欢迎您访问《智慧农业(中英文)》官方网站! English

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

• 专题--空间信息技术农业应用 • 上一篇    下一篇

基于遥感与气象数据的冬小麦主产区籽粒蛋白质含量预报

王琳1,2(), 梁健3, 孟范玉4, 孟炀1,2, 张永涛5, 李振海1,2()   

  1. 1.农业农村部农业遥感机理与定量遥感重点实验室/北京农业信息技术研究中心,北京 100097
    2.国家农业信息化工程技术研究中心,北京 100097
    3.全国农业技术推广服务中心,北京 100125
    4.北京市农业技术推广站,北京,100029
    5.江苏诺丽慧农农业科技有限公司,江苏 南京 210001
  • 收稿日期:2021-03-22 修回日期:2021-04-25 出版日期:2021-06-30
  • 基金资助:
    现代农业产业技术体系建设专项资金(CARS-03);国家自然科学基金(41701375)
  • 作者简介:王 琳(1995-),女,硕士研究生,研究方向为遥感信息处理与分析。E-mail:17852320332@163.com
  • 通信作者:

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

摘要:

开展小麦籽粒蛋白质含量的监测预报研究对于指导农户调优栽培、企业分类收储、期货小麦价格、进口政策调整等具有重要意义。本研究以冬小麦主产区(河南省、山东省、河北省、安徽省和江苏省)为研究区域,构建了冬小麦籽粒蛋白质含量多层线性预测模型,并实现了2019年冬小麦蛋白质含量预报。为了解决预测模型在年际扩展和空间扩展存在偏差的问题,在蛋白质含量估算模型中考虑了气象因素(温度、降水、辐射量)、冬小麦筋型、抽穗—开花期增强型植被指数(EVI)等因素。结果表明,融合3个气象因素的蛋白质含量估算模型建模集精度(R2 = 0.39,RMSE = 1.04%)与验证集精度(R2 = 0.43、RMSE = 0.94%)均高于融合2个气象因子的估算模型和单个气象因子的估算模型。将蛋白质含量估算模型应用冬小麦主产区的蛋白质含量遥感估算,得到了2019年冬小麦主产区品质预报图,并形成黄淮海地区冬小麦品质分布专题图。本研究结果可同时为后续小麦种植区划和实现绿色、高产、优质、高效粮食生产提供数据支撑。

关键词: 冬小麦, 籽粒蛋白质含量, 遥感, 多层线性模型, 气象数据

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

中图分类号: