利用高光谱参数反演水稻叶片类胡萝卜素含量

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

摘要/Abstract

摘要：

为了探讨快速、准确预测水稻(Oryza sativa)叶片类胡萝卜素(Car)含量的敏感光谱波段和光谱指数, 通过实施涉及不同年份、不同生态点、不同施氮水平和不同品种类型的4个田间试验, 于主要生育期同步测定了水稻顶部4张叶片的光谱反射率及Car含量, 系统分析了350-2 500 nm范围内任意两波段组合而成的比值(SR (λ1, λ2))、归一化(ND (λ1, λ2))及已报道的对Car敏感的光谱指数与水稻叶片Car含量间的定量关系。结果表明, 不同Car含量水平下水稻叶片光谱反射率存在着明显变化, 以绿光及红边波段对水稻叶片Car含量变化最为敏感。723 nm附近的波段与近红外波段的比值组合以及713 nm附近的波段与近红外波段的归一化组合可以较好地预测水稻叶片Car含量, 以SR (723, 770)和ND (770, 713)表现最好, 线性拟合R²分别达到0.897和0.898。基于独立的试验资料的检验表明, 预测值和实测值的拟合R²分别为0.856和0.858, 均方根差RMSE均为0.072, 平均相对误差RE分别为11.9%和12.0%, 表明SR (723,770)和ND (770, 713)可有效地估算水稻上部叶片的Car含量。

关键词: 类胡萝卜素含量, 高光谱, 水稻, 光谱指数

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

杨杰, 田永超, 姚霞, 曹卫星, 朱艳. 利用高光谱参数反演水稻叶片类胡萝卜素含量. 植物生态学报, 2010, 34(7): 845-854. DOI: 10.3773/j.issn.1005-264x.2010.07.010

YANG Jie, TIAN Yong-Chao, YAO Xia, CAO Wei-Xing, ZHU Yan. Estimating leaf carotenoid content with hyperspectral parameters in rice. Chinese Journal of Plant Ecology, 2010, 34(7): 845-854. DOI: 10.3773/j.issn.1005-264x.2010.07.010

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

图/表 9

表1 本文采用的高光谱指数列表

Table 1 Different hyperspectral indices used in this study

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)

表2 水稻叶片类胡萝卜素含量的变化

Table 2 Changes in carotenoid content of rice leaves (mg·g-1 FW)

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

图1 水稻顶部叶片类胡萝卜素含量随生育期的变化(试验2, ‘武香粳14’)。 N0、N1、N2、N3, 分别表示0、90、240和360 kg N·hm-2处理。L1、L2、L3、L4, 分别表示顶1、顶2、顶3、顶4叶。

Fig. 1 Changes in carotenoid content of top leaves with growth process in rice (EXP. 2, ‘Wuxiangjing14’). N0, N1, N2, N3, treatments of 0, 90, 240 and 360 kg N·hm-2, respectively. L1, L2, L3, L4, the 1st, 2nd, 3rd, 4th leaf from top, respectively.

图2 不同类胡萝卜素含量(mg·g-1)水平水稻叶片的光谱反射率特征。

Fig. 2 Reflectance characteristics of rice leaves at different carotenoid content levels.

图3 水稻叶片类胡萝卜素含量与叶片光谱反射率及一阶导数光谱数据的相关性(n = 400)。

Fig. 3 The correlation patterns of leaf carotenoid content to leaf spectral reflectance and its first derivative spectra (n = 400).

图4 两波段组合的比值(A)及归一化(B)光谱指数估算类胡萝卜素含量的决定系数(R2)等势图。

Fig. 4 A contour map of coefficients of determination (R2) between total carotenoid content and simple ratio spectral indices (A), normalized difference spectral indices (B) based on two separate wavelengths on x and y axes.

表3 水稻叶片类胡萝卜素含量(y)与不同光谱指数(x)的定量关系(n = 400)及检验效果(n = 223)

Table 3 Quantitative relationships of leaf carotenoid content in rice (y) to different spectral indices (x) (n = 400) and their predicting performance (n = 223)

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

图5 水稻叶片类胡萝卜素含量与比值指数(SR) (723, 770)及归一化指数(ND) (770, 713)的关系(n = 400)。 L1、L2、L3、L4同图1。

Fig. 5 Relationships of leaf corotenoid content in rice to the simple ratio spectral index SR (723, 770) and normalized difference spectral index ND (770, 713) in rice (n = 400). ①, elongation; ②, bloting; ③, heading; ④, early filling; ⑤, middle filling; ⑥, late filling. L1, L2, L3, L4 see Fig. 1.

图6 基于比值指数SR (723, 770)和归一化指数ND (770, 713)的水稻叶片类胡萝卜素含量实测值与预测值间的比较(n = 223)。 RE、RMSE, 同表3。

Fig. 6 Comparison between the predicted and observed leaf carotenoid content in rice based on simple ratio spectral index SR (723, 770) and normalized difference spectral index ND (770, 713) (n = 223). RE、RMSE, see Table 3.

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