基于叶片反射光谱和叶绿素荧光估测水稻叶片含水量

doi:10.3969/j.issn.1004-1524.2023.06.04

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (6): 1265-1277.DOI: 10.3969/j.issn.1004-1524.2023.06.04

基于叶片反射光谱和叶绿素荧光估测水稻叶片含水量

张雪楠¹(), 王乐乐¹, 钮铭轩¹, 詹妮¹, 任浩杰², 徐浩聪¹, 杨昆¹, 武立权¹^,³, 柯健¹, 尤翠翠¹, 何海兵¹^,^*()

1.安徽农业大学农学院,安徽合肥 230036
2.普济圩现代农业集团有限公司,安徽铜陵 244071
3.江苏省现代作物生产协同创新中心,江苏南京 210095

收稿日期:2022-07-15 出版日期:2023-06-25 发布日期:2023-07-04
通讯作者: *何海兵,E-mail:hhb_agr@ahau.edu.cn
作者简介:张雪楠(1998—),女,河南周口人,硕士研究生,主要从事农作物光谱模型研究。E-mail:2869231417@qq.com
基金资助:
国家自然科学基金(32071946);安徽省自然科学基金(1908085MC67);安徽省教育厅自然科学基金重点项目(KJ2021A0201)

Estimation of rice leaf water content based on leaf reflectance spectrum and chlorophyll fluorescence

ZHANG Xuenan¹(), WANG Lele¹, NIU Mingxuan¹, ZHAN Ni¹, REN Haojie², XU Haocong¹, YANG Kun¹, WU Liquan¹^,³, KE Jian¹, YOU Cuicui¹, HE Haibing¹^,^*()

1. College of Agronomy, Anhui Agricultural University, Hefei 230036, China
2. Pujiwei Modern Agriculture Group Co., Ltd., Tongling 244071, Anhui, China
3. Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, China

Received:2022-07-15 Online:2023-06-25 Published:2023-07-04

摘要/Abstract

摘要：

水稻冠层叶片含水量(leaf water content,LWC)快速无损监测对指导稻田精准灌溉和提高水稻水分利用效率具有重要意义。试验设置3个不同水分处理(传统淹灌、轻度干湿交替-15 kPa、重度干湿交替-30 kPa),于水分敏感期(抽穗-灌浆期)动态监测顶1叶(L₁)、顶2叶(L₂)和顶3叶(L₃)的光谱数据和叶绿素荧光参数,通过全光谱波段筛选出水分敏感波段,建立新型植被指数,结合叶绿素荧光参数,以期建立基于叶位组合的水稻冠层LWC精准监测模型。结果表明:水稻叶片水分敏感波段在近红外波段(1 000~1 400 nm),所构建新型植被指数NDSII_(1114,1387)较传统植被指数能更好地监测LWC;通过筛选与LWC有高相关性的荧光参数,基于实际光量子产量Y(Ⅱ)和植被指数NDSII_(1114,1387)的耦合监测模型较单一植被指数NDSII_(1114,1387)模型精度提高71.807%~83.976%。与单叶相比,L₂和L₃叶位组合的Y(Ⅱ)和植被指数NDSII_(1114,1387)耦合模型对水稻冠层LWC监测精度相较L₂、L₃分别显著(P<0.05)提高11.641%和23.029%。由此表明,基于叶位组合的叶片反射光谱与叶绿素荧光耦合可有效监测水稻冠层LWC,为光学仪器监测水稻LWC提供理论基础,并对未来利用反射光谱与荧光参数进行作物光合作用研究提供理论支持。

关键词: 叶片含水量, 水稻, 植被指数, 叶绿素荧光参数, 模型

Abstract:

