基于优化植被指数组合的多品种辣椒叶片叶绿素数值估测

doi:10.3969/j.issn.1004-1524.20221456

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (9): 2109-2120.DOI: 10.3969/j.issn.1004-1524.20221456

基于优化植被指数组合的多品种辣椒叶片叶绿素数值估测

王宇¹(), 汪泓¹^,^*(), 肖玖军²^,³, 李可相²^,³, 邢丹⁴, 张永亮¹, 陈阳²^,³, 张蓝月²^,³

1.贵州大学矿业学院,贵州贵阳 550025
2.贵州科学院贵州省山地资源研究所,贵州贵阳 550001
3.贵州省土地绿色整治工程研究中心,贵州贵阳 550001
4.贵州省农业科学院辣椒研究所,贵州贵阳 550009

收稿日期:2022-10-11 出版日期:2023-09-25 发布日期:2023-10-09
作者简介:王宇(1998—),女,贵州安顺人,硕士研究生,研究方向为摄影测量与遥感。E-mail: 2029807592@qq.com
通讯作者: 汪泓,E-mail: 7653606@qq.com
基金资助:
贵州省科技支撑计划项目(黔科合支撑〔2020〕1Y172号);贵州省科技支撑计划项目(黔科合支撑〔2021〕一般496号);贵州省科技支撑计划项目(黔科合支撑〔2021〕一般173号);贵州科学院青年基金项目(黔科院J字〔2018〕25号)

Numerical estimation of chlorophyll in pepper leaves based on optimized vegetation index combination

WANG Yu¹(), WANG Hong¹^,^*(), XIAO Jiujun²^,³, LI Kexiang²^,³, XING Dan⁴, ZHANG Yongliang¹, CHEN Yang²^,³, ZHANG Lanyue²^,³

1. Mining College, Guizhou University, Guiyang 550025, China
2. Institute of Mountain Resources of Guizhou, Guizhou Academy of Sciences, Guiyang 550001, China
3. Engineering Research Center for Land Green Consolidation of Guizhou, Guiyang 550001, China
4. Pepper Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550009, China

Received:2022-10-11 Online:2023-09-25 Published:2023-10-09

摘要/Abstract

摘要：

叶片叶绿素与植被生长状况息息相关,SPAD值能够反映作物叶片叶绿素含量,不同品种辣椒外形和生理生态参数具有明显差异,因此,准确、快速地估算SPAD值具有重要意义。以4个不同品种辣椒为研究对象,测量其SPAD值,对原始光谱进行倒数、对数、倒数对数、一阶微分和二阶微分变换,通过将变换光谱替换原始光谱来优化植被指数,对比优化植被指数和经典植被指数搭建模型的差异,最终得到不同品种辣椒SPAD值和高光谱之间的关系,寻找SPAD值的最优反演模型。结果表明:不同品种辣椒冠层光谱特性存在差异;辣椒叶片建模集、验证集和全样本SPAD值的变化趋势均为线椒大于朝天椒;基于倒数对数光谱优化的植被指数除了CI_{red edge}外,其余植被指数的相关系数均高于经典植被指数;基于lg1/R-VI搭建的随机森林模型无论是建模集还是验证集精度均较好,适合于不同品种辣椒SPAD值的估算,其中全样本模型测试集决定系数(R²)为0.83,平均绝对误差(MAD)为1.90,验证集R²和MAD分别为0.45和1.26。

关键词: 辣椒, SPAD值, 冠层高光谱, 优化植被指数, 随机森林模型

Abstract:

Leaf chlorophyll is closely related to vegetation growth, and it is an important parameter that directly affects vegetation growth and health status. SPAD value can reflect the chlorophyll content of crop leaves. The larger the SPAD value, the higher the chlorophyll content, and the smaller the SPAD value, the lower the chlorophyll content. The shape and physiological and ecological parameters of different varieties of pepper are significantly different, so it is of great significance to accurately and quickly estimate the SPAD value. The SPAD values of 4 different varieties of peppers, Qianjiao 8, Hongla 18, Layan 101, and Red Globe, were measured. The reciprocal, logarithm, reciprocal logarithm, first-order differential and second-order differential transformation of the original spectrum were performed. The vegetation index was optimized by replacing the original spectrum with the transformed spectrum, and the difference between the optimized vegetation index and the classical vegetation index was compared. Finally, the relationship between SPAD values and hyperspectral of different varieties of peppers was obtained, and the optimal inversion model of SPAD values was found. The results showed that there were differences in canopy spectral characteristics of different pepper varieties. SPAD values of modeling set, validation set and full sample of pepper leaves showed that the change trend of line pepper was greater than that of Chao tian pepper. The correlation coefficients of vegetation indices based on reciprocal logarithmic spectral optimization were higher than classical vegetation indices except CI_{red edge}. The random forest model based on lg1/R-VI had good accuracy in both the modeling set and the validation set, which was suitable for the estimation of SPAD values of different varieties of pepper leaves. The coefficient of determination (R²) of full sample model test set was 0.83, mean absolute deviation (MAD) was 1.90, and R²of validation set was 0.45, MAD was 1.26.

