Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (9): 1518-1528.doi: 10.3864/j.issn.0578-1752.2019.09.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Spectral Diagnosis of Leaf Area Density of Maize at Heading Stage Under Lodging Stress

ZHOU LongFei1,2,3,4,GU XiaoHe2,3,4(),CHENG Shu1,YANG GuiJun2,3,4,SUN Qian1,2,3,4,SHU MeiYan1,2,3,4   

  1. 1 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong
    2 Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture/Beijing Research Center for Information Technology in Agriculture, Beijing 100097
    3 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097
    4 Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097
  • Received:2018-12-11 Accepted:2019-01-30 Online:2019-05-01 Published:2019-05-16
  • Contact: XiaoHe GU E-mail:guxh@nercita.org.cn

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Abstract:

【Objective】 Leaf area density (LAD) reflects the difference of the total leaf area per volume in vertical direction and the distribution of the leaf area in the canopy with the change of height. The purpose of this study was to explore the characterization ability of maize leaf area density and its spectral response to lodging stress intensity. 【Method】 Taking lodging summer maize at heading stage as the research object, the multi-stage of LAD and canopy spectral data after lodging were obtained. The first-order differential and wavelet transform of the canopy spectrum of lodging maize were processed. Based on the correlation analysis between LAD, the first-order differential and wavelet decomposition coefficients of canopy spectrum, the sensitive bands of LAD and the optimal wavelet decomposition scale were screened. Partial least squares (PLS) method was used to construct the LAD spectral diagnosis model of lodging maize, and the accuracy of the model was verified by the measured samples.【Result】The LAD of maize increased with the increase of lodging stress, and LAD could effectively characterize the intensity of lodging stress and recovery ability of maize. After lodging, the canopy structure of maize changed greatly. The spectral reflectance of lodging maize canopy was higher than that of normal maize. The increase of near infrared band was higher than that of visible band. The stronger lodging intensity was, the higher spectral reflectance was. The sensitive bands of LAD were mainly distributed in the blue band 354-442 nm and 472-495 nm, the red band 649-829 nm, and the near infrared band 903-1 195 nm and 1 564-1 581 nm. Comparing with the first-order differential, the validation R 2 of LAD diagnostic model of maize lodging based on continuous wavelet transform increased by 6.08%-9.11%, and RMSE decreased by 23.08%-31.63%. The scale of wavelet decomposition had a certain influence on the diagnostic accuracy of LAD. The accuracy of the low- and medium-scale model was better than that of the high-scale model, and the model constructed by the fifth scale had the best fitting effect on LAD (R 2=0.898, RMSE=1.016). 【Conclusion】 The application of continuous wavelet transform to analyze the maize canopy hyperspectral could effectively diagnose maize leaf area density under lodging stress. It could provide necessary prior knowledge for remote sensing monitoring of maize lodging stress disaster.

Key words: heading stage, lodging stress, continuous wavelet transform, LAD, maize, hyperspectral

Fig. 1

Geographical location of the study area"

Fig. 2

Treatment of different lodging types (left-to-right: GD, JS, JD)"

Fig. 3

Test design"

Table 1

LAD statistics of continuous observation of maize lodging samples"

样本类型
Sample type		 采样数
Sampling numbers		 最大值
Maximum value		 最小值
Minimum value		 平均值
Average value		 标准差
Standard deviation
GD 30 15.111 0.948 3.122 3.956
JS 30 10.751 1.313 3.096 2.746
JD 30 4.120 0.878 1.432 0.706
CK 18 1.279 0.749 1.061 0.139

Fig. 4

Dynamic changes of LAD under different lodging treatments"

Fig. 5

Spectral reflectance curves of canopy under different lodging treatments"

Fig. 6

Correlation coefficient between first order differential and LAD"

Fig. 7

Correlation coefficient between wavelet coefficients and LAD"

Fig. 8

The determination coefficient between LAD and wavelet coefficients"

Table 2

Modeling and verification of LAD based on spectral transformation"

光谱变换形式
Spectral transformation		 尺度
Scale		 模型
Model		 建模Modeling 验证Verification
R2 RMSE R2 RMSE
一阶微分(R,) Y=196625×X407-5921573×X634-187548×X707-236231×X1130-2 0.653 1.408 0.823 1.486
CWT 2 Y=312.28×X357-5071.2×X493+710.68×X701+258.79×X760-4.1 0.724 1.219 0.873 1.143
4 Y=173.508×X365+176.319×X424+16.742×X700-49.675×X1161-3.233 0.703 1.264 0.884 1.094
5 Y=154.518×X418-5.031×X764+32.643×X913-26.993×X1451-2.034 0.702 1.266 0.898 1.016

Fig. 9

Validation of LAD model based on spectral transformation"

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