倒伏胁迫下玉米抽雄期叶面积密度光谱诊断

doi:10.3864/j.issn.0578-1752.2019.09.004

Abstract

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

ZHOU LongFei, GU XiaoHe, CHENG Shu, YANG GuiJun, SUN Qian, SHU MeiYan. Spectral Diagnosis of Leaf Area Density of Maize at Heading Stage Under Lodging Stress[J].Scientia Agricultura Sinica, 2019, 52(9): 1518-1528.

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References 34

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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

光谱变换形式 Spectral transformation	尺度 Scale	模型 Model	建模Modeling		验证Verification
光谱变换形式 Spectral transformation	尺度 Scale	模型 Model	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

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

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