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中国农学通报 ›› 2021, Vol. 37 ›› Issue (20): 7-16.doi: 10.11924/j.issn.1000-6850.casb2020-0534

所属专题: 玉米 烟草种植与生产

• 农学·农业基础科学 • 上一篇    下一篇

基于回归分析的玉米冠层叶绿素含量高光谱反演分析

赵占辉1,2(), 张丛志2, 张佳宝2(), 吴运金3(), 张宏敏1, 鲁春阳1   

  1. 1河南城建学院 测绘与城市空间信息学院,河南平顶山 467036
    2封丘农田生态系统国家试验站/土壤与农业可持续发展国家重点实验室/中国科学院南京土壤研究所,南京 210008
    3生态环境部南京环境科学研究所,南京 210042
  • 收稿日期:2020-10-08 修回日期:2020-12-12 出版日期:2021-07-15 发布日期:2021-08-06
  • 通讯作者: 张佳宝,吴运金
  • 作者简介:赵占辉,男,1988年出生,河南周口人,讲师,博士,主要从事土壤改良和定量遥感等方面的研究。通信地址:467036 河南省平顶山市新城区龙翔大道河南城建学院测绘学院,Tel:0375-2089033,E-mail:zhanhuizhao@126.com
  • 基金资助:
    江苏省科技计划重点项目“高标准农田基础地力提升与肥水高效利用关键技术集成与示范”(BE2019378);国家自然科学基金项目“潮土秸秆碳氮高效利用机理和途径及其对土壤孔隙和团聚结构的影响”(41877020);国家重点研发计划项目“黑土玉米秸秆还田养分高效利用机理”(2016YFD0200107)

The Hyperspectral Inversion for Estimating Maize Chlorophyll Contents Based on Regression Analysis

Zhao Zhanhui1,2(), Zhang Congzhi2, Zhang Jiabao2(), Wu Yunjin3(), Zhang Hongmin1, Lu Chunyang1   

  1. 1School of Geomatics and Urban Spatial Informatics, Henan University of Urban Construction, Pingdingshan Henan 467036
    2State Experimental Station of Agro-ecosystem in Fengqiu/State Key Laboratory of Soil and Sustainable Agriculture/ Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
    3Nanjing Institute of Environmental Sciences,Ministry of Ecology and Environment of the People’s Republic of China, Nanjing 210042
  • Received:2020-10-08 Revised:2020-12-12 Online:2021-07-15 Published:2021-08-06
  • Contact: Zhang Jiabao,Wu Yunjin

摘要:

以长期定位氮肥梯度试验地为靶区,采集的42个玉米冠层叶片SPAD值作为训练样本,利用无人机为平台搭载高光谱相机获取玉米冠层高光谱影像,从中提取17个光谱参数,采用相关性分析方法遴选出4个与数学变换(R、lgR1/lgR1/R、R1R)前后SPAD值密切相关的光谱参数,并用来构建一元、多元回归方程作为SPAD值一元、多元诊断模型。结果表明,光谱参数λrRgSDbNDVISPAD值关系最密切,该参数与SPAD值的一元归回方程具有较高的决定系数,分别为0.19、0.83、0.71、0.80,而该参数与SPAD值多元回归方程决定系数为0.93。利用训练样本之外的21个独立样本对一元、多元诊断模型进行验证分析,结果表明,基于λrRgSDbNDVISPAD值“多元诊断模型”具有较高的预测精度。与前人研究结果相比,本文所建立的玉米冠层SPAD值高光谱反演模型参数数量更少、决定系数更高,在高光谱或多光谱遥感与作物冠层叶绿素监测领域具有一定的参考价值。

关键词: 高光谱, 玉米, 叶绿素, 诊断模型, 回归分析

Abstract:

The long-term gradient experiment of nitrogen fertilizer was selected as the study area, and the collected 42 samples of chlorophyll contents from maize leaf were treated as training samples. Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing system was used to obtain maize hyperspectral images, and seventeen spectral parameters were extracted from these hyperspectral images. Using correlation analysis result, four spectral parameters, which closely related to the SPAD values or the values of after mathematical transformation (R, lgR, 1/lgR,1/R,R and1R) were selected and used to build unary or multiple regression equation. The results showed that λr, Rg, SDb, NDVI were the most closely related to SPAD values. The coefficients of determination between SPAD values and these four parameters were relatively high, which were 0.19, 0.83, 0.71 and 0.80, respectively. What’s more, the determination coefficient of multivariate regression equation between the four parameters and SPAD values was 0.93. The test results for the unitary or multivariate diagnostic model by using 21 independent samples from the training samples showed that the multivariate diagnostic model of SPAD values based on λr, Rg, SDb and NDVI had higher prediction accuracy. Compared with previous research results, the diagnostic model established in this study has the advantages of simple parameters and higher determination coefficient, which could be used as the data support for related field research.

Key words: hyperspectral, maize, chlorophyll, diagnostic model, regression analysis

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