Rapid and non-destructive monitoring to leaf water content (LWC) in rice is of great significance for guiding precision irrigation of paddy fields and improving water use efficiency of rice plants. In this study, three different water treatments including traditional flooded irrigation, mild dry-wet alternation with-15 kPa supplementary irrigation critical value, severe dry-wet alternation with-30 kPa supplementary irrigation critical value were set in pot experiment to precisely regulate plant growth. The LWC of the canopy, single leaf spectral data and chlorophyll fluorescence parameters of the top 1 (L₁), top 2 (L₂), and top 3 (L₃) leaves were continuously measured in the water-sensitive periods from heading to grain filling period. Water-sensitive bands were screened out through the full spectral bands to establish a new vegetation index. An accurate monitoring model for rice LWC based on leaf position combination was established in combination with the chlorophyll fluorescence parameters. It was found that: The sensitive water band is in the near-infrared band (1 000-1 400 nm), and a new vegetation index NDSII_(1114,1387) was constructed. By screening the fluorescence parameters with a high correlation with LWC, the coupled monitoring model based on the actual photochemical efficiency Y(Ⅱ) and the vegetation index NDSII_(1114,1387) was 71.807%-83.976% better accuracy than the single vegetation index NDSII_(1114,1387) model. Compared with the single leaf, the Y(Ⅱ) and vegetation index NDSII_(1114,1387) coupling model of L₂and L₃leaf position combination significantly improved the accuracy of LWC monitoring of rice canopy by 11.641% and 23.029% compared with L₂and L₃. This showed that the coupling of leaf reflectance spectrum and chlorophyll fluorescence could effectively monitor the LWC in the water-sensitive period of rice, providing a theoretical basis for optical instrument monitoring of rice LWC and theoretical support for future research on crop photosynthesis using the reflectance spectrum and fluorescence parameters.

Key words: leaf water content, Oryza sativa L., vegetation index, chlorophyll fluorescence parameter, model

中图分类号:

TP79
S511

张雪楠, 王乐乐, 钮铭轩, 詹妮, 任浩杰, 徐浩聪, 杨昆, 武立权, 柯健, 尤翠翠, 何海兵. 基于叶片反射光谱和叶绿素荧光估测水稻叶片含水量[J]. 浙江农业学报, 2023, 35(6): 1265-1277.

ZHANG Xuenan, WANG Lele, NIU Mingxuan, ZHAN Ni, REN Haojie, XU Haocong, YANG Kun, WU Liquan, KE Jian, YOU Cuicui, HE Haibing. Estimation of rice leaf water content based on leaf reflectance spectrum and chlorophyll fluorescence[J]. Acta Agriculturae Zhejiangensis, 2023, 35(6): 1265-1277.

图/表 13

表1 本研究采用的植被指数列表

Table 1 List of vegetation indexes used in this study

植被指数 Vegetation index	计算公式 Algorithm	参考文献 Reference
归一化差值短红外指数Normalized difference short infrared index (NDSII)	V_NDSII=(R_λ1-R_λ2)/(R_λ1+R_λ2)	本研究This study
归一化差值植被指数Normalized difference vegetation index (NDVI)	V_NDVI=(R₈₉₅-R₆₇₅)/(R₈₉₅+R₆₇₅)	[28]
归一化差值近红外指数Normalized difference infrared index (NDII)	V_NDII=(R₈₁₉-R₁₆₀₀)/(R₈₁₉+R₁₆₀₀)	[29]
归一化差值水分指数Normalized difference water index (NDWI)	V_NDWI=(R₈₆₀-R₁₂₄₀)/(R₈₆₀+R₁₂₄₀)	[30]
水分指数Water index (WI)	V_WI=R₉₇₀/R₉₀₀	[9]
简单比值水分指数Simple ratio water index (SRWI)	V_SRWI=R₈₆₀/R₁₂₄₀	[31]
水分胁迫指数Moisture stress index (MSI)	V_MSI=R₁₆₀₀/R₈₂₀	[32]
全球植被水分指数Global vegetation water index (GVWI)	V_GVWI=[(R₈₂₀+0.1)-(R₁₆₀₀+0.2)]/	[33]
	[(R₈₂₀+0.1)+(R₁₆₀₀+0.2)]

图1 水稻LWC的变化特征柱上无相同小写字母表示差异显著(P<0.05)。下同。

Fig.1 Characteristics of LWC variation in rice Data on the bars marked without the same lowercase letter indicate significant differences at P<0.05. The same as below.

