Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Abstract Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quant...

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Autores principales:	Peng-Peng Zhang, Xin-Xing Zhou, Zhi-Xiang Wang, Wei Mao, Wen-Xi Li, Fei Yun, Wen-Shan Guo, Chang-Wei Tan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aabf3488118d418c9bfd3f272ee78657
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spelling	oai:doaj.org-article:aabf3488118d418c9bfd3f272ee786572021-12-02T17:05:00ZUsing HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat10.1038/s41598-020-62125-52045-2322https://doaj.org/article/aabf3488118d418c9bfd3f272ee786572020-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-62125-5https://doaj.org/toc/2045-2322Abstract Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model’s coefficients of determination (R2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha−1 and 786.5 kg ha−1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion.Peng-Peng ZhangXin-Xing ZhouZhi-Xiang WangWei MaoWen-Xi LiFei YunWen-Shan GuoChang-Wei TanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peng-Peng Zhang Xin-Xing Zhou Zhi-Xiang Wang Wei Mao Wen-Xi Li Fei Yun Wen-Shan Guo Chang-Wei Tan Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat
description	Abstract Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model’s coefficients of determination (R2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha−1 and 786.5 kg ha−1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion.
format	article
author	Peng-Peng Zhang Xin-Xing Zhou Zhi-Xiang Wang Wei Mao Wen-Xi Li Fei Yun Wen-Shan Guo Chang-Wei Tan
author_facet	Peng-Peng Zhang Xin-Xing Zhou Zhi-Xiang Wang Wei Mao Wen-Xi Li Fei Yun Wen-Shan Guo Chang-Wei Tan
author_sort	Peng-Peng Zhang
title	Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat
title_short	Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat
title_full	Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat
title_fullStr	Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat
title_full_unstemmed	Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat
title_sort	using hj-ccd image and pls algorithm to estimate the yield of field-grown winter wheat
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/aabf3488118d418c9bfd3f272ee78657
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_version_	1718381807579168768

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

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