基于高光谱数据的滴灌甜菜叶片全氮含量估算

doi:10.3724/SP.J.1006.2020.94045

Abstract

Abstract:

The purpose of this paper is to clarify the quantitative relationship between total nitrogen content of sugar beet and high-resolution vegetation remote sensing, to explore the establishment of an optimal estimation model for total nitrogen content of sugar beet, and to monitor the growth of sugar beet. Xinjiang drip-irrigated sugar beet (Beta356) was selected to collect the reflectance spectra of leaf cluster during the leaves rapid growth period, root expansion period and sugar accumulation period by the ASD field hyperspectral apparatus. The total nitrogen content was also measured and the relationship between original spectral reflectance and total nitrogen content was analyzed. According to the correlation between the first-order differential spectral reflectance and total nitrogen content, a total nitrogen content estimation model was established. The model with spectral characteristic parameter Dr762 power function had a good ability to estimate total nitrogen content in leaves of beet, with the determination coefficient, relative error, and root mean square error of 0.747, 21.635, and 4.914, respectively. Various function estimation models were established based on vegetation index and leaf total nitrogen content. The linear function under vegetation index Dr762-Dr496 had better ability to estimate leaf total nitrogen content. Its determinant coefficient, relative error, and root mean square error were 0.794, 23.008, and 5.372, respectively.

Key words: total nitrogen, hyperspectral, characteristic parameters, vegetation index, estimation model

LI Zong-Fei,SU Ji-Xia,FEI Cong,LI Yang-Yang,LIU Ning-Ning,DAI Yu-Xiang,ZHANG Kai-Xiang,WANG Kai-Yong,FAN Hua,CHEN Bing. Estimation of total nitrogen content in sugarbeet leaves under drip irrigation based on hyperspectral characteristic parameters and vegetation index[J].Acta Agronomica Sinica, 2020, 46(4): 557-570.

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光谱特征参数与植被指数 Spectral characteristic parameter and Spectral index	定义 Definition
红边斜率 Red edge slope (Dr)	680-760 nm内最大一阶微分光谱值 Maximum first order differential spectrum in 680-760 nm
红边位置 Red edge position (λr)	680-760 nm内最大的一阶微分光谱值对应的波长 The wavelength corresponding to the largest first-order differential spectrum in 680-760 nm
红边面积 Red edge area (SDr)	680-760 nm内一阶微分光谱值的总和 The sum of the first-order differential spectral values in 680-760 nm
黄边斜率 Yellow edge slope (Dy)	560-640 nm内最大一阶微分光谱值 Maximum first order differential spectrum in 560-640 nm
黄边位置 Yellow edge position (Λy)	560-640 nm内最大一阶微分光谱值对应的波长 The wavelength corresponding to the largest first-order differential spectrum in 560-640 nm
黄边面积 Yellow edge area (SDy)	560-640 nm内一阶微分光谱值的总和 The sum of the first-order differential spectral values in 560-640 nm
蓝边斜率 Blue edge slope (Db)	490-530 nm内最大一阶微分光谱值 Maximum first order differential spectrum in 490-530 nm
蓝边位置 Blue edge position (λb)	490-530 nm内最大一阶微分光谱值对应的波长 The wavelength corresponding to the largest first-order differential spectrum in 490-530 nm
蓝边面积 Blue edge area (SDb)	490-530 nm内一阶微分光谱值的总和 The sum of the first-order differential spectral values in 490-530 nm
比值植被指数 Ratio vegetation index (RVI)	R_λ1/R_λ2
差值植被指数 Difference vegetation index (DVI)	R_λ1- R_λ2
归一化比值植被指数 Normalized ratio vegetation index (NDVI)	(R_NIR-R_red)/(R_NIR+R_red)
红边归一化差异指数 Red edge normalized difference index (NDI)	(DR_λ1-DR_λ2)/(DR_λ1+DR_λ2)