Key words: pepper, SPAD, canopy hyperspectral, optimizing vegetation index, random forest model

中图分类号:

S641.3

王宇, 汪泓, 肖玖军, 李可相, 邢丹, 张永亮, 陈阳, 张蓝月. 基于优化植被指数组合的多品种辣椒叶片叶绿素数值估测[J]. 浙江农业学报, 2023, 35(9): 2109-2120.

WANG Yu, WANG Hong, XIAO Jiujun, LI Kexiang, XING Dan, ZHANG Yongliang, CHEN Yang, ZHANG Lanyue. Numerical estimation of chlorophyll in pepper leaves based on optimized vegetation index combination[J]. Acta Agriculturae Zhejiangensis, 2023, 35(9): 2109-2120.

图/表 6

表1 辣椒SPAD值统计性分析

Table 1 Statistical analysis of SPAD value of pepper

统计参数 Statistical parameter	辣研101号 Layan 101		红全球 Red global		黔椒8号 Qianjiao No.8		红辣18号 Red Hot 18		全样本 Whole sample
统计参数 Statistical parameter	建模集 Modeling set	验证集 Validation set	建模集 Modeling set	验证集 Validation set	建模集 Modeling set	验证集 Validation set	建模集 Modeling set	验证集 Validation set	建模集 Modeling set	验证集 Validation set
最小值Minimum value	29.4	31.2	34.5	35.9	40.9	46.1	45.1	52.1	29.4	31.2
最大值Maximum value	78.2	68.3	77.8	72	79.9	71.5	73.5	70.5	79.9	74.9
平均值Mean value	53.46	58.32	55.84	51.38	58.09	60.65	57.74	59.72	57.04	55.28
标准差Standard deviation	12.32	10.45	11.93	12.82	10.17	7.97	7.88	5.08	10.45	11.41
变异系数	0.23	0.18	0.21	0.25	0.18	0.13	0.14	0.09	0.18	0.21
Coefficient of variation

图1 不同品种辣椒变换光谱反射率 A代表黔椒8号;B代表红辣18号;C代表辣研101号;D代表红全球。FR表示原始光谱反射率,F1/R表示倒数光谱反射率,FlgR表示对数光谱反射率,Flg(1/R)表示倒数对数光谱反射率,FR'表示一阶微分光谱反射率,FR″表示二阶微分光谱反射率。

Fig.1 Spectral reflectance of different pepper varieties A represents Qianjiao No.8, B represents Hongla 18, C represents Layan 101, D represents Red Global. FRrepresents the original spectral reflectance, F1/R represents the reciprocal spectral reflectance, FlgR represents the logarithmic spectral reflectance, Flg(1/R) represents the reciprocal logarithmic spectral reflectance, FR' represents the first-order differential spectral reflectance, FR″ represents the second-order spectral reflectance.

图2 SPAD值与植被指数相关性分析热力图 CARI,叶绿素吸收率指数;MCARI,修正型叶绿素吸收植被指数;MTCI,陆地植被指数;NDVI,归一化植被指数;TCARI,改进型叶绿素吸收植被指数;OSVAI,土壤调节植被指数; C I r e d ? e d g e,红边叶绿素指数。

Fig.2 Heat map of correlation analysis between SPAD and vegetation index CARI, Chlorophyll absorption ratio index; MCARI, Modified chlorophyll absorption ratio index; MTCI, MERIS terrestrial chlorophyll index; NDVI, Normalized difference vegetation index; TCARI, Transformed chlorophyll absorption in reflectance index; OSVAI, Optimized soil-adjusted vegetation index; C I r e d ? e d g e, Red edge chlorophyll index.

表2 全样本优化植被指数与SPAD值相关性分析

Table 2 Correlation analysis between whole sample optimized vegetation index and SPAD value

经典植被指数 Classical vegetation index		优化植被指数 Optimize vegetation index
R-VI	r	1/R-VI	r	lgR-VI	r	lg1/R-VI	r	R'-VI	r	R″-VI	r
CARI	0.02	CARI	0.18^*	CARI	0.09	CARI	-0.03	CARI	-0.01	CARI	-0.02
MCRAI	0.06	MCARI	0.01	MCARI	-0.12	MCARI	0.14^*	MCARI	0.03	MCARI	0.03
MTCI	-0.08	MTCI	-0.01	MTCI	0.11	MTCI	-0.20^**	MTCI	-0.04	MTCI	0.03
NDVI	0.13	NDVI	0.004	NDVI	0.001	NDVI	-0.16^*	NDVI	0.09	NDVI	0.04
TCARI	0.06	TCARI	-0.06	TCARI	-0.08	TCARI	0.20^**	TCARI	-0.05	TCARI	-0.04
OSVAI	0.09	OSVAI	-0.07	OSVAI	-0.08	OSVAI	0.22^**	OSVAI	-0.11	OSVAI	-0.02
T/O	0.04	T/O	0.03	T/O	0.04	T/O	0.18^*	T/O	0.03	T/O	-0.03
$C I r e d ? e d g e$	0.19^**	$C I r e d ? e d g e$	-0.11	$C I r e d ? e d g e$	-0.15^*	$C I r e d ? e d g e$	-0.02	$C I r e d ? e d g e$	0.07	$C I r e d ? e d g e$	0.06

