction of soil TNC in the study area, providing a theoretical reference and technical support for local precision agriculture.

Keywords fractional order differential BPNN SVM precision agriculture

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Issue Date: 19 September 2023

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	Haoyu CHEN
	Lei XIANG
	He GAO
	Jinyi MU
	Xiaojing SUO
	Bowei HUA

Cite this article:

Haoyu CHEN,Lei XIANG,He GAO, et al. Hyperspectral inversion of total nitrogen content in soils based on fractional order differential[J]. Remote Sensing for Natural Resources, 2023, 35(3): 170-178.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022376 OR https://www.gtzyyg.com/EN/Y2023/V35/I3/170

Fig.1 Overview of study area of Togtoh County

Fig.2 Schematic diagram of soil collection and distribution

Tab.1 Descriptive statistics of soil nutrients

Fig.3 Schematic diagram of spectrum indoor measurement

Fig.4 Spectral reflectance curve

Fig.5 Spectral reflectance of different total nitrogen content

Fig.6 Spectrum comparison chart

Fig.7 Correlation distribution

Tab.2 Sensitive band screening

Tab.3 Descriptive statistics of modeling set and prediction set of total nitrogen

Tab.4 SVM prediction model

Tab.5 BP neural network prediction model

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