高CO2浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演

doi:10.3969/j.issn.1004-1524.2022.03.19

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (3): 582-589.DOI: 10.3969/j.issn.1004-1524.2022.03.19

高CO₂浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演

蔡瑶(), 缪宇轩, 吴浩, 王丹()

南京信息工程大学应用气象学院,江苏南京 210044

收稿日期:2021-02-19 出版日期:2022-03-25 发布日期:2022-03-30
通讯作者: 王丹
作者简介:*王丹,E-mail: wangdan.nuist@outlook.com
蔡瑶(1996—),男,江苏盐城人,硕士研究生,主要从事农作物光谱模型研究。E-mail: caiyaomiss@163.com
基金资助:
国家自然科学基金(31500503);国家自然科学基金(31770485);江苏省自然科学基金(BK20150894)

Hyperspectral characteristics and leaf area index (LAI) and SPAD value inversion of winter wheat under elevated CO₂ concentration

CAI Yao(), MIAO Yuxuan, WU Hao, WANG Dan()

School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received:2021-02-19 Online:2022-03-25 Published:2022-03-30
Contact: WANG Dan

摘要/Abstract

摘要：

以冬小麦为研究对象,利用开顶式气室试验,开展以环境CO₂ 浓度为对照(CK)和比CK处理的CO₂浓度高200 μmol·mol^-1(T)处理的试验,测定冬小麦主要生育期冠层光谱反射率、叶面积指数(LAI)和SPAD值,分析LAI、SPAD值与原始光谱反射率、光谱特征参数的相关性,并探究最优回归反演模型。结果表明,高CO₂浓度处理提高冬小麦孕穗-抽穗期和灌浆期的LAI和SPAD值,影响光谱反射率大小,但不改变光谱反射率曲线波形。CO₂浓度升高导致红边位置先红移再蓝移。用红黄边面积比(x)估算小麦LAI(y)的回归模型最优,回归方程为y=3.96×10^-3x²-8.60×10^-2x+1.93,在验证集上的决定系数和均方根误差分别为0.66和0.42。用红边振幅(x)估算小麦SPAD值(y)的回归模型最优,回归方程为y=-5.151×10⁴x²+2.883×10³x+33.83,在验证集上的决定系数和均方根误差分别为0.63和4.61。

关键词: 高二氧化碳, 高光谱, 叶绿素含量, 叶面积指数, 冬小麦

Abstract:

In the present study, winter wheat was selected as the research object, and an open-top chamber experiment was carried out with ambient CO₂ as control (CK) and plus 200 μmol·mol^-1CO₂ on the basis of CK as treatment (T). The canopy spectral reflectance, leaf area index(LAI) and SPAD value of winter wheat at main growth stages were measured. The correlation within LAI, SPAD value, original spectral reflectance and spectral characteristic parameters were analyzed, and the optimal estimation models were established. The results showed that the elevated CO₂ concentration increased LAI and SPAD value of winter wheat at booting-heading stage and filling stage.The elevated CO₂ concentration did not change the spectral reflectance curve, but changed the reflectance. The elevated CO₂ concentration induced the red edge position red shift first and then blue shift.Red and yellow edge area ratio (x) was the most suitable spectral parameter to estimate LAI(y) of wheat, and the regression equation was y=3.96×10^-3x²-8.60×10^-2x+1.93, of which the coefficient of determination (R²) and the root mean square error (RMSE) on validation set were 0.66 and 0.42, respectively. Red edge amplitude (x) was the most suitable spectral parameter to estimate SPAD value (y) of wheat, and the regression equation was y=-5.151×10⁴x²+2.883×10³x+33.83, of which the R² and RMSE on validation set were 0.63 and 4.61, respectively.

Key words: elevated CO₂, hyperspectral, chlorophyll content, leaf area index, winter wheat

中图分类号:

TP79
S512.11

蔡瑶, 缪宇轩, 吴浩, 王丹. 高CO₂浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演[J]. 浙江农业学报, 2022, 34(3): 582-589.

CAI Yao, MIAO Yuxuan, WU Hao, WANG Dan. Hyperspectral characteristics and leaf area index (LAI) and SPAD value inversion of winter wheat under elevated CO₂ concentration[J]. Acta Agriculturae Zhejiangensis, 2022, 34(3): 582-589.

图/表 7

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处理 Treatment	LAI				SPAD
处理 Treatment	拔节期 Jointing stage	孕穗-抽穗期 Booting-heading stage	灌浆期 Filling stage	乳熟期 Milk-ripening stage	拔节期 Jointing stage	孕穗-抽穗期 Booting-heading stage	灌浆期 Filling stage	乳熟期 Milk-ripening stage
CK	2.09±0.11 b	2.89±0.46 a	3.23±0.55 b	2.15±0.14 a	48.13±2.72 a	55.61±2.45 a	57.31±2.77 b	49.08±3.45 a
T	2.30±0.21 a	3.05±0.29 a	3.73±0.34 a	1.99±0.31 a	47.65±3.71 a	58.29±4.34 a	61.06±3.53 a	42.73±2.70 b

处理 Treatment	LAI				SPAD
处理 Treatment	拔节期 Jointing stage	孕穗-抽穗期 Booting-heading stage	灌浆期 Filling stage	乳熟期 Milk-ripening stage	拔节期 Jointing stage	孕穗-抽穗期 Booting-heading stage	灌浆期 Filling stage	乳熟期 Milk-ripening stage
CK	2.09±0.11 b	2.89±0.46 a	3.23±0.55 b	2.15±0.14 a	48.13±2.72 a	55.61±2.45 a	57.31±2.77 b	49.08±3.45 a
T	2.30±0.21 a	3.05±0.29 a	3.73±0.34 a	1.99±0.31 a	47.65±3.71 a	58.29±4.34 a	61.06±3.53 a	42.73±2.70 b

