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

doi:10.3969/j.issn.1004-1524.2023.06.04

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (6): 1265-1277.DOI: 10.3969/j.issn.1004-1524.2023.06.04

• Crop Science • Previous Articles Next Articles

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

Abstract

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

CLC Number:

TP79
S511

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.

Figures/Tables 13

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)]

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.

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

Fig.2 Variation characteristics of fluorescence parameter

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

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

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^**

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

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

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^**

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

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

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

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

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

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

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

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