Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm

Taking the west lakeside oasis of Bosten Lake as the study area, using the measured soil organic carbon content and hyperspectral data, the successive projection algorithm (SPA) was used to filter the characteristic variables from the full-band spectral data, and then the full-band and characteristi...

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Main Authors: NIU Fangpeng, LI Xinguo, MAMATTURSUN•Eziz, ZHAO Hui
Format: Article
Language:English
Published: Zhejiang University Press 2021-10-01
Series:浙江大学学报. 农业与生命科学版
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Online Access:https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.01.181
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author NIU Fangpeng
LI Xinguo
MAMATTURSUN•Eziz
ZHAO Hui
author_facet NIU Fangpeng
LI Xinguo
MAMATTURSUN•Eziz
ZHAO Hui
author_sort NIU Fangpeng
collection DOAJ
description Taking the west lakeside oasis of Bosten Lake as the study area, using the measured soil organic carbon content and hyperspectral data, the successive projection algorithm (SPA) was used to filter the characteristic variables from the full-band spectral data, and then the full-band and characteristic bands were used to construct partial least square regression (PLSR) and support vector machine (SVM) models to estimate soil organic carbon content. The results showed that: 1) The soil organic carbon content varied from 0.75 to 48.13 g/kg, with an average value of 13.31 g/kg, showed moderate variability, with a coefficient of variation of 63.19%. 2) The soil organic carbon content and the original spectral reflectance showed a negative correlation, with -0.62<correlation coefficient (r)<-0.07. After the bands were preprocessed by Savitzky-Golay-standard normal variate-first derivative (SG-SNV-1st Der), the number of bands that passed the extremely significant test (P<0.01) were 414, mainly concentrated in 487-575, 725-998 and 1 464-1 514 nm. The correlation between 788, 800 and 1 768 nm was the highest, with the correlation coefficients of more than 0.80. 3) After the spectra were preprocessed by SG-SNV-1st Der, the coefficient of determination (R<sup>2</sup>) of validation set of PLSR model constructed by SPA was 0.79; root mean square error (RMSE) was 3.58 g/kg; residual prediction deviation (RPD) was 1.99; and ratio of performance to interquartile distance (RPIQ) was 2.23. However, the validation set constructed by SPA combined with SVM was R<sup>2</sup>=0.81, RMSE=3.16 g/kg, RPD=2.25, RPIQ=2.53. It shows that the model constructed by SPA combined with SVM can better estimate soil organic carbon content in the study area.
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series 浙江大学学报. 农业与生命科学版
spelling doaj-art-1fc7b530030a43bb8cd83b882df5e2ad2025-08-20T03:16:11ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552021-10-014767368210.3785/j.issn.1008-9209.2021.01.18110089209Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithmNIU FangpengLI XinguoMAMATTURSUN•Eziz ZHAO HuiTaking the west lakeside oasis of Bosten Lake as the study area, using the measured soil organic carbon content and hyperspectral data, the successive projection algorithm (SPA) was used to filter the characteristic variables from the full-band spectral data, and then the full-band and characteristic bands were used to construct partial least square regression (PLSR) and support vector machine (SVM) models to estimate soil organic carbon content. The results showed that: 1) The soil organic carbon content varied from 0.75 to 48.13 g/kg, with an average value of 13.31 g/kg, showed moderate variability, with a coefficient of variation of 63.19%. 2) The soil organic carbon content and the original spectral reflectance showed a negative correlation, with -0.62<correlation coefficient (r)<-0.07. After the bands were preprocessed by Savitzky-Golay-standard normal variate-first derivative (SG-SNV-1st Der), the number of bands that passed the extremely significant test (P<0.01) were 414, mainly concentrated in 487-575, 725-998 and 1 464-1 514 nm. The correlation between 788, 800 and 1 768 nm was the highest, with the correlation coefficients of more than 0.80. 3) After the spectra were preprocessed by SG-SNV-1st Der, the coefficient of determination (R<sup>2</sup>) of validation set of PLSR model constructed by SPA was 0.79; root mean square error (RMSE) was 3.58 g/kg; residual prediction deviation (RPD) was 1.99; and ratio of performance to interquartile distance (RPIQ) was 2.23. However, the validation set constructed by SPA combined with SVM was R<sup>2</sup>=0.81, RMSE=3.16 g/kg, RPD=2.25, RPIQ=2.53. It shows that the model constructed by SPA combined with SVM can better estimate soil organic carbon content in the study area.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.01.181soil organic carbonhyperspectral datasuccessive projection algorithmsupport vector machinelakeside oasis
spellingShingle NIU Fangpeng
LI Xinguo
MAMATTURSUN•Eziz
ZHAO Hui
Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm
浙江大学学报. 农业与生命科学版
soil organic carbon
hyperspectral data
successive projection algorithm
support vector machine
lakeside oasis
title Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm
title_full Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm
title_fullStr Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm
title_full_unstemmed Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm
title_short Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm
title_sort hyperspectral estimation of soil organic carbon content in the west lakeside oasis of bosten lake based on successive projection algorithm
topic soil organic carbon
hyperspectral data
successive projection algorithm
support vector machine
lakeside oasis
url https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.01.181
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AT lixinguo hyperspectralestimationofsoilorganiccarboncontentinthewestlakesideoasisofbostenlakebasedonsuccessiveprojectionalgorithm
AT mamattursuneziz hyperspectralestimationofsoilorganiccarboncontentinthewestlakesideoasisofbostenlakebasedonsuccessiveprojectionalgorithm
AT zhaohui hyperspectralestimationofsoilorganiccarboncontentinthewestlakesideoasisofbostenlakebasedonsuccessiveprojectionalgorithm