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Smart Agriculture ?? 2021, Vol. 3 ?? Issue (2): 35-44.doi: 10.12133/j.smartag.2021.3.2.202105-SA001

? 專題--空間信息技術農業應用 ? 上一篇    下一篇

基于高光譜遙感的冬小麥澇漬脅迫識別及程度判別分析

楊菲菲(), 劉升平, 諸葉平, 李世娟()   

  1. 中國農業科學院農業信息研究所/農業農村部信息服務技術重點實驗室,北京 100081
  • 收稿日期:2021-05-08 修回日期:2021-06-27 出版日期:2021-06-30 發布日期:2021-08-25
  • 基金資助:
    國家重點研發計劃項目(2016YFD0200600);國家重點研發計劃課題(2016YFD0200601);河北省重點研發計劃項目(19227407D);中央級公益性科研院所基本科研業務費專項(Y2021XK09)
  • 作者簡介:楊菲菲(1995-),女,博士研究生,研究方向為農業信息技術。 E-mail:yangfeifei61@163.com。
  • 通訊作者: 李世娟 E-mail:yangfeifei61@163.com;lishijuan@caas.cn

Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing

YANG Feifei(), LIU Shengping, ZHU Yeping, LI Shijuan()   

  1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
  • Received:2021-05-08 Revised:2021-06-27 Online:2021-06-30 Published:2021-08-25
  • corresponding?author: Shijuan LI E-mail:yangfeifei61@163.com;lishijuan@caas.cn

摘要:

冬小麥澇漬脅迫頻發不僅嚴重影響區域糧食安全和生態安全,還威脅社會經濟穩定和可持續發展。為識別冬小麥澇漬脅迫及判別其脅迫程度,本研究設置冬小麥澇漬脅迫梯度盆栽試驗,采用ASD地物光譜儀和Gaiasky-mini2推掃式成像光譜儀分別測定葉片及冠層高光譜數據,結合植被指數、歸一化均值距離和光譜微分差信息熵等方法,監測冬小麥是否遭受澇漬脅迫并判別其澇漬脅迫程度。試驗結果顯示,簡單比值色素指數SRPI是識別澇漬脅迫冬小麥的最優植被指數。紅光吸收谷(RW:640~680 nm)是識別冬小麥澇漬脅迫程度的最優波段,在RW波段內,抽穗、開花和灌漿期的光譜微分差信息熵可判別冬小麥澇漬脅迫程度,脅迫程度越大,光譜微分差信息熵越大。本研究為澇漬脅迫監測提供了一種新方法,在澇漬脅迫精確防控中具有較好的應用前景。

關鍵詞: 高光譜遙感, 澇漬脅迫, 植被指數, 光譜微分差信息熵, 冬小麥

Abstract:

The frequent occurrence of waterlogging stress in winter wheat not only seriously affects regional food security and ecological security, but also threatens social and economic stability and sustainable development. In order to identify the waterlogging stress level of winter wheat, a waterlogging stress gradient pot experiment was set up in this research. Three factors were controlled: waterlogging stress level (control, slight waterlogging, severe waterlogging), stress duration (5 days, 10 days, 15 days) and wheat variety (YF4, JM31, JM38). Leaf and canopy hyperspectral data were measured by using ASD Field Spec3 and Gaiasky-mini2 imaging spectrometer, respectively. The data were collected from the first waterlogging day of winter wheat. The sunny and windless weather was selected and measured every 7 days until the wheat was mature. Combined with vegetation index, normalized mean distance and spectral derivative difference entropy, if winter wheat was under waterlogging stress was monitored and stress level was identified. The results showed that: 1) the spectral response characteristics of winter wheat under waterlogging stress changed significantly in RW, RE, NIR and 1650-1800 nm region, which may be due to the sensitivity of these regions to physiological parameters affecting the spectral response characteristics, such as pigment, nutrient, leaf internal structure, etc; 2) the simple ratio pigment index SRPI was the optimal vegetation index for identifying the waterlogging stress of winter wheat. The excellent performance of this vegetation index may come from its extreme sensitivity to the epoxidation state and photosynthetic efficiency of the xanthophyll cycle pigment; 3) the red light absorption valley (RW: 640-680 nm) region was the optimal region for identifying waterlogging stress level. In RW region, waterlogging stress level of winter wheat could be determined by the spectral derivative difference entropy at heading, flowering and filling stages. The greater the level of waterlogging stress, the greater the spectral derivative difference entropy. This may be due to the fact that the RW region was more sensitive to pigment content, and the spectral derivative difference entropy could reduce the effects of spectral noise and background. This study could provide a new method for monitoring waterlogging stress, and would have a good application prospect in the precise prevention and control of waterlogging stress. There are still shortcomings in this study, such as the difference between the pot experiment and the actual field environment, the lack of independent experimental verification, etc. Next research could add pot and field experiments, combine with cross-validation, to further verify the feasibility of this research method.

Key words: hyperspectral remote sensing, waterlogging stress, vegetation index, spectral derivative difference entropy, winter wheat

中圖分類號: 

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