表2 LWC在品种、观测时期和水分处理下的方差分析

Table 2 Analysis of variance of LWC among varieties, water treatments, and observed periods, respectively

分组因子 Grouping factor	变异来源 Variation source	平方和 Sum of squares	自由度 Degree of freedom	均方 Mean square	F值 F-value	P值 P-value
水分处理	组间Intergroup	0.011	2	0.005	18.154	<0.01
Water treatment	组内Intragroup	0.023	78	0
	总计Total	0.034	80
品种Variety	组间Intergroup	0.001	2	0	0.958	0.388
	组内Intragroup	0.033	78	0
	总计Total	0.034	80
观测时期	组间Intergroup	0.017	2	0.009	39.646	0
Observed periods	组内Intragroup	0.017	78	0
	总计Total	0.034	80

图2 荧光参数的变化特征

Fig.2 Variation characteristics of fluorescence parameter

图3 不同观测时期水稻叶片光谱变化特征

Fig.3 Spectral characteristics of rice leaves in different observed stages

图4 不同水分处理和不同叶位水稻叶片光谱变化特征

Fig.4 Spectral characteristics of rice leaves under different water treatments and different leaf positions

表3 不同时期荧光参数与水稻LWC的相关系数

Table 3 Correlation coefficient between different fluorescence parameters and rice LWC at different growth stages

时期 Stage	叶位 Leaf position	F_v/F_m	F_o	Y(Ⅱ)
抽穗期	L₁	0.572^**	-0.573^**	0.705^**
Heading stage	L₂	0.709^**	-0.591^**	0.778^**
	L₃	0.662^**	-0.585^**	0.740^**
	L₁₂	0.717^**	-0.594^**	0.797^**
	L₁₃	0.677^**	-0.634^**	0.785^**
	L₂₃	0.732^**	-0.656^**	0.800^**
开花期	L₁	0.676^**	-0.535^**	0.642^**
Flowering stage	L₂	0.694^**	-0.613^**	0.758^**
	L₃	0.679^**	-0.570^**	0.756^**
	L₁₂	0.717^**	-0.603^**	0.747^**
	L₁₃	0.714^**	-0.589^**	0.735^**
	L₂₃	0.721^**	-0.623^**	0.767^**
灌浆期	L₁	0.604^**	-0.528^**	0.718^**
Filling stage	L₂	0.674^**	-0.589^**	0.767^**
	L₃	0.645^**	-0.548^**	0.748^**
	L₁₂	0.681^**	-0.625^**	0.792^**
	L₁₃	0.664^**	-0.586^**	0.776^**
	L₂₃	0.698^**	-0.669^**	0.807^**
抽穗-灌浆期	L₁	0.560^**	-0.426^**	0.580^**
Heading-	L₂	0.613^**	-0.577^**	0.644^**
filling stage	L₃	0.592^**	-0.521^**	0.607^**
	L₁₂	0.626^**	-0.528^**	0.654^**
	L₁₃	0.632^**	-0.517^**	0.635^**
	L₂₃	0.654^**	-0.618^**	0.663^**

表4 水稻LWC与荧光参数Y(Ⅱ)的定量关系与模型检验效果

Table 4 Quantitative relationship and model verification effect of LWC and fluorescence parameter Y(Ⅱ) in rice

叶位 Leaf position	回归方程 Regression equation	模型精度 Model Precision(R²)	预测精度 Prediction Precision( $R p 2$ )	均方根误 RMSE	相对误差 RE/%
L₁	y =0.925x+0.670	0.337	0.363	0.018	2.318
L₂	y=1.032x+0.674	0.415	0.429	0.016	2.197
L₃	y=0.982x+0.682	0.369	0.416	0.016	2.295
L₁₂	y=1.114x+0.666	0.427	0.457	0.016	2.163
L₁₃	y=1.091x+0.670	0.404	0.425	0.016	2.215
L₂₃	y=1.130x+0.673	0.439	0.468	0.015	2.156

表4 水稻LWC与荧光参数Y(Ⅱ)的定量关系与模型检验效果

Table 4 Quantitative relationship and model verification effect of LWC and fluorescence parameter Y(Ⅱ) in rice

