Prediction of Rice Canopy SPAD Value Based on UAV Multispectral Images

doi:10.11924/j.issn.1000-6850.casb2022-0176

Abstract

Abstract:

The aim of this study is to compare and screen SPAD estimation models of rice canopy, and to provide a basis for inversing SPAD value of rice by unmanned aerial vehicle (UAV) multispectral remote sensing. This paper used UAV to obtain the canopy multispectral images of rice at jointing stage, heading stage and milky maturity stage. Seven commonly used vegetation indices were selected and three regression methods were used to establish the SPAD value inversion model of rice leaves. The results showed that the vegetation indices with the highest correlation coefficient with SPAD value of rice leaves were different at different growth stages. GNDVI was the highest at jointing stage, CI_Greenwas the highest at heading stage, and CI_Rededge was the highest at milky maturity stage. The heading stage was the best inversion stage of SPAD value, and the model had good modeling precision and estimation effect. According to the accuracy test, multivariate linear regression model had relatively high modeling accuracy, and partial least squares regression had the best estimation effect. Thus, this research provides a new technology to supervise the growth information of rice and other crops. The experimental results can provide methods and reference for the real-time and nondestructive monitoring of rice growth.

Key words: rice, SPAD value, predictive model, unmanned aerial vehicle, multispectral remote sensing

TIAN Ting, ZHANG Qing, XU Wen. Prediction of Rice Canopy SPAD Value Based on UAV Multispectral Images[J]. Chinese Agricultural Science Bulletin, 2023, 39(4): 149-153.

Figures/Tables 6

植被指数	计算公式或定义	文献
归一化植被指数(NDVI)	$ρ N I R - ρ R e d ρ N I R + ρ R e d$	[12]
绿色归一化植被指数(GNDVI)	$ρ N I R - ρ G r e e n ρ N I R + ρ G r e e n$	[13]
比值植被指数(RVI)	$ρ N I R ρ R e d$	[14]
土壤调节植被指数(SAVI)	$(1 + L) (ρ N I R - ρ R e d) ρ N I R + ρ R e d + L$ L=0.5	[15]
绿色叶绿素指数(CI_Green)	$ρ N I R ρ G r e e n - 1$	[16]
红边叶绿素指数(CI_Rededge)	$ρ N I R ρ R e d e d g e - 1$	[16]
修正叶绿素吸收指数(MCARI)	$[ρ R e d e d g e - ρ R e d - 0.2 × (ρ R e d e d g e - ρ G r e e n)] × ρ R e d e d g e ρ R e d$	[17]

植被指数	计算公式或定义	文献
归一化植被指数(NDVI)	$ρ N I R - ρ R e d ρ N I R + ρ R e d$	[12]
绿色归一化植被指数(GNDVI)	$ρ N I R - ρ G r e e n ρ N I R + ρ G r e e n$	[13]
比值植被指数(RVI)	$ρ N I R ρ R e d$	[14]
土壤调节植被指数(SAVI)	$(1 + L) (ρ N I R - ρ R e d) ρ N I R + ρ R e d + L$ L=0.5	[15]
绿色叶绿素指数(CI_Green)	$ρ N I R ρ G r e e n - 1$	[16]
红边叶绿素指数(CI_Rededge)	$ρ N I R ρ R e d e d g e - 1$	[16]
修正叶绿素吸收指数(MCARI)	$[ρ R e d e d g e - ρ R e d - 0.2 × (ρ R e d e d g e - ρ G r e e n)] × ρ R e d e d g e ρ R e d$	[17]

生育期	样本数	最小值	最大值	平均值	标准差	变异系数/%
拔节期	27	38.08	45.13	42.02	2.14	5.09
抽穗期	27	36.22	42.93	40.27	1.73	4.30
乳熟期	27	24.38	43.47	36.65	4.90	13.34

植被指数	拔节期	抽穗期	乳熟期
NDVI	0.680^**	0.755^**	0.836^**
GNDVI	0.685^**	0.782^**	0.838^**
RVI	0.606^**	0.754^**	0.791^**
SAVI	0.600^**	0.368	0.677^**
CI_Green	0.613^**	0.791^**	0.826^**
CI_Rededge	0.630^**	0.310	0.864^**
MCARI	0.452^*	0.272	0.522^**

生育期	类型	模型	R²	RMSE
拔节期	一元线性回归	y=6.698+44.174GNDVI	0.51	1.615
	多元线性回归	y=-73.683-102.534NDVI+254.286GNDVI-0.794RVI+56.845SAVI-2.323CI_Green+2.464CI_Rededge	0.65	1.657
	偏最小二乘回归	y=34.01428+1.1030GNDVI+0.8581NDVI+6.2888CI_Rededge	0.44	1.636
抽穗期	一元线性回归	y=31.278+1.184CI_Green	0.68	1.005
	多元线性回归	y=-108.744+296.452NDVI-163.978GNDVI-1.570RVI+5.596CI_Green	0.75	0.986
	偏最小二乘回归	y=31.24788+1.1831CI_Green+0.0277GNDVI+0.0158NDVI	0.68	0.948
乳熟期	一元线性回归	y=18.225+33.273CI_Rededge	0.79	2.558
	多元线性回归	y=-136.185+140.624NDVI+231.001GNDVI+1.137RVI-181.482SAVI-2.093CI_Green-24.218CI_Rededge	0.92	1.926
	偏最小二乘回归	y=8.00065+29.0085CI_Rededge+7.9596GNDVI+8.3624NDVI	0.79	2.368

生育期	模型类型	验证结果
生育期	模型类型	R²	RMSE
拔节期	一元线性回归	0.38	1.5686
	多元线性回归	0.34	1.6790
	偏最小二乘回归	0.33	1.6166
抽穗期	一元线性回归	0.56	1.2256
	多元线性回归	0.48	1.4923
	偏最小二乘回归	0.56	1.2254
乳熟期	一元线性回归	0.61	2.4660
	多元线性回归	0.48	2.8132
	偏最小二乘回归	0.59	2.5143

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