光谱特征参数 Spectral characteristic parameter	相关关系 Correlation	植被指数 Vegetation index	相关性Correlation
Dr	0.590^**	原始光谱比值植被指数Ratio vegetation index R1104/R767	0.358^**
λr	0.497^**	原始光谱差值植被指数Difference vegetation index R1104-R767	0.280^**
SDr	0.601^**	原始光谱归一化植被指数Normalized ratio vegetation index (R1104-R767)/(R1104+R767)	0.364^**
Dy	0.074	原始光谱比值植被指数Ratio vegetation index R767/R604	0.652^**
Λy	0.089	原始光谱差值植被指数Difference vegetation index R767-R604	0.433^**
SDy	-0.086	一阶微分比值植被指数Ratio vegetation index Dr1138/Dr762	-0.169
Db	0.072	一阶微分差值植被指数Difference vegetation index Dr1138-Dr762	-0.744^**
λb	0.506^**	一阶微分归一化植被指Normalized ratio vegetation index (Dr1138-Dr762)/(Dr1138+Dr762)	0.500^**
SDb	-0.216	一阶微分比值植被指数Ratio vegetation index Dr762/Dr496	0.586^**
R604	-0.267^**	一阶微分差值植被指数Difference vegetation index Dr762-Dr496	0.721^**
R1104	0.680^**	红边比值植被指数Red edge ratio vegetation index Dr747/Dr687	0.431^**
Dr1138	-0.715^**	红边差值植被指数Red edge difference vegetation index Dr747-Dr687	0.589^**
Dr762	0.703^**	红边归一化差异指数Red edge normalized difference index (Dr747-Dr687)/( Dr747+Dr687)	0.545^**

植被指数 Vegetation index	计算公式 Calculation formula	相关关系Correlation	文献来源 Source of literature
RSI	R990/R720	0.528^**	[23]
NDSI	(R860-R720)/(R860+R720)	0.582^**	[23]
NDVI	(R790-R670)/(R790+R670)	0.332^**	[24]
FD-NDVI	(R730-R525)/(R730+R525)	0.451^**	[25]
RENDVI	(R750-R705/( R750+ R705)	0.367^**	[26]
mND705	(R750-R705)/(R750+2R445)	0.360^**	[27]
GNDVI	(R790-R550)/(R790+R550)	0.464^**	[28]
SAVI	1.5(R870-R680)/(R870+R680+0.5)	0.692^**	[29]
OSAVI	(1+0.16)(R810-R680)/(R810-R680+0.16)	0.633^**	[30]
MSAVI	0.5{2R800+1-[(2R800+1)2-8(R800-R670)]0.5}	0.623^**	[31]
DCNI	(R720-R700/(R700-R670)/(R720-R670+0.03)	0.460^**	[32]
CIgreen	[(R840-R870)/R550]-1	-0.493^**	[33]
NINI	[lg(1/R1510)-lg(1/R1680)]/[lg(1/R1510)+lg(1/R1680)]	0.553^**	[34]
TVI	0.5[120(R750-R550)-200(R670-R550)]	0.538^**	[35]
DSI	R800-R680	0.510^**	[36]

高光谱特征参数 Characteristic parameter	拟合模型 Fitting model		验证模型 Performance model
高光谱特征参数 Characteristic parameter	拟合方程 Equation	R²	RE(%)	RMSE
R1104	y = 63.88x-8.167	0.516	21.429	4.762
	y = -30.27x²+96.39-16.64	0.519	22.299	4.829
	y = 58.24x^1.296	0.513	20.898	4.766
	y = 7.555e^2.277^x	0.493	22.904	4.898
Dr1138	y = 64.73x-1.144	0.643	19.893	4.874
	y = -47.08x²+104.7x-9.162	0.648	20.625	4.945
	y = 63.68x^1.031	0.643	19.909	4.881
	y = 9.629e^2.313^x	0.615	23.265	5.010
Dr762	y = 157.8x-147	0.770	20.877	5.038
	y = -192.3x²+578.8x-377.1	0.772	21.518	5.137
	y = 13.96x^6.461	0.747	21.635	4.914
	y = 0.04302e^5.813^x	0.740	22.103	4.928

高光谱特征参数 Characteristic parameter	拟合模型 Fitting model		验证模型 Performance model
高光谱特征参数 Characteristic parameter	拟合方程 Equation	R²	RE(%)	RMSE
SAVI	y = 114.7x-105.7	0.541	24.492	5.092
	y = 104.5x²-122.1x+28.18	0.544	23.153	4.926
	y = 12.39x^5.208	0.539	21.878	4.830
	y = 0.1489e^4.474^x	0.535	21.514	4.829
Dr1138-Dr762	y = 49.36x+0.4913	0.732	19.708	4.709
	y = -1.864x²+51.27x+0.04145	0.732	19.777	4.721
	y = 49.73x^0.9814	0.732	19.746	4.715
	y = 9.737e^1.842^x	0.714	20.934	4.656
Dr762-Dr496	y = 12.85x+5.673	0.794	23.008	5.372
	y = -2.828x²+21.68x-0.3212	0.804	22.996	5.608
	y = 18.64x^0.755	0.799	23.025	5.447
	y = 11.91e^0.4736^x	0.759	24.623	5.288

Estimation of total nitrogen content in sugarbeet leaves under drip irrigation based on hyperspectral characteristic parameters and vegetation index

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