叶位 Leaf position	回归方程 Regression equation	模型精度 Model Precision(R²)	预测精度 Prediction Precision( $R p 2$ )	均方根误 RMSE	相对误差 RE/%
L₁	y =0.925x+0.670	0.337	0.363	0.018	2.318
L₂	y=1.032x+0.674	0.415	0.429	0.016	2.197
L₃	y=0.982x+0.682	0.369	0.416	0.016	2.295
L₁₂	y=1.114x+0.666	0.427	0.457	0.016	2.163
L₁₃	y=1.091x+0.670	0.404	0.425	0.016	2.215
L₂₃	y=1.130x+0.673	0.439	0.468	0.015	2.156

表5 不同时期典型植被指数与水稻LWC的相关系数

Table 5 Correlation coefficient between typical vegetation indexes and rice LWC at different growth stages

时期	叶位	NDVI	NDII	NDWI	WI	SRWI	MSI	GVWI
Stage	Leaf position
抽穗期	L₁	-0.663^**	-0.604^**	0.674^**	0.588^**	0.591^**	0.623^**	-0.603^**
Heading stage	L₂	-0.759^**	-0.678^**	0.743^**	0.615^**	0.680^**	0.675^**	-0.626^**
	L₃	-0.678^**	-0.644^**	0.735^**	0.637^**	0.625^**	0.660^**	-0.684^**
	L₁₂	-0.783^**	-0.683^**	0.832^**	0.673^**	0.683^**	0.667^**	-0.686^**
	L₁₃	-0.764^**	-0.661^**	0.748^**	0.699^**	0.681^**	0.683^**	-0.677^**
	L₂₃	-0.782^**	-0.705^**	0.842^**	0.717^**	0.702^**	0.699^**	-0.704^**
开花期	L₁	-0.631^**	-0.599^**	0.583^**	0.604^**	0.582^**	0.588^**	-0.608^**
Flowering stage	L₂	-0.750^**	-0.612^**	0.706^**	0.594^**	0.624^**	0.623^**	-0.630^**
	L₃	-0.656^**	-0.601^**	0.625^**	0.557^**	0.653^**	0.632^**	-0.620^**
	L₁₂	-0.716^**	-0.629^**	0.721^**	0.628^**	0.622^**	0.632^**	-0.664^**
	L₁₃	-0.709^**	-0.649^**	0.684^**	0.607^**	0.615^**	0.670^**	-0.658^**
	L₂₃	-0.725^**	-0.657^**	0.737^**	0.621^**	0.667^**	0.696^**	-0.709^**
灌浆期	L₁	-0.615^**	-0.620^**	0.621^**	0.535^**	0.558^**	0.522^**	-0.601^**
Filling stage	L₂	-0.691^**	-0.661^**	0.699^**	0.613^**	0.622^**	0.623^**	-0.585^**
	L₃	-0.687^**	-0.638^**	0.630^**	0.606^**	0.610^**	0.616^**	-0.593^**
	L₁₂	-0.765^**	-0.673^**	0.687^**	0.689^**	0.627^**	0.614^**	-0.700^**
	L₁₃	-0.763^**	-0.729^**	0.684^**	0.641^**	0.630^**	0.598^**	-0.740^**
	L₂₃	-0.775^**	-0.746^**	0.702^**	0.722^**	0.673^**	0.694^**	-0.652^**
抽穗-灌浆期	L₁	-0.581^**	-0.507^**	0.565^**	0.548^**	0.543^**	0.524^**	-0.495^**
Heading-filling stage	L₂	-0.693^**	-0.576^**	0.664^**	0.570^**	0.613^**	0.545^**	-0.517^**
	L₃	-0.651^**	-0.555^**	0.600^**	0.567^**	0.587^**	0.541^**	-0.520^**
	L₁₂	-0.702^**	-0.546^**	0.644^**	0.565^**	0.624^**	0.538^**	-0.516^**
	L₁₃	-0.670^**	-0.554^**	0.583^**	0.571^**	0.607^**	0.538^**	-0.522^**
	L₂₃	-0.731^**	-0.583^**	0.656^**	0.576^**	0.643^**	0.545^**	-0.521^**

