Hyperspectral estimation of leaf water content of Lycium barbarum based on continuum-removed method

doi:10.3969/j.issn.1004-1524.2022.04.14

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (4): 781-789.DOI: 10.3969/j.issn.1004-1524.2022.04.14

• Horticultural Science • Previous Articles Next Articles

Hyperspectral estimation of leaf water content of Lycium barbarum based on continuum-removed method

LI Yongmei¹^,²(), WANG Hao¹^,³^,^*(), ZHAO Yong³, ZHANG Ligen⁴

1. School of Civil and Hydraulic Engineering,Ningxia University, Yinchuan 750021, China
2. Institute of Agricultural Economy and Information Technology, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China
3. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research,Beijing 100038, China
4. Ningxia Academy of Building Research Co.,Ltd., Yinchuan 750021, China

Received:2021-06-28 Online:2022-04-25 Published:2022-04-28
Contact: WANG Hao

Abstract

Abstract:

In order to explore the potential of continuum removal method for estimating water content of Lycium barbarum leaves, Ningxia L. barbarum main plant variety Ningqi 7 was used as research object, the natural water loss method and drying method were used to determine the water content of L. barbarum leaf and the original spectral reflectance was processed by continuum removal method. Response characteristics of the continuum removal spectra to water content were analyzed, and correlation between the continuum removal spectra, absorption valley characteristic parameters and water content of leaves was analyzed, and a model for estimating water content of L. barbarum leaves was established.The result showed that continuum removal spectra could amplify the response characteristics of absorption valley band to water content, and the spectral reflectance in 1 100-2 200 nm band was the most responsive to the water change of L. barbarum leaves. The correlation between continuum removal spectra and water content of leaves in 1 500-1 850 nm band was enhanced. The multiple regression model based on the right area of the absorption peak in 900-1 100 nm band, the right area of the absorption peak in 1 270-1 700 nm band, and the total area of the absorption peak in 1 100-1 270 nm band was the best, its modeling set coefficient of determination was 0.787 0, validation set coefficient of determination was 0.800 3, root mean square error was 0.683 3 and mean relative error was 0.72%, which could be used to quantitatively estimate water content of L. barbarum leaves.

Key words: Lycium barbarum, water content, continuum-removed, hyperspectrum

CLC Number:

S127

LI Yongmei, WANG Hao, ZHAO Yong, ZHANG Ligen. Hyperspectral estimation of leaf water content of Lycium barbarum based on continuum-removed method[J]. Acta Agriculturae Zhejiangensis, 2022, 34(4): 781-789.

Figures/Tables 9

References 24

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测定日期 Measurement date	统计量 Statistical Sample	极小值 Minimum/%	极大值 Maximum/%	均值 Mean value/%	标准差 Standard deviation	方差 Variance
6月8日上午Morning of June 8	5	53.161	60.247	57.088	2.579	6.651
6月8日下午Afternoon of June 8	5	42.373	48.168	44.461	2.338	5.466
6月9日上午Morning of June 9	5	22.371	27.253	23.676	2.066	4.267
6月10日下午Afternoon of June10	5	7.602	11.908	9.491	1.677	2.813
6月12日晚上Evening of June 12	5	3.698	4.698	4.183	0.389	0.151

测定日期 Measurement date	统计量 Statistical Sample	极小值 Minimum/%	极大值 Maximum/%	均值 Mean value/%	标准差 Standard deviation	方差 Variance
6月8日上午Morning of June 8	5	53.161	60.247	57.088	2.579	6.651
6月8日下午Afternoon of June 8	5	42.373	48.168	44.461	2.338	5.466
6月9日上午Morning of June 9	5	22.371	27.253	23.676	2.066	4.267
6月10日下午Afternoon of June10	5	7.602	11.908	9.491	1.677	2.813
6月12日晚上Evening of June 12	5	3.698	4.698	4.183	0.389	0.151

样本集 Sample set	样本数量 Number of samples	极小值 Minimum/%	极大值 Maximum/%	均值 Mean value/%	标准差 Standard deviation	方差 Variance
建模样本Modeling sample	27	77.03	82.45	79.65	1. 53	1.92
检验样本Test sample	10	77.95	81.49	79.54	1.19	1.5

样本集 Sample set	样本数量 Number of samples	极小值 Minimum/%	极大值 Maximum/%	均值 Mean value/%	标准差 Standard deviation	方差 Variance
建模样本Modeling sample	27	77.03	82.45	79.65	1. 53	1.92
检验样本Test sample	10	77.95	81.49	79.54	1.19	1.5

光谱 Spectrum	敏感波长 Sensitive wavelength/ nm	模型Model		验证模型Validation set
光谱 Spectrum	敏感波长 Sensitive wavelength/ nm	回归模型 Regression model	决定系数 Determination coefficient	决定系数 Determination coefficient	RMSE	MRE/%
原始光谱Original spectrum	1 620	y=-31.581x+86.813	0.560 6	0.530 8	0.979 0	1.23
连续统去除光谱	1 602	y=-21.611x+90.104	0.619 4	0.603 7	0.884 3	1.12
Continuum-removal spectrum	1 662	y=-19.751x+91.128	0.619 2	0.597 2	0.949 1	1.13

Hyperspectral estimation of leaf water content of Lycium barbarum based on continuum-removed method

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参数parameters	900~1100 nm	1100~1270 nm	1270~1700 nm	1800~2200 nm
吸收波段波长Absorption band wavelength	0.588^**	0.270	0.664^**	0.124
最大吸收深度Maximum band depth	-0.487^*	-0.628^**	-0.658^**	0.335
吸收峰总面积Absorption peak area	-0.364	-0.640^**	-0.742^**	-0.342
吸收峰左面积Absorption peak left area	0.583^*	-0.188	-0.498^*	0.127
吸收峰右面积Absorption peak right area	-0.599^**	-0.443^*	-0.778^**	-0.379
对称度Symmetry	0.592^**	0.306	0.760^**	0.348
面积归一化最大吸收深度	-0.488^*	-0.621^**	-0.594^**	0.413
Area normalized maximum absorption depth

参数 Parameter	波长 Wavelength/ nm	模型建立Model establishment		模型验证Validation test
参数 Parameter	波长 Wavelength/ nm	回归模型 Regression model	决定系数 Determination coefficient	决定系数 Determination coefficient	RMSE	MRE/%
RA	900~1 100	y=-0.157 3x+98.404	0.358 7	0.159 3	1.245 4	1.96
TA	1 100~1 270	y=-0.504 2x+159.38	0.409 3	0.403 2	1.050 3	1.44
RA	1 270~1 700	y=-0.199 2x+109.44	0.620 2	0.606 0	0.882 8	1.00
RA	900~1 100	y=-0.122x₁-0.139x₂-0.132x₃+135.494	0.787 0	0.800 3	0.683 3	0.72
RA	1 270~1 700
TA	1 100~1 270

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