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

? 專題--空間信息技術農業應用 ? 上一篇    下一篇

基于遙感與氣象數據的冬小麥主產區籽粒蛋白質含量預報

王琳1,2(), 梁健3, 孟范玉4, 孟煬1,2, 張永濤5, 李振海1,2()   

  1. 1.農業農村部農業遙感機理與定量遙感重點實驗室/北京農業信息技術研究中心,北京 100097
    2.國家農業信息化工程技術研究中心,北京 100097
    3.全國農業技術推廣服務中心,北京 100125
    4.北京市農業技術推廣站,北京,100029
    5.江蘇諾麗慧農農業科技有限公司,江蘇 南京 210001
  • 收稿日期:2021-03-22 修回日期:2021-04-25 出版日期:2021-06-30 發布日期:2021-08-25
  • 基金資助:
    現代農業產業技術體系建設專項資金(CARS-03);國家自然科學基金(41701375)
  • 作者簡介:王 琳(1995-),女,碩士研究生,研究方向為遙感信息處理與分析。E-mail:17852320332@163.com。
  • 通訊作者: 李振海 E-mail:17852320332@163.com;lizh323@126.com

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

摘要:

開展小麥籽粒蛋白質含量的監測預報研究對于指導農戶調優栽培、企業分類收儲、期貨小麥價格、進口政策調整等具有重要意義。本研究以冬小麥主產區(河南省、山東省、河北省、安徽省和江蘇?。檠芯繀^域,構建了冬小麥籽粒蛋白質含量多層線性預測模型,并實現了2019年冬小麥蛋白質含量預報。為了解決預測模型在年際擴展和空間擴展存在偏差的問題,在蛋白質含量估算模型中考慮了氣象因素(溫度、降水、輻射量)、冬小麥筋型、抽穗—開花期增強型植被指數(EVI)等因素。結果表明,融合3個氣象因素的蛋白質含量估算模型建模集精度(R2 = 0.39,RMSE = 1.04%)與驗證集精度(R2 = 0.43、RMSE = 0.94%)均高于融合2個氣象因子的估算模型和單個氣象因子的估算模型。將蛋白質含量估算模型應用冬小麥主產區的蛋白質含量遙感估算,得到了2019年冬小麥主產區品質預報圖,并形成黃淮海地區冬小麥品質分布專題圖。本研究結果可同時為后續小麥種植區劃和實現綠色、高產、優質、高效糧食生產提供數據支撐。

關鍵詞: 冬小麥, 籽粒蛋白質含量, 遙感, 多層線性模型, 氣象數據

Abstract:

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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