表5 不同时期典型植被指数与水稻LWC的相关系数

Table 5 Correlation coefficient between typical vegetation indexes and rice LWC at different growth stages

时期	叶位	NDVI	NDII	NDWI	WI	SRWI	MSI	GVWI
Stage	Leaf position
抽穗期	L₁	-0.663^**	-0.604^**	0.674^**	0.588^**	0.591^**	0.623^**	-0.603^**
Heading stage	L₂	-0.759^**	-0.678^**	0.743^**	0.615^**	0.680^**	0.675^**	-0.626^**
	L₃	-0.678^**	-0.644^**	0.735^**	0.637^**	0.625^**	0.660^**	-0.684^**
	L₁₂	-0.783^**	-0.683^**	0.832^**	0.673^**	0.683^**	0.667^**	-0.686^**
	L₁₃	-0.764^**	-0.661^**	0.748^**	0.699^**	0.681^**	0.683^**	-0.677^**
	L₂₃	-0.782^**	-0.705^**	0.842^**	0.717^**	0.702^**	0.699^**	-0.704^**
开花期	L₁	-0.631^**	-0.599^**	0.583^**	0.604^**	0.582^**	0.588^**	-0.608^**
Flowering stage	L₂	-0.750^**	-0.612^**	0.706^**	0.594^**	0.624^**	0.623^**	-0.630^**
	L₃	-0.656^**	-0.601^**	0.625^**	0.557^**	0.653^**	0.632^**	-0.620^**
	L₁₂	-0.716^**	-0.629^**	0.721^**	0.628^**	0.622^**	0.632^**	-0.664^**
	L₁₃	-0.709^**	-0.649^**	0.684^**	0.607^**	0.615^**	0.670^**	-0.658^**
	L₂₃	-0.725^**	-0.657^**	0.737^**	0.621^**	0.667^**	0.696^**	-0.709^**
灌浆期	L₁	-0.615^**	-0.620^**	0.621^**	0.535^**	0.558^**	0.522^**	-0.601^**
Filling stage	L₂	-0.691^**	-0.661^**	0.699^**	0.613^**	0.622^**	0.623^**	-0.585^**
	L₃	-0.687^**	-0.638^**	0.630^**	0.606^**	0.610^**	0.616^**	-0.593^**
	L₁₂	-0.765^**	-0.673^**	0.687^**	0.689^**	0.627^**	0.614^**	-0.700^**
	L₁₃	-0.763^**	-0.729^**	0.684^**	0.641^**	0.630^**	0.598^**	-0.740^**
	L₂₃	-0.775^**	-0.746^**	0.702^**	0.722^**	0.673^**	0.694^**	-0.652^**
抽穗-灌浆期	L₁	-0.581^**	-0.507^**	0.565^**	0.548^**	0.543^**	0.524^**	-0.495^**
Heading-filling stage	L₂	-0.693^**	-0.576^**	0.664^**	0.570^**	0.613^**	0.545^**	-0.517^**
	L₃	-0.651^**	-0.555^**	0.600^**	0.567^**	0.587^**	0.541^**	-0.520^**
	L₁₂	-0.702^**	-0.546^**	0.644^**	0.565^**	0.624^**	0.538^**	-0.516^**
	L₁₃	-0.670^**	-0.554^**	0.583^**	0.571^**	0.607^**	0.538^**	-0.522^**
	L₂₃	-0.731^**	-0.583^**	0.656^**	0.576^**	0.643^**	0.545^**	-0.521^**

图5 两波段组合的归一化差值植被指数预测水稻LWC的R2、RMSE和RE等势图

Fig.5 Contour maps of R2、RMSE and RE of LWC predicted by normalized difference vegetation index based on two wavebands-combination in rice

表6 水稻LWC与植被指数NDSII(1114,1387)的定量关系与模型检验效果

Table 6 Quantitative relationships and model verification effect of LWC and vegetation index NDSII(1114,1387) in rice

