基于光谱特征参数的琯溪蜜柚叶片叶绿素含量估算

栗方亮; 孔庆波; 张青

doi:10.19303/j.issn.1008-0384.2021.12.008

基于光谱特征参数的琯溪蜜柚叶片叶绿素含量估算

doi: 10.19303/j.issn.1008-0384.2021.12.008

福建省农业科学院土壤肥料研究所, 福建　福州　350013

基金项目: 福建省自然科学基金项目（2019J01106）；福建省属公益类科研院所基本科研专项（2021R1025008）

详细信息

作者简介:
栗方亮（1980−），男，博士，助理研究员，研究方向：土壤生态与植物营养（E-mail：lifl007@qq.com）

通讯作者:
孔庆波（1978−），男，博士，副研究员，研究方向：植物营养与水肥一体化（E-mail:qbkong@qq.com）

中图分类号: S 666.3; S 127
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出版历程
- 收稿日期: 2021-04-20
- 修回日期: 2021-09-13
- 网络出版日期: 2021-12-30
- 刊出日期: 2021-12-28

Spectral Measurements-based Estimation for Chlorophyll in Guanxi Honey Pomelo Leaves

Institute of Soil and Fertilizer, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian　350013, China

摘要

摘要: 目的利用光谱特征参数建立蜜柚叶片叶绿素含量估算模型，为实现快速、无损、精确的叶绿素含量估算提供理论依据和技术支持。方法通过提取原始光谱及一阶微分光谱特征波段和光谱特征变量，分析蜜柚叶片高光谱特征参数与叶绿素相对含量（SPAD）值之间的相关关系，构建单变量估算模型和多元回归模型，并确定蜜柚叶绿素含量的最佳估算模型。结果在350～1050 nm的波段，不同SPAD 值的蜜柚叶片反射光谱存在明显差异，光谱反射率均随叶片叶绿素含量升高而降低。原始光谱和一阶微分光谱与叶绿素含量在可见光范围内有多波段相关性显著。原始光谱曲线敏感波长为576 nm和701 nm，一阶微分曲线的敏感波长为691 nm和748 nm。在利用光谱特征参数建立的回归模型中，根据拟合验证精度筛选出多个拟合模型，其中多元回归模型Y_SPAD=54.67−15.75 NDVI′_691,748−10.60 GRVI_550,770+6565.6 R′₆₉₁−6784.58 DVI′_691,748，其拟合决定系数R²为0.894，验证决定系数R²为0.8356，RMSE为7.07，可确定为蜜柚叶片叶绿素含量的最佳预测模型；而一阶微分归一化植被指数NDVI′_691,748和差值植被指数DVI′_691,748为单变量的回归模型的拟合决定系数R²分别为0.824和0.798，验证决定系数R²分别为0.797和0.7918，RMSE分别为13.21和12.56。结论综合建模精度和模型验证精度，基于高光谱指数NDVI′_691,748、GRVI_550,770、R′₆₉₁和DVI′_691,748的多元回归模型可确定为蜜柚叶片叶绿素含量的最佳估算模型。
- 高光谱 /
- 蜜柚 /
- 叶绿素 /
- 光谱指数
Abstract: Objective A hyperspectral estimation model for the chlorophyll content in the leaves of honey pomelo was developed using spectral measurements on the characteristic parameters for the establishment of a rapid, noninvasive, accurate determination method. Method Characteristic bands of spectrum of the leaves were obtained. The first-order differential spectrum and spectral characteristic variables were used to analyze the correlation between the hyperspectral bands and the relative chlorophyll content (SPAD) of pomelo leaves. Univariate estimation and multiple regression models were constructed and compared to arrive at the best prediction model. Result At the wavelengths between 350 nm and 1 050 nm, the reflectance spectra on the pomelo leaves of varied SPADs decreased significantly with increasing chlorophyll content. SPAD significantly correlated with either the original or the first-order differential spectrum at several wavelengths in the visible light range. The sensitive wavelengths for the original spectral curve were 576 nm and 701 nm, and 691 nm and 748 nm for the first order differential spectrum. Based on the precision of fitting, the one that based on the hyperspectral Y_SPAD=54.67−15.75 NDVI_′691,748−10.60 GRVI_550,770+6 565.6 R′₆₉₁−6 784.58 DVI′_691,748 of 3 multiple regression models displayed the R² of 0.894, the verification R² of 0.835 6, and the RMSE of 7.07 and was selected. The univariate regression models applied the first-order differential normalized vegetation index NDVI′_691,748 and the differential vegetation index DVI′_691,748 had R² of 0.824 and 0.798, respectively, the validation R² of 0.797 and 0.7918, respectively, and RMSEs of 13.21 and 12.56, respectively. Conclusion The multiple regression model based on hyperspectral indices NDVI′_691,748, GRVI_550,770, R′₆₉₁ and DVI′_691,748 could be adequately applied for estimating chlorophyll content of honey pomelo leaves.
- Hyperspectral /
- honey pomelo /
- chlorophyll /
- spectral parameters