生育期 Growing stage	λr/nm		Dr/10^-2		SDr/10^-2
生育期 Growing stage	CK	T	CK	T	CK	T
拔节期Jointing stage	732	732	0.74	0.69	31.06	28.87
孕穗-抽穗期Booting-heading stage	736	736	0.97	0.95	38.45	37.21
灌浆期Filling stage	735	736	0.94	0.83	38.36	33.47
乳熟期Milk-ripening stage	718	709	0.50	0.41	25.31	20.81

生育期 Growing stage	λr/nm		Dr/10^-2		SDr/10^-2
生育期 Growing stage	CK	T	CK	T	CK	T
拔节期Jointing stage	732	732	0.74	0.69	31.06	28.87
孕穗-抽穗期Booting-heading stage	736	736	0.97	0.95	38.45	37.21
灌浆期Filling stage	735	736	0.94	0.83	38.36	33.47
乳熟期Milk-ripening stage	718	709	0.50	0.41	25.31	20.81

x	r	回归方程 Regression equation	建模集Modeling set		验证集Validation set
x	r	回归方程 Regression equation	R²	RMSE	R²	RMSE
R₇₇₇	0.63^**	y=4.964+0.458 5ln x^4.964	0.43^**	0.51	0.32^**	0.59
Dr	0.70^**	y=910.1x²+192.3x+1.16	0.52^**	0.46	0.40^**	0.56
SDr	0.65^**	y=5.748x+0.850 4	0.46^**	0.50	0.35^**	0.58
SDr/SDy	0.81^**	y=3.96×10^-3x²-8.60×10^-2x+1.93	0.78^**	0.32	0.66^**	0.42
SDr/SDb	0.68^**	y=7.90×10^-4x²+5.20×10^-2x+1.52	0.52^**	0.47	0.37^**	0.57
SDr-SDy	0.64^**	y=1.150x²+4.600x+1.042	0.45^**	0.50	0.35^**	0.58
SDr-SDb	0.62^**	y=1.610x²+4.800x+1.092	0.48^**	0.49	0.37^**	0.57
(SDr-SDb)/(SDr+SDb)	-0.60^**	y=18.90x²-51.08x+36.30	0.47^**	0.49	0.34^**	0.59

高CO₂浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演

Hyperspectral characteristics and leaf area index (LAI) and SPAD value inversion of winter wheat under elevated CO₂ concentration

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x	r	回归方程 Regression equation	建模集Modeling set		验证集Validation set
x	r	回归方程 Regression equation	R²	RMSE	R²	RMSE
R₇₇₀	0.71^**	y=-191.7x²+213.0x+0.813 0	0.56^**	4.37	0.47^**	5.56
λr	0.62^**	y=2.030×10^-2x²-28.76x+1.024×10⁴	0.52^**	4.61	0.47^**	5.55
Dr	0.78^**	y=-5.151×10⁴x²+2.883×10³x+33.83	0.60^**	4.21	0.63^**	4.61
SDr	0.76^**	y=62.16x+32.72	0.56^**	4.40	0.59^**	4.86
SDr/SDy	0.70^**	y=2.07×10^-2x²-1.17×10^-1x+39.28	0.50^**	4.67	0.58^**	4.95
SDr/SDb	0.67^**	y=-9.720×10^-3x²+1.003x+38.48	0.45^**	4.93	0.42^**	5.80
SDr-SDy	0.76^**	y=57.81x+33.61	0.56^**	4.38	0.60^**	4.80
SDr-SDb	0.76^**	y=60.93x+34.34	0.57^**	4.31	0.61^**	4.77
(SDr-SDb)/(SDr+SDb)	-0.60^**	y=170.6x²-462.8x+357.9	0.43^**	5.00	0.45^**	5.63

x	r	回归方程 Regression equation	建模集Modeling set		验证集Validation set
x	r	回归方程 Regression equation	R²	RMSE	R²	RMSE
R₇₇₀	0.71^**	y=-191.7x²+213.0x+0.813 0	0.56^**	4.37	0.47^**	5.56
λr	0.62^**	y=2.030×10^-2x²-28.76x+1.024×10⁴	0.52^**	4.61	0.47^**	5.55
Dr	0.78^**	y=-5.151×10⁴x²+2.883×10³x+33.83	0.60^**	4.21	0.63^**	4.61
SDr	0.76^**	y=62.16x+32.72	0.56^**	4.40	0.59^**	4.86
SDr/SDy	0.70^**	y=2.07×10^-2x²-1.17×10^-1x+39.28	0.50^**	4.67	0.58^**	4.95
SDr/SDb	0.67^**	y=-9.720×10^-3x²+1.003x+38.48	0.45^**	4.93	0.42^**	5.80
SDr-SDy	0.76^**	y=57.81x+33.61	0.56^**	4.38	0.60^**	4.80
SDr-SDb	0.76^**	y=60.93x+34.34	0.57^**	4.31	0.61^**	4.77
(SDr-SDb)/(SDr+SDb)	-0.60^**	y=170.6x²-462.8x+357.9	0.43^**	5.00	0.45^**	5.63