表2 全样本优化植被指数与SPAD值相关性分析

Table 2 Correlation analysis between whole sample optimized vegetation index and SPAD value

经典植被指数 Classical vegetation index		优化植被指数 Optimize vegetation index
R-VI	r	1/R-VI	r	lgR-VI	r	lg1/R-VI	r	R'-VI	r	R″-VI	r
CARI	0.02	CARI	0.18^*	CARI	0.09	CARI	-0.03	CARI	-0.01	CARI	-0.02
MCRAI	0.06	MCARI	0.01	MCARI	-0.12	MCARI	0.14^*	MCARI	0.03	MCARI	0.03
MTCI	-0.08	MTCI	-0.01	MTCI	0.11	MTCI	-0.20^**	MTCI	-0.04	MTCI	0.03
NDVI	0.13	NDVI	0.004	NDVI	0.001	NDVI	-0.16^*	NDVI	0.09	NDVI	0.04
TCARI	0.06	TCARI	-0.06	TCARI	-0.08	TCARI	0.20^**	TCARI	-0.05	TCARI	-0.04
OSVAI	0.09	OSVAI	-0.07	OSVAI	-0.08	OSVAI	0.22^**	OSVAI	-0.11	OSVAI	-0.02
T/O	0.04	T/O	0.03	T/O	0.04	T/O	0.18^*	T/O	0.03	T/O	-0.03
$C I r e d ? e d g e$	0.19^**	$C I r e d ? e d g e$	-0.11	$C I r e d ? e d g e$	-0.15^*	$C I r e d ? e d g e$	-0.02	$C I r e d ? e d g e$	0.07	$C I r e d ? e d g e$	0.06

表3 优化植被指数组合估算SPAD值的RF模型结果

Table 3 RF model results of SPAD value estimated by optimizing vegetation index combination

品种 Variety	优化植被指数组合 Optimize vegetation	建模集 Modeling set		验证集 Validation set		品种 Variety	优化植被指数组合 Optimize vegetation	建模集 Modeling set		验证集 Validation set
品种 Variety	优化植被指数组合 Optimize vegetation	R²	MAD	R²	MAD	品种 Variety	优化植被指数组合 Optimize vegetation	R²	MAD	R²	MAD
辣研101号	F_R-VI	0.90	2.33	0.80	1.74	黔椒8号	F_R-VI	0.86	1.96	0.80	1.61
Layan 101	F_lgR-VI	0.87	2.29	0.64	1.67	Qianjiao	F_lgR-VI	0.87	2.10	0.65	1.38
	F_1/R-VI	0.90	2.42	0.90	2.45	No.8	F_1/R-VI	0.85	2.01	0.72	1.66
	F_lg1/R-VI	0.87	2.22	0.79	2.03		F_lg1/R-VI	0.87	2.24	0.83	2.07
	F_R'-VI	0.88	2.43	0.61	1.44		F_R'-VI	0.80	1.61	0.42	1.13
	F_R″-VI	0.84	2.05	0.51	1.37		F_R″-VI	0.85	1.64	0.46	1.25
红全球	F_R-VI	0.80	1.81	0.83	1.55	红辣18号	F_R-VI	0.83	1.95	0.63	1.26
Red global	F_lgR-VI	0.85	1.74	0.95	1.31	Red Hot 18	F_lgR-VI	0.79	1.83	0.66	1.40
	F_1/R-VI	0.83	1.84	0.56	1.28		F_1/R-VI	0.80	1.84	0.63	1.35
	F_lg1/R-VI	0.85	1.81	0.94	1.58		F_lg1/R-VI	0.76	1.74	0.51	1.22
	F_R'-VI	0.84	1.71	0.49	1.12		F_R'-VI	0.81	1.84	0.30	1.10
	F_R″-VI	0.83	1.69	0.71	0.86		F_R″-VI	0.84	1.83	0.30	0.71
全样本	F_R-VI	0.80	1.84	0.39	1.22
Whole	F_lgR-VI	0.82	1.91	0.42	1.27
sample	F_1/R-VI	0.80	1.80	0.42	1.25
	F_lg1/R-VI	0.83	1.90	0.45	1.26
	F_R'-VI	0.79	1.84	0.41	1.23
	F_R″-VI	0.79	1.83	0.40	1.22

图3 不同品种辣椒的SPAD值最优估算模型实测值与预测值关系

Fig.3 Relationship between the measured value and the predicted value of SPAD optimal estimation model of different varieties of pepper

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基于优化植被指数组合的多品种辣椒叶片叶绿素数值估测

Numerical estimation of chlorophyll in pepper leaves based on optimized vegetation index combination

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