Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 15-22.doi: 10.12133/j.smartag.2021.3.2.202103-SA007

• Topic--Application of Spatial Information Technology in Agriculture • Previous Articles     Next Articles

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

WANG Lin1,2(), LIANG Jian3, MENG Fanyu4, MENG Yang1,2, ZHANG Yongtao5, LI Zhenhai1,2()   

  1. 1.Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs/ Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    3.National Agro-tech Extension and Service Center, Beijing 100125, China
    4.Beijing Agriculture Technology Extension Station, Beijing 100029, China
    5.Jiangsu Nonidt Agricultural Science and Technology Co. Ltd, Nanjing 210001, China
  • Received:2021-03-22 Revised:2021-04-25 Online:2021-06-30 Published:2021-08-25
  • corresponding author: Zhenhai LI E-mail:17852320332@163.com;lizh323@126.com


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.

Key words: winter wheat, grain protein content (GPC), remote sensing, hierarchical linear model (HLM), meteorological data

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