Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging

Abstract The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Cheng Li, Xicun Zhu, Yu Wei, Shujing Cao, Xiaoyan Guo, Xinyang Yu, Chunyan Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/62648359130b42da98889557f67e25d5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:62648359130b42da98889557f67e25d5
record_format	dspace
spelling	oai:doaj.org-article:62648359130b42da98889557f67e25d52021-12-02T15:07:49ZEstimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging10.1038/s41598-018-21963-02045-2322https://doaj.org/article/62648359130b42da98889557f67e25d52018-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-21963-0https://doaj.org/toc/2045-2322Abstract The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both frequently used method to construct estimation model based on remote sensing imaging. The aim of this study was to find out which estimation model of apple tree canopy chlorophyll content based on the vegetation indices constructed with visible, red edge and near-infrared bands of the sensor of Sentinel-2 was more accurate and stabler. The results were as follows: The calibration set coefficient of determination (R2) value of 0.729 and validation set R2 value of 0.667 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were higher than those of the model using the BPNN method by 8.2% and 11.0%, respectively. The calibration set root mean square error (RMSE) of 0.159 and validation set RMSE of 0.178 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were lower than those of the model using the BPNN method by 5.9% and 3.8%, respectively.Cheng LiXicun ZhuYu WeiShujing CaoXiaoyan GuoXinyang YuChunyan ChangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cheng Li Xicun Zhu Yu Wei Shujing Cao Xiaoyan Guo Xinyang Yu Chunyan Chang Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging
description	Abstract The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both frequently used method to construct estimation model based on remote sensing imaging. The aim of this study was to find out which estimation model of apple tree canopy chlorophyll content based on the vegetation indices constructed with visible, red edge and near-infrared bands of the sensor of Sentinel-2 was more accurate and stabler. The results were as follows: The calibration set coefficient of determination (R2) value of 0.729 and validation set R2 value of 0.667 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were higher than those of the model using the BPNN method by 8.2% and 11.0%, respectively. The calibration set root mean square error (RMSE) of 0.159 and validation set RMSE of 0.178 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were lower than those of the model using the BPNN method by 5.9% and 3.8%, respectively.
format	article
author	Cheng Li Xicun Zhu Yu Wei Shujing Cao Xiaoyan Guo Xinyang Yu Chunyan Chang
author_facet	Cheng Li Xicun Zhu Yu Wei Shujing Cao Xiaoyan Guo Xinyang Yu Chunyan Chang
author_sort	Cheng Li
title	Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging
title_short	Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging
title_full	Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging
title_fullStr	Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging
title_full_unstemmed	Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging
title_sort	estimating apple tree canopy chlorophyll content based on sentinel-2a remote sensing imaging
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/62648359130b42da98889557f67e25d5
work_keys_str_mv	AT chengli estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging AT xicunzhu estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging AT yuwei estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging AT shujingcao estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging AT xiaoyanguo estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging AT xinyangyu estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging AT chunyanchang estimatingappletreecanopychlorophyllcontentbasedonsentinel2aremotesensingimaging
_version_	1718388389554683904

Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging

Ejemplares similares