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图 1 不同叶绿素含量的蜜柚叶片高光谱曲线

Figure 1. Hyperspectral curves of pomelo leaves with varied chlorophyll contents

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图 2 蜜柚叶片叶绿素含量与原始光谱反射率的相关性

Figure 2. Correlation between chlorophyll content and original reflectance spectrum of pomelo leaves

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图 3 蜜柚叶片叶绿素含量与一阶光谱反射率的相关性

Figure 3. Correlation between chlorophyll content and first-order reflectance spectrum of pomelo leaves

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图 4 蜜柚叶片SPAD实测值与预测值比较

Figure 4. Measured and predicted SPADs of pomelo leaves

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表 1 蜜柚叶片样本的SPAD值

Table 1. SPADs of pomelo leaf samples

组别 Group	样本数 Sample number	最小值 Minimum value	最大值 Maximum value	平均值 Average value	标准偏差 Standard deviation
SPAD1	30	26.46	43.43	35.57	4.63
SPAD2	30	46.25	78.46	68.61	4.95
SPAD3	30	78.80	85.21	81.14	4.35
验证组 Validation group	30	26.12	77.21	53.61	16.56

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表 2 选取的高光谱特征参数及说明

Table 2. Selected hyperspectral characteristic parameters and descriptions

光谱特征参数类型 Type of spectral characteristic parameter	光谱特征参数名称 Name of spectral characteristic parameter	光谱特征参数说明 Description of spectral characteristic parameter
光谱位置变量 Spectral position variable	D_b（蓝边幅值Blue edge amplitude）	490～530 nm内一阶微分的最大值 Maximum value of first-order differential in 490-530 nm
	D_y（黄边幅值Yellow edge amplitude）	560～640 nm内一阶微分的最大值 Maximum value of first-order differential in 560-640 nm
	D_r（红边幅值Red edge amplitude）	680～760 nm内一阶微分的最大值 Maximum value of first-order differential in 680-760 nm
	λ_b（蓝边位置Blue edge position）	D_b对应的波长位置Wavelength position corresponding to D_b
	λ_y（黄边位置Yellow edge position）	D_y对应的波长位置 Wavelength position corresponding to D_y
	λ_r（红边位置Red edge position）	D_r对应的波长位置 Wavelength position corresponding to D_r
	R_g（绿峰幅值Green peak amplitude）	510～560 nm内光谱反射率的最大值 Maximum spectral reflectance in 510-560 nm
	R_r（红谷幅值Red valley amplitude）	640～680 nm内光谱反射率的最小值 Minimum spectral reflectance in 640-680 nm
光谱面积变量 Spectral area variable	SD_b（蓝边面积Blue edge area）	490～530 nm内一阶微分光谱值的总和 Sum of first-order differential spectral values in 490-530 nm
	SD_y（黄边面积Yellow edge area）	560～640 nm内一阶微分光谱值的总和 Sum of first-order differential spectral values in 560-640 nm
	SD_r（红边面积Red edge area）	680～760 nm内一阶微分光谱值的总和 Sum of first-order differential spectral values in 680-760 nm
	VI1=R_g/R_r	绿峰R_g与红谷R_r幅值的比值 Ratio of green peak amplitude R_g to red valley amplitude R_r
	VI2=（R_g−R_r）/（R_g+R_r）	绿峰R_g与红谷R_r幅值的归一化值 Normalized value of green peak amplitude R_g to red valley amplitude R_r
	VI3=SD_r/SD_b	红边与蓝边面积比值 Ratio of red edge area to blue edge area
	VI4=（SD_r−SD_b）/（SD_r+SD_b）	红边与蓝边面积的归一化值 Normalized value of red edge area and blue edge area
植被指数变量 Vegetation index variable	DVI（差值植被指数Difference vegetation index）	DVI_{(λ1, λ2)}= R_λ1− R_λ2
	RVI（比值植被指数Ratio vegetation index）	RVI_{(λ1, λ2)}= R_λ1/R_λ2
	NDVI（归一化植被指数 Normalized difference vegetation index）	NDVI_{(λ1, λ2)}=( R_λ1− R_λ2)/（R_λ1+ R_λ2）
	GRVI（绿波段比值植被指数 Green band ratio vegetation index）	GRVI_{(550, 770)}= R₅₅₀/ R₇₇₀
	GNDVI（绿波段归一化植被指数 Green band normalized difference vegetation index）	GNDVI_{(550, 770)} =( R₅₅₀−R₇₇₀)/（R₅₅₀+ R₇₇₀）

