Wavelet geographically weighted regression for spectroscopic modelling of soil properties

Abstract Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares...

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Autores principales:	Yongze Song, Zefang Shen, Peng Wu, R. A. Viscarra Rossel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fa39f1282e1d420abebb5f318f389f06
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spelling	oai:doaj.org-article:fa39f1282e1d420abebb5f318f389f062021-12-02T19:10:21ZWavelet geographically weighted regression for spectroscopic modelling of soil properties10.1038/s41598-021-96772-z2045-2322https://doaj.org/article/fa39f1282e1d420abebb5f318f389f062021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96772-zhttps://doaj.org/toc/2045-2322Abstract Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares regression (PLSR), and machine learning are the most common methods for modelling soil properties with spectra. Often, such models do not account for the multiresolution information presented in the vis–NIR signal, or the spatial variation in the data. To address these potential shortcomings, we used wavelets to decompose the vis–NIR spectra of 226 soils from agricultural and forested regions in south-western Western Australia and developed a wavelet geographically weighted regression (WGWR) for estimating soil organic carbon content, clay content and pH. To evaluate the WGWR models, we compared them to linear models derived with multiresolution data from a wavelet decomposition (WLR) and PLSR without multiresolution information. Overall, validation of the WGWR models produced more accurate estimates of the soil properties than WLR and PLSR. Around 3.5–49.1% of the improvement in the estimates was due to the multiresolution analysis and 1.0–5.2% due to the integration of spatial information in the modelling. The WGWR improves the modelling of soil properties with spectra.Yongze SongZefang ShenPeng WuR. A. Viscarra RosselNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yongze Song Zefang Shen Peng Wu R. A. Viscarra Rossel Wavelet geographically weighted regression for spectroscopic modelling of soil properties
description	Abstract Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares regression (PLSR), and machine learning are the most common methods for modelling soil properties with spectra. Often, such models do not account for the multiresolution information presented in the vis–NIR signal, or the spatial variation in the data. To address these potential shortcomings, we used wavelets to decompose the vis–NIR spectra of 226 soils from agricultural and forested regions in south-western Western Australia and developed a wavelet geographically weighted regression (WGWR) for estimating soil organic carbon content, clay content and pH. To evaluate the WGWR models, we compared them to linear models derived with multiresolution data from a wavelet decomposition (WLR) and PLSR without multiresolution information. Overall, validation of the WGWR models produced more accurate estimates of the soil properties than WLR and PLSR. Around 3.5–49.1% of the improvement in the estimates was due to the multiresolution analysis and 1.0–5.2% due to the integration of spatial information in the modelling. The WGWR improves the modelling of soil properties with spectra.
format	article
author	Yongze Song Zefang Shen Peng Wu R. A. Viscarra Rossel
author_facet	Yongze Song Zefang Shen Peng Wu R. A. Viscarra Rossel
author_sort	Yongze Song
title	Wavelet geographically weighted regression for spectroscopic modelling of soil properties
title_short	Wavelet geographically weighted regression for spectroscopic modelling of soil properties
title_full	Wavelet geographically weighted regression for spectroscopic modelling of soil properties
title_fullStr	Wavelet geographically weighted regression for spectroscopic modelling of soil properties
title_full_unstemmed	Wavelet geographically weighted regression for spectroscopic modelling of soil properties
title_sort	wavelet geographically weighted regression for spectroscopic modelling of soil properties
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fa39f1282e1d420abebb5f318f389f06
work_keys_str_mv	AT yongzesong waveletgeographicallyweightedregressionforspectroscopicmodellingofsoilproperties AT zefangshen waveletgeographicallyweightedregressionforspectroscopicmodellingofsoilproperties AT pengwu waveletgeographicallyweightedregressionforspectroscopicmodellingofsoilproperties AT raviscarrarossel waveletgeographicallyweightedregressionforspectroscopicmodellingofsoilproperties
_version_	1718377125219663872

Wavelet geographically weighted regression for spectroscopic modelling of soil properties

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