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中国农学通报 ›› 2023, Vol. 39 ›› Issue (28): 137-141.doi: 10.11924/j.issn.1000-6850.casb2022-0738

• 农业信息·科技教育 • 上一篇    下一篇

基于HJ卫星的冬小麦白粉病监测研究

庄东英1(), 殷敏1, 耿安红1, 李卫国2, 崔必波1()   

  1. 1 江苏沿海地区农业科学研究所新洋试验站,江苏盐城 224049
    2 江苏省农科院农业信息研究所,南京 210000
  • 收稿日期:2022-08-25 修回日期:2023-05-18 出版日期:2023-10-05 发布日期:2023-09-25
  • 通讯作者: 崔必波,1971年出生,江苏建湖人,助理研究员,本科,主要从事农作物新品种选育及农业新技术示范推广工作。通信地址:224049 江苏省盐城市亭湖区黄尖镇南首盐城市新洋农业试验站,Tel:0515-82600181,E-mail:cuibibo1971@163.com。
  • 作者简介:

    庄东英,女,江苏海门人,1988年出生,研究实习员,硕士研究生,研究方向:农业遥感应用。通信地址:224049 江苏沿海地区农业科学研究所新洋试验站,Tel:0515-82600181,E-mail:

  • 基金资助:
    国家重点研发计划项目政府间重点专项“智慧农业中气象保障关键技术”(2021YFE0104400); 国家重点研发计划项目政府间重点专项“智慧农业中气象保障关键技术”(2037)

Monitoring of Powdery Mildew in Winter Wheat Based on HJ Satellite

ZHUANG Dongying1(), YIN Ming1, GENG Anhong1, LI Weiguo2, CUI Bibo1()   

  1. 1 Xinyang Experimental Station of Jiangsu Coastal Area Agricultural Science Research Institute, Yancheng, Jiangsu 224049
    2 Institute of Economy and Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210000
  • Received:2022-08-25 Revised:2023-05-18 Published-:2023-10-05 Online:2023-09-25

摘要:

对不同区域处在开花期的冬小麦进行白粉病病情指数(DI)调查,并同步进行冠层光谱测定及田间取样。选择叶片叶绿素含量、叶片含水量和空气温度3个对小麦生长起重要作用的因子,分别将这些因子与DI进行统计分析,建立小麦白粉病预测模型和专题图制作。结果表明,3个因子与DI有一定的相关性,其中,叶片叶绿素、叶片含水量和DI呈负相关,相关系数分别为0.6316和0.633。叶片叶面积指数(LAI)和温度与DI呈正相关,相关系数为0.6372和0.561。可以通过叶片叶绿素含量、叶片含水量、温度和LAI(用NDVI转换)的变化了解白粉病病情指数变化,研究证明遥感监测病情指数是可行的,建立的模型能较好反演小麦的白粉病病情指数。本研究在此基础上建立了遥感监测白粉病病情指数的专题图。

关键词: 冬小麦, 白粉病, 光谱, 监测, 病情指数

Abstract:

The disease index (DI) survey of powdery mildew for winter wheat at flowering stage was carried out in different regions. Canopy spectral measurement and field sampling were conducted simultaneously during this process. Three factors that played an important role in wheat growth were selected, namely leaf chlorophyll content, leaf water content and air temperature. These factors were statistically analyzed with DI to build a prediction model of wheat powdery mildew and obtained a thematic map. The results showed that there was a certain correlation between the three factors and DI. Leaf chlorophyll content and leaf water content were negatively correlated with DI with correlation coefficients of 0.6316 and 0.633 respectively, and leaf area index (LAI) and air temperature were positively correlated with DI with correlation coefficients of 0.6372 and 0.561 respectively. The change of wheat powdery mildew disease index can be obtained through changes of leaf chlorophyll content, leaf water content, air temperature and LAI (converted by NDVI), which proves that it is feasible to monitor the disease index based on remote sensing technology, and the constructed model can better retrieve the disease index of wheat powdery mildew. On this basis, this study drew the thematic map of wheat powdery mildew disease index monitored by remote sensing technology.

Key words: winter wheat, powdery mildew, spectrum, monitoring, disease index