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表 3 光谱特征参数及敏感波段植被指数与蜜柚叶绿素含量相关关系

Table 3. Correlation between spectral characteristic parameters, sensitive band vegetation indices and chlorophyll contents of pomelo leaves

光谱特征参数名称 Name of spectral characteristic parameter	相关系数r Correlation coefficient	光谱特征参数名称 Name of spectral characteristic parameter	相关系数r Correlation coefficient
D_b	−0.703**	VI4	0.744**
D_y	0.255	R₅₇₆	−0.799**
D_r	−0.532**	R₇₀₁	−0.829**
λ_b	0.498**	R′₆₉₁	−0.900**
λ_y	−0.134	R′₇₄₈	0. 940**
λ_r	0.707**	DVI_576,701	0.718**
R_g	−0.734**	RVI_576,701	0.620**
R_r	−0.525**	NDVI_576,701	−0.560**
SD_b	−0.716**	DVI′_691,748	0.880**
SD_y	−0.541**	RVI′_691,748	0.708**
SD_r	0.546**	NDVI′_691,748	0.940**
VI1	−0.714**	GRVI_550,770	0.910**
VI2	−0.738**	GNDVI_550,770	0.850**
VI3	0.745**

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表 4 蜜柚叶片叶绿素含量的单变量估测模型

Table 4. Univariate estimation models for chlorophyll content of pomelo leaves

光谱特征参数名称 Name of spectral characteristic parameter	估测模型 Estimation model	R²	RMSE	RE%
NDVI′_691,748 GRVI_550,770 R′₆₉₁ DVI′_691,748 R′₇₄₈ GNDVI_550,770	Y=71.26−23.82 x−16.46 x² Y=109.38−292.40x+259.51x² Y=80.11+82367.6x+3415x² Y=68.58−5369.37x+195635.43x² Y=20.99+28303.8x−3056x² Y=158.79−460.33x+449.68x²	0.824 0.816 0.802 0.798 0.797 0.796	12.40 13.58 13.98 15.45 15.59 15.98	17.01 18.54 18.98 20.14 20.45 21.41

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表 5 蜜柚叶片叶绿素含量估测模型的拟合精度比较

Table 5. Fitting accuracy of estimation models for chlorophyll content of pomelo leaves

光谱特征参数名称 Name of spectral characteristic parameter	实测值与估测值拟合方程 Fitting equation between measured value and estimated value	R²	RMSE	RE%
NDVI′_691,748 GRVI_550,770 R′₆₉₁ DVI′_691,748 R′₇₄₈ GNDVI_550,770 多元回归 Multiple regression	Y=1.25x−5.6191 Y=1.1633x−0.945 Y=1.1982x−2.8258 Y=1.2451x−5.3558 Y=1.9522x−35.447 Y=0.7895x+14.453 Y=1.2732x−7.2039	0.797 0.7293 0.7535 0.7918 0.7520 0.6044 0.8356	13.21 12.86 11.08 12.56 25.03 9.92 7.07	17.45 17.77 15.59 18.54 36.03 15.99 10.70

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