Estimating leaf carotenoid content with hyperspectral parameters in rice

doi:10.3773/j.issn.1005-264x.2010.07.010

Abstract

Abstract:

Aims Our objectives were to detect the relationship between leaf carotenoid (Car) content and spectral reflectance characteristics and to recommend useful hyperspectral wavebands and hyperspectral indices for nondestructive and quick estimation of Car content in rice (Oryza sativa).

Methods Four field experiments with different nitrogen application rates and rice cultivars were conducted at different eco-sites over three years. Time-course measurements were taken on hyperspectral reflectance of 350- 2 500 nm and Car content in four top leaves. We calculated the simple ratio spectral index (SR (λ1, λ2)) and normalized difference spectral index (ND (λ1, λ2)) with all combinations of two wavelengths (λ1 and λ2 nm) as well as other indices sensitive to Car, and analyzed the relationships between Car content to single wavelength reflectance and these spectral indices.

Important findings Spectral reflectance varied with Car content, and the sensitive wavebands mostly occurred at green and red edge regions. The SR indices using reflectance around 723 nm combined with near infrared reflectance (NIR) or the ND indices using reflectance around 713 nm combined with NIR could be used to estimate leaf Car content in rice, among which the SR (723, 770) and ND (770, 713) have the best performance, with determination of coefficients (R²) of 0.897 and 0.898, respectively. Tests with an independent dataset showed that R² values between observed and predicted Car content with SR (723, 770) and ND (770, 713) were 0.856 and 0.858, with root meansquare error (RMSE) as 0.072, and relative error (RE) as 11.9% and 12.0%, respectively, which indicated that Car content in top leaves of rice could be predicted effectively with these two indices.

Key words: carotenoid content, hyperspectral, Oryza sativa, spectral index

YANG Jie, TIAN Yong-Chao, YAO Xia, CAO Wei-Xing, ZHU Yan. Estimating leaf carotenoid content with hyperspectral parameters in rice[J]. Chin J Plant Ecol, 2010, 34(7): 845-854.

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URL: https://www.plant-ecology.com/EN/10.3773/j.issn.1005-264x.2010.07.010

https://www.plant-ecology.com/EN/Y2010/V34/I7/845

Figures/Tables 9

References 23

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光谱指数 Hyperspectral index	计算公式或定义 Algorithm formula or definition	参考文献 Reference
比值光谱指数 Simple ratio spectral indices
SR ( λ1, λ2)	R_λ1/ R_λ2	This work
PSSRc	R₈₀₀/ R₄₇₀	Blackburn (1998)
R₇₆₀/R₅₅₀	R₇₆₀/ R₅₅₀	Chappelle et al. (1992)
VI2	R₁₂₀₀/ R₅₅₃	Tang et al. (2003)
归一化差值光谱指数 Normalized difference spectral indices
ND (λ1, λ2)	(R_λ1- R_λ2) / (R_λ1+ R_λ2)	This work
PSNDc	(R₈₀₀- R₄₇₀) / (R₈₀₀+ R₄₇₀)	Blackburn (1998)
其他类型光谱指数 Other spectral indices
CRI₅₅₀	1/R₅₁₀ - 1/R₅₅₀	Gitelson et al. (2002)
CRI₇₀₀	1/R₅₁₀ - 1/R₇₀₀	Gitelson et al. (2002)
R₆₇₂/(R₅₅₀× R₇₀₈)	R₆₇₂/ (R₅₅₀× R₇₀₈)	Datt (1998)

光谱指数 Hyperspectral index	计算公式或定义 Algorithm formula or definition	参考文献 Reference
比值光谱指数 Simple ratio spectral indices
SR ( λ1, λ2)	R_λ1/ R_λ2	This work
PSSRc	R₈₀₀/ R₄₇₀	Blackburn (1998)
R₇₆₀/R₅₅₀	R₇₆₀/ R₅₅₀	Chappelle et al. (1992)
VI2	R₁₂₀₀/ R₅₅₃	Tang et al. (2003)
归一化差值光谱指数 Normalized difference spectral indices
ND (λ1, λ2)	(R_λ1- R_λ2) / (R_λ1+ R_λ2)	This work
PSNDc	(R₈₀₀- R₄₇₀) / (R₈₀₀+ R₄₇₀)	Blackburn (1998)
其他类型光谱指数 Other spectral indices
CRI₅₅₀	1/R₅₁₀ - 1/R₅₅₀	Gitelson et al. (2002)
CRI₇₀₀	1/R₅₁₀ - 1/R₇₀₀	Gitelson et al. (2002)
R₆₇₂/(R₅₅₀× R₇₀₈)	R₆₇₂/ (R₅₅₀× R₇₀₈)	Datt (1998)

样本集 Sample	试验 Experiment	样本数 No. of sample	最小值 Min. value	最大值 Max. value	平均值 Mean value	标准偏差 SD	变异系数 CV (%)
建模样本 Modeling	EXP. 1, EXP. 2	400	0.136	0.854	0.544	0.157	28.8
测试样本 Testing	EXP. 3, EXP. 4	223	0.217	0.866	0.576	0.130	22.7

样本集 Sample	试验 Experiment	样本数 No. of sample	最小值 Min. value	最大值 Max. value	平均值 Mean value	标准偏差 SD	变异系数 CV (%)
建模样本 Modeling	EXP. 1, EXP. 2	400	0.136	0.854	0.544	0.157	28.8
测试样本 Testing	EXP. 3, EXP. 4	223	0.217	0.866	0.576	0.130	22.7

光谱指数 (x) Spectral index	回归方程 Regression equation	拟合精度 S-R²	预测精度 P-R²	均方根差 RMSE	平均相对误差 RE (%)
SR (723, 770)	y = -2.0382x + 2.1014	0.897	0.856	0.072	11.9
ND (770, 713)	y = 1.9306x + 0.0514	0.898	0.858	0.072	12.0
SR (554, 773)	y = -1.6552x + 1.0831	0.854	0.782	0.074	12.8
ND (773, 562)	y = 1.494x - 0.2512	0.867	0.794	0.075	12.7
PSSRc	y = 0.0734x - 0.2458	0.718	0.516	0.157	25.2
PSNDc	y = 4.0897x - 2.8301	0.712	0.549	0.141	23.1
CRI₅₅₀	y = 0.0697x - 0.0242	0.741	0.549	0.137	22.1
CRI₇₀₀	y = 0.0716x - 0.0632	0.680	0.418	0.140	23.0
R₇₆₀/R₅₀₀	y = 0.0742x - 0.1306	0.798	0.617	0.131	21.2
VI2	y = 0.2033x - 0.0782	0.815	0.781	0.089	14.5
R₆₇₂/(R₅₅₀×R₇₀₈)	y = 0.3762x - 0.0132	0.584	0.632	0.079	18.5