Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments

Abstract Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of differ...

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Autores principales:	Chenbo Yang, Meichen Feng, Lifang Song, Chao Wang, Wude Yang, Yongkai Xie, Binghan Jing, Lujie Xiao, Meijun Zhang, Xiaoyan Song, Muhammad Saleem
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f515b7a59544a4f810962588c91fe3e
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spelling	oai:doaj.org-article:1f515b7a59544a4f810962588c91fe3e2021-12-02T15:15:14ZStudy on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments10.1038/s41598-021-98143-02045-2322https://doaj.org/article/1f515b7a59544a4f810962588c91fe3e2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98143-0https://doaj.org/toc/2045-2322Abstract Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC.Chenbo YangMeichen FengLifang SongChao WangWude YangYongkai XieBinghan JingLujie XiaoMeijun ZhangXiaoyan SongMuhammad SaleemNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chenbo Yang Meichen Feng Lifang Song Chao Wang Wude Yang Yongkai Xie Binghan Jing Lujie Xiao Meijun Zhang Xiaoyan Song Muhammad Saleem Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
description	Abstract Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC.
format	article
author	Chenbo Yang Meichen Feng Lifang Song Chao Wang Wude Yang Yongkai Xie Binghan Jing Lujie Xiao Meijun Zhang Xiaoyan Song Muhammad Saleem
author_facet	Chenbo Yang Meichen Feng Lifang Song Chao Wang Wude Yang Yongkai Xie Binghan Jing Lujie Xiao Meijun Zhang Xiaoyan Song Muhammad Saleem
author_sort	Chenbo Yang
title	Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
title_short	Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
title_full	Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
title_fullStr	Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
title_full_unstemmed	Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
title_sort	study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1f515b7a59544a4f810962588c91fe3e
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Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments

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