Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algor...

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Autores principales:	Yuanyuan Shi, Junyu Zhao, Xianchong Song, Zuoyu Qin, Lichao Wu, Huili Wang, Jian Tang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2bb7836e7e32480fb5af9f96e967fef6
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id	oai:doaj.org-article:2bb7836e7e32480fb5af9f96e967fef6
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spelling	oai:doaj.org-article:2bb7836e7e32480fb5af9f96e967fef62021-12-02T20:09:54ZHyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.1932-620310.1371/journal.pone.0253385https://doaj.org/article/2bb7836e7e32480fb5af9f96e967fef62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253385https://doaj.org/toc/1932-6203Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.Yuanyuan ShiJunyu ZhaoXianchong SongZuoyu QinLichao WuHuili WangJian TangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253385 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuanyuan Shi Junyu Zhao Xianchong Song Zuoyu Qin Lichao Wu Huili Wang Jian Tang Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
description	Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.
format	article
author	Yuanyuan Shi Junyu Zhao Xianchong Song Zuoyu Qin Lichao Wu Huili Wang Jian Tang
author_facet	Yuanyuan Shi Junyu Zhao Xianchong Song Zuoyu Qin Lichao Wu Huili Wang Jian Tang
author_sort	Yuanyuan Shi
title	Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
title_short	Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
title_full	Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
title_fullStr	Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
title_full_unstemmed	Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.
title_sort	hyperspectral band selection and modeling of soil organic matter content in a forest using the ranger algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2bb7836e7e32480fb5af9f96e967fef6
work_keys_str_mv	AT yuanyuanshi hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm AT junyuzhao hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm AT xianchongsong hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm AT zuoyuqin hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm AT lichaowu hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm AT huiliwang hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm AT jiantang hyperspectralbandselectionandmodelingofsoilorganicmattercontentinaforestusingtherangeralgorithm
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Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

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