Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images

Monitoring the aboveground biomass (AGB) of maize is essential for improving site-specific nutrient management and predicting yield to ensure food safety. A low-altitude unmanned aerial vehicle (UAV) was employed to acquire hyperspectral imagery of the maize canopy at three growth stages (V6, R1, R3...

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Autores principales:	Yue Zhang, Chenzhen Xia, Xingyu Zhang, Xianhe Cheng, Guozhong Feng, Yin Wang, Qiang Gao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	UAV-remote sensing Aboveground biomass Stepwise regression Random forest regression XGBoost regression Precision agriculture Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/93ef0023a3dc4635bf1cbbecbc297dea
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spelling	oai:doaj.org-article:93ef0023a3dc4635bf1cbbecbc297dea2021-12-01T04:57:27ZEstimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images1470-160X10.1016/j.ecolind.2021.107985https://doaj.org/article/93ef0023a3dc4635bf1cbbecbc297dea2021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21006506https://doaj.org/toc/1470-160XMonitoring the aboveground biomass (AGB) of maize is essential for improving site-specific nutrient management and predicting yield to ensure food safety. A low-altitude unmanned aerial vehicle (UAV) was employed to acquire hyperspectral imagery of the maize canopy at three growth stages (V6, R1, R3) to estimate the maize AGB. Five maize nitrogen (N) rate experiments were conducted in Lishu County, Jilin Province, Northeastern China, to create different biomass conditions. Combined with crop height data obtained from the field measurements, 30 narrowband vegetation indices (VIs) were extracted using surface reflectance data from the hyperspectral imagery. Stepwise regression, random forest (RF) regression and XGBoost regression models were used to predict the fresh and dry AGB of the 2019 growth season. The study revealed that (1) crop height explained the most variability (60–70%) in the maize dry and fresh AGB estimation of the V6 growth stage, while different VIs exhibited various importance for AGB estimation at other maize growth stages; (2) XGBoost regression models demonstrated high prediction accuracy in both fresh and dry AGB estimation, compared with stepwise regression and RF models. (3) XGBoost models also presented high prediction accuracy at each single-growth stage and the whole-growth stage, with the highest accuracy for the dry AGB at V6 growth stage (R2 = 0.81, RMSE = 0.27 t/ha). This study demonstrated the capability of UAV-based hyperspectral imagery for estimating maize AGB at the field scale, which can be used to assist precision agriculture.Yue ZhangChenzhen XiaXingyu ZhangXianhe ChengGuozhong FengYin WangQiang GaoElsevierarticleUAV-remote sensingAboveground biomassStepwise regressionRandom forest regressionXGBoost regressionPrecision agricultureEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107985- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UAV-remote sensing Aboveground biomass Stepwise regression Random forest regression XGBoost regression Precision agriculture Ecology QH540-549.5
spellingShingle	UAV-remote sensing Aboveground biomass Stepwise regression Random forest regression XGBoost regression Precision agriculture Ecology QH540-549.5 Yue Zhang Chenzhen Xia Xingyu Zhang Xianhe Cheng Guozhong Feng Yin Wang Qiang Gao Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
description	Monitoring the aboveground biomass (AGB) of maize is essential for improving site-specific nutrient management and predicting yield to ensure food safety. A low-altitude unmanned aerial vehicle (UAV) was employed to acquire hyperspectral imagery of the maize canopy at three growth stages (V6, R1, R3) to estimate the maize AGB. Five maize nitrogen (N) rate experiments were conducted in Lishu County, Jilin Province, Northeastern China, to create different biomass conditions. Combined with crop height data obtained from the field measurements, 30 narrowband vegetation indices (VIs) were extracted using surface reflectance data from the hyperspectral imagery. Stepwise regression, random forest (RF) regression and XGBoost regression models were used to predict the fresh and dry AGB of the 2019 growth season. The study revealed that (1) crop height explained the most variability (60–70%) in the maize dry and fresh AGB estimation of the V6 growth stage, while different VIs exhibited various importance for AGB estimation at other maize growth stages; (2) XGBoost regression models demonstrated high prediction accuracy in both fresh and dry AGB estimation, compared with stepwise regression and RF models. (3) XGBoost models also presented high prediction accuracy at each single-growth stage and the whole-growth stage, with the highest accuracy for the dry AGB at V6 growth stage (R2 = 0.81, RMSE = 0.27 t/ha). This study demonstrated the capability of UAV-based hyperspectral imagery for estimating maize AGB at the field scale, which can be used to assist precision agriculture.
format	article
author	Yue Zhang Chenzhen Xia Xingyu Zhang Xianhe Cheng Guozhong Feng Yin Wang Qiang Gao
author_facet	Yue Zhang Chenzhen Xia Xingyu Zhang Xianhe Cheng Guozhong Feng Yin Wang Qiang Gao
author_sort	Yue Zhang
title	Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
title_short	Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
title_full	Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
title_fullStr	Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
title_full_unstemmed	Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images
title_sort	estimating the maize biomass by crop height and narrowband vegetation indices derived from uav-based hyperspectral images
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/93ef0023a3dc4635bf1cbbecbc297dea
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Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images

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