Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data

With the rapid development of economy and people's living standards, people's demands for crops have changed from quantity to quality. The rise and rapid development of remote sensing technology provides an effective method for crop monitoring. Accurately predicting wheat quality before ha...

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Autores principales:	WANG Lin, LIANG Jian, MENG Fanyu, MENG Yang, ZHANG Yongtao, LI Zhenhai
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	winter wheat grain protein content (gpc) remote sensing hierarchical linear model (hlm) meteorological data Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/606db1ac7a1049e8aeeaa3fb85d68f95
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spelling	oai:doaj.org-article:606db1ac7a1049e8aeeaa3fb85d68f952021-11-17T07:52:11ZEstimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data2096-809410.12133/j.smartag.2021.3.2.202103-SA007https://doaj.org/article/606db1ac7a1049e8aeeaa3fb85d68f952021-06-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-2-15.shtmlhttps://doaj.org/toc/2096-8094With the rapid development of economy and people's living standards, people's demands for crops have changed from quantity to quality. The rise and rapid development of remote sensing technology provides an effective method for crop monitoring. Accurately predicting wheat quality before harvest is highly desirable to optimize management for farmers, grading harvest and categorized storage for the enterprise, future trading price, and policy planning. In this research, the main producing areas of winter wheat (Henan, Shandong, Hebei, Anhui and Jiangsu provinces) were chosed as the research areas, with collected 898 samples of winter wheat over growing seasons of 2008, 2009 and 2019. A Hierarchical Linear model (HLM) for estimating grain protein content (GPC) of winter wheat at heading-flowering stage was constructed to estimate the GPC of winter wheat in 2019 by using meteorological factors, remote sensing imagery and gluten type of winter wheat, where remote sensing data and gluten type were input variables at the first level of HLM and the meteorological data was used as the second level of HLM. To solve the problem of deviation in interannual and spatial expansion of GPC estimation model, maximum values of Enhanced Vegetation Index (EVI) from April to May calculated by moderate-resolution-imaging spectroradiometer were computed to represent the crop growth status and used in the GPC estimation model. Critical meteorological factors (temperature, precipitation, radiation) and their combinations for GPS estimation were compared and the best estimation model was used in this study. The results showed that the accuracy of GPC considering three meteorological factors performed higher accuracy (Calibrated set: R2 = 0.39, RMSE = 1.04%; Verification set: R2 = 0.43, RMSE = 0.94%) than the others GPC model with two meteorological factors or single meteorological factor. Therefore, three meteorological factors were used as input variables to build a winter wheat GPC forecast model for the regional winter wheat GPC forecast in this research. The GPC estimation model was applied to the GPC remote sensing estimation of the main winter wheat-producing areas, and the GPC prediction map of the main winter wheat producing areas in 2019 was obtained, which could obtain the distribution of winter wheat quality in the Huang-Huai-Hai region. The results of this study could provide data support for subsequent wheat planting regionalization to achieve green, high-yield, high-quality and efficient grain production.WANG LinLIANG JianMENG FanyuMENG YangZHANG YongtaoLI ZhenhaiEditorial Office of Smart Agriculturearticlewinter wheatgrain protein content (gpc)remote sensinghierarchical linear model (hlm)meteorological dataAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 2, Pp 15-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	winter wheat grain protein content (gpc) remote sensing hierarchical linear model (hlm) meteorological data Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	winter wheat grain protein content (gpc) remote sensing hierarchical linear model (hlm) meteorological data Agriculture (General) S1-972 Technology (General) T1-995 WANG Lin LIANG Jian MENG Fanyu MENG Yang ZHANG Yongtao LI Zhenhai Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data
description	With the rapid development of economy and people's living standards, people's demands for crops have changed from quantity to quality. The rise and rapid development of remote sensing technology provides an effective method for crop monitoring. Accurately predicting wheat quality before harvest is highly desirable to optimize management for farmers, grading harvest and categorized storage for the enterprise, future trading price, and policy planning. In this research, the main producing areas of winter wheat (Henan, Shandong, Hebei, Anhui and Jiangsu provinces) were chosed as the research areas, with collected 898 samples of winter wheat over growing seasons of 2008, 2009 and 2019. A Hierarchical Linear model (HLM) for estimating grain protein content (GPC) of winter wheat at heading-flowering stage was constructed to estimate the GPC of winter wheat in 2019 by using meteorological factors, remote sensing imagery and gluten type of winter wheat, where remote sensing data and gluten type were input variables at the first level of HLM and the meteorological data was used as the second level of HLM. To solve the problem of deviation in interannual and spatial expansion of GPC estimation model, maximum values of Enhanced Vegetation Index (EVI) from April to May calculated by moderate-resolution-imaging spectroradiometer were computed to represent the crop growth status and used in the GPC estimation model. Critical meteorological factors (temperature, precipitation, radiation) and their combinations for GPS estimation were compared and the best estimation model was used in this study. The results showed that the accuracy of GPC considering three meteorological factors performed higher accuracy (Calibrated set: R2 = 0.39, RMSE = 1.04%; Verification set: R2 = 0.43, RMSE = 0.94%) than the others GPC model with two meteorological factors or single meteorological factor. Therefore, three meteorological factors were used as input variables to build a winter wheat GPC forecast model for the regional winter wheat GPC forecast in this research. The GPC estimation model was applied to the GPC remote sensing estimation of the main winter wheat-producing areas, and the GPC prediction map of the main winter wheat producing areas in 2019 was obtained, which could obtain the distribution of winter wheat quality in the Huang-Huai-Hai region. The results of this study could provide data support for subsequent wheat planting regionalization to achieve green, high-yield, high-quality and efficient grain production.
format	article
author	WANG Lin LIANG Jian MENG Fanyu MENG Yang ZHANG Yongtao LI Zhenhai
author_facet	WANG Lin LIANG Jian MENG Fanyu MENG Yang ZHANG Yongtao LI Zhenhai
author_sort	WANG Lin
title	Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data
title_short	Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data
title_full	Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data
title_fullStr	Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data
title_full_unstemmed	Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data
title_sort	estimating grain protein content of winter wheat in producing areas based on remote sensing and meteorological data
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/606db1ac7a1049e8aeeaa3fb85d68f95
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_version_	1718425861493882880

Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data

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