叶位 Leaf position	回归方程 Regression equation	模型精度 Model Precision(R²)	预测精度 Prediction Precision( $R p 2$ )	均方根误 RMSE	相对误差 RE(%)
L₁	y=-0.582x+0.930	0.482	0.505	0.015	2.065
L₂	y=-0.620x+0.941	0.597	0.619	0.013	1.849
L₃	y=-0.567x+0.915	0.577	0.618	0.013	1.873
L₁₂	y=-0.781x+0.965	0.609	0.621	0.013	1.806
L₁₃	y=-0.647x+0.948	0.598	0.627	0.013	1.825
L₂₃	y=-0.654x+0.950	0.648	0.679	0.012	1.718

表6 水稻LWC与植被指数NDSII(1114,1387)的定量关系与模型检验效果

Table 6 Quantitative relationships and model verification effect of LWC and vegetation index NDSII(1114,1387) in rice

叶位 Leaf position	回归方程 Regression equation	模型精度 Model Precision(R²)	预测精度 Prediction Precision( $R p 2$ )	均方根误 RMSE	相对误差 RE(%)
L₁	y=-0.582x+0.930	0.482	0.505	0.015	2.065
L₂	y=-0.620x+0.941	0.597	0.619	0.013	1.849
L₃	y=-0.567x+0.915	0.577	0.618	0.013	1.873
L₁₂	y=-0.781x+0.965	0.609	0.621	0.013	1.806
L₁₃	y=-0.647x+0.948	0.598	0.627	0.013	1.825
L₂₃	y=-0.654x+0.950	0.648	0.679	0.012	1.718

表7 模型综合评价

Table 7 Comprehensive evaluation of model

模型 Model	回归方程 Regression equation	模型精度 Model Precision (R²)	预测精度 Prediction Precision ( $R p 2$ )	均方根误 RMSE	相对误差 RE/%	校正效果 Correction effect/%
模型 Model	回归方程 Regression equation	模型精度 Model Precision (R²)	预测精度 Prediction Precision ( $R p 2$ )	均方根误 RMSE	相对误差 RE/%	Y(Ⅱ)	NDSII
L₁	y=0.545a-0.482b+0.864	0.620	0.635	0.009	1.249	83.976	28.631
L₂	y=0.367a-0.459b+0.871	0.713	0.725	0.008	1.090	71.807	19.430
L₃	y=0.237a-0.668b+0.949	0.647	0.661	0.009	1.207	75.339	12.132
L₁₂	y=0.495a-0.427b+0.852	0.768	0.777	0.007	0.981	79.859	26.108
L₁₃	y=0.579a-0.478b+0.865	0.714	0.726	0.008	1.079	76.733	19.398
L₂₃	y=0.452a-0.464b+0.866	0.796	0.804	0.007	0.917	81.321	22.840

表7 模型综合评价

Table 7 Comprehensive evaluation of model

模型 Model	回归方程 Regression equation	模型精度 Model Precision (R²)	预测精度 Prediction Precision ( $R p 2$ )	均方根误 RMSE	相对误差 RE/%	校正效果 Correction effect/%
模型 Model	回归方程 Regression equation	模型精度 Model Precision (R²)	预测精度 Prediction Precision ( $R p 2$ )	均方根误 RMSE	相对误差 RE/%	Y(Ⅱ)	NDSII
L₁	y=0.545a-0.482b+0.864	0.620	0.635	0.009	1.249	83.976	28.631
L₂	y=0.367a-0.459b+0.871	0.713	0.725	0.008	1.090	71.807	19.430
L₃	y=0.237a-0.668b+0.949	0.647	0.661	0.009	1.207	75.339	12.132
L₁₂	y=0.495a-0.427b+0.852	0.768	0.777	0.007	0.981	79.859	26.108
L₁₃	y=0.579a-0.478b+0.865	0.714	0.726	0.008	1.079	76.733	19.398
L₂₃	y=0.452a-0.464b+0.866	0.796	0.804	0.007	0.917	81.321	22.840

图6 水稻L2和L23模型多元线性回归结果验证

Fig.6 Verification of MLSR result for L2and L23 models in rice

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基于叶片反射光谱和叶绿素荧光估测水稻叶片含水量

Estimation of rice leaf water content based on leaf reflectance spectrum and chlorophyll fluorescence

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