中国农业气象 ›› 2023, Vol. 44 ›› Issue (02): 154-164.doi: 10.3969/j.issn.1000-6362.2023.02.007

• 农业气象信息技术 栏目 • 上一篇    

稻纵卷叶螟危害下水稻叶片光谱特征及产量估测

黄璐,包云轩,郭铭淇,朱凤,杨荣明   

  1. 1.南京信息工程大学气象灾害预报和评估协同创新中心/南京信息工程大学江苏省农业气象重点实验室/气象灾害教育部重点实验室/南京信息工程大学气候与环境变化国际合作联合实验室,南京 210044;2.江苏省植物保护站, 南京 210013
  • 收稿日期:2022-03-09 出版日期:2023-02-20 发布日期:2023-01-16
  • 通讯作者: 包云轩,教授,研究方向为农业气象。 E-mail:baoyx@nuist.edu.cn; baoyunxuan@163.com
  • 作者简介:黄璐,E-mail:735602559@qq.com
  • 基金资助:
    国家自然科学基金项目(41975144);江苏省重点研发计划(现代农业)(BE2019387)

Hyperspectral Characteristics of Rice Leaf and Yield Estimation under the Infestation of Cnaphalocrocis medinalis Güenée

HUANG Lu, BAO Yun-xuan, GUO Ming-qi, ZHU Feng, YANG Ron-ming   

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology/Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044,China; 2.Plant Protection Station in Jiangsu Province, Nanjing 210013
  • Received:2022-03-09 Online:2023-02-20 Published:2023-01-16

摘要: 2020年在南京市浦口区桥林街道对稻纵卷叶螟[Cnaphalocrocis medinalis Güenée(C. medinalis)]自然发生的水稻农田进行高光谱观测试验,以探明不同稻纵卷叶螟危害程度下水稻叶片光谱特征与产量的关系,并对水稻产量进行预测。试验共选取80个样点,各样点虫害等级根据稻株的受害叶片数量占叶片总数的比例进行划分,利用SOC710VP便携式高光谱成像仪,采集水稻主要生育期(拔节期、孕穗期、灌浆期和成熟期)各样点水稻叶片的高光谱数据,调查收获后各样点的水稻产量数据,分析不同虫害等级下水稻叶片原始光谱、一阶导数光谱特征和产量参数的变化规律,并利用观测光谱与产量相关性较强的特征波段计算植被指数,建立基于植被指数的水稻产量估算模型。结果表明:(1)同一生育期内,水稻叶片近红外波段和红边波段的反射率随着虫害等级的升高而降低,而红光波段则相反。(2)同一生育期内,一阶导数光谱的峰值、红边幅值和红边面积随着虫害等级的增大而降低,红边位置的“蓝移”现象加重。(3)水稻的有效穗数、千粒重、结实率以及产量总体上随着虫害等级的上升而降低;但虫害等级较低时,有效穗数、千粒重以及结实率均出现“回升”现象。(4)利用各生育期DVI、RVI和CARI构建水稻产量估测模型,其中RVI的二项式模型模拟效果最佳。研究表明利用水稻叶片成像光谱特征可对稻纵卷叶螟危害进行长期、动态的监测,由其敏感波段构建的植被指数能够有效估测稻纵卷叶螟为害下的水稻产量。

关键词: 稻纵卷叶螟, 高光谱遥感, 水稻产量, 植被指数, 估算模型

Abstract: In 2020, a hyperspectral observation experiment was conducted on the rice fields of Cnaphalocrocis medinalis Güenée (C. medinalis) has been occurring naturally in Qiaolin Subdistrict, Pukou District, Nanjing, to explore the relationship between the spectral characteristics of rice leaves and the yields under the different infestation level of C. medinalis, and predict the yields of rice. 80 samples were selected in the experiments, and the different pest levels were divided according to the proportion of the number of infested leaves to the total number of leaves in the sample points. SOC710VP, a portable hyperspectral imager, was used to collect the hyperspectral data of rice leaves at different main growth stages (jointing stage, booting stage, grouting stage, mature stage), and the rice yield data of samples were investigated. The variation of the original spectral pattern, first derivative spectral characteristics of rice leaves and the yield parameters under the different pest levels was analyzed. The vegetation indices were calculated by using the characteristic bands with the strong correlation between the observation spectrum and yield, and the rice yield estimation model based on these vegetation indices was established. The results were showed as follows: (1) during the same growth period, the reflectivity on the near-infrared and red-edge bands of rice leaves decreased with the increasing of pest levels, while the red band was the opposite. (2) During the same growth period, the peaks of the first derivative spectrum, the amplitude of the red edge and the area of the red edge decreased with the increasing of the pest levels, and the "blue shifting" of the red edge position was aggravated. (3) The effective panicle number, 1000 grain weight, firming rate of rice and rice yield decreased with the increasing of the pest level, but when the pest levels were low, the effective panicle number, 1000 grain weight and firming rate of rice all rebounded. (4) The rice yield estimation models were constructed using DVI, RVI and CARI at each growth stage, of which the binomial model of RVI had the best effect. (5) The long-term and dynamic monitoring of the hazards of C. medinalis’ infestation can be monitored by using the imaging spectral characteristics of rice leaves, and the vegetation indices constructed from its sensitive bands can effectively estimate the rice yield under the infestation of C. medinalis.

Key words: Cnaphalocrocis medinalis Güenée, Hyperspectral remote sensing, Rice yield, Vegetation index, Estimation model