基于连续统去除法的枸杞叶片含水率高光谱估算

doi:10.3969/j.issn.1004-1524.2022.04.14

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (4): 781-789.DOI: 10.3969/j.issn.1004-1524.2022.04.14

基于连续统去除法的枸杞叶片含水率高光谱估算

李永梅¹^,²(), 王浩¹^,³^,^*(), 赵勇³, 张立根⁴

1.宁夏大学土木与水利工程学院,宁夏银川 750021
2.宁夏农林科学院农业经济与信息技术研究所,宁夏银川 750002
3.中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京 100038
4.宁夏建筑科学研究院股份有限公司,宁夏银川 750021

收稿日期:2021-06-28 出版日期:2022-04-25 发布日期:2022-04-28
通讯作者: 王浩
作者简介:*王浩,E-mail: wanghao@iwhr.com
李永梅(1979—),女,山西平遥人,博士研究生,助理研究员,主要从事植物遥感、旱区水环境与水土资源调控等研究。E-mail: ymlisx2014@163.com
基金资助:
宁夏自然科学基金(2020AAC03294);宁夏自然科学基金(NZ17133);宁夏回族自治区农业科技自主创新专项科技创新引导项目(NKYJ-18-21)

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

摘要/Abstract

摘要：

为探讨连续统去除法估算枸杞叶片含水率的潜力,以宁夏枸杞主栽品种宁杞7号为研究对象,采用自然失水法和烘干法测定枸杞叶片含水率,采用连续统去除法对原始光谱反射率进行处理,分析连续统去除光谱对含水率的响应特征,分析连续统去除光谱、吸收特征参数与叶片含水率的相关性,并建立枸杞叶片含水率估算模型。研究表明:连续统去除光谱能放大吸收谷波段对含水率的响应特征,1 100~2 200 nm波段光谱反射率对枸杞叶片水分变化的响应能力最强;连续统去除光谱在1 500~1 850 nm波段与叶片含水率之间的相关性得到增强;基于连续统去除光谱敏感波长和1 270~1 700 nm波段的吸收峰右面积建立的一元回归模型优于原始光谱敏感波长;基于900~1 100 nm波段的吸收峰右面积、1 270~1 700 nm波段的吸收峰右面积和1 100~1 270 nm波段的吸收峰总面积建立的多元回归模型估算效果最好,其模型R²=0.787 0,检验R²=0.800 3,均方根误差为0.683 3,平均相对误差为0.72%,可用来定量估算枸杞叶片含水率。

关键词: 枸杞, 含水率, 连续统去除法, 高光谱

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

中图分类号:

S127

李永梅, 王浩, 赵勇, 张立根. 基于连续统去除法的枸杞叶片含水率高光谱估算[J]. 浙江农业学报, 2022, 34(4): 781-789.

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.

图/表 9

参考文献 24

[1]	郭建茂, 高云峰, 李淑婷, 等. 基于多角度高光谱遥感的冬小麦叶片含水率估算模型[J]. 安徽农业大学学报, 2019, 46(1): 124-132.
	GUO J M, GAO Y F, LI S T, et al. Estimation model of leaf water content of winter wheat based on multi-angle hyperspectral remote sensing[J]. Journal of Anhui Agricultural University, 2019, 46(1): 124-132. (in Chinese with English abstract)
[2]	HASSANLI A M, AHMADIRAD S, BEECHAM S. Evaluation of the influence of irrigation methods and water quality on sugar beet yield and water use efficiency[J]. Agricultural Water Management, 2010, 97(2): 357-362. DOI URL
[3]	冀荣华, 郑立华, 邓小蕾, 等. 基于反射光谱的苹果叶片叶绿素和含水率预测模型[J]. 农业机械学报, 2014, 45(8): 269-275.
	JI R H, ZHENG L H, DENG X L, et al. Forecasting chlorophyll content and moisture of apple leaves in different tree growth period based on spectral reflectance[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(8): 269-275. (in Chinese with English abstract)
[4]	张玮, 王鑫梅, 潘庆梅, 等. 雷竹冠层叶片反射光谱特征及其对叶片水分变化的响应[J]. 林业科学研究, 2019, 32(3): 73-79.
	ZHANG W, WANG X M, PAN Q M, et al. Spectral reflectance characteristics of Phyllostachys violascens canopy leaves in response to water change[J]. Forest Research, 2019, 32(3): 73-79. (in Chinese with English abstract)
[5]	李粉玲, 常庆瑞. 基于连续统去除法的冬小麦叶片全氮含量估算[J]. 农业机械学报, 2017, 48(7): 174-179.
	LI F L, CHANG Q R. Estimation of winter wheat leaf nitrogen content based on continuum removed spectra[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(7): 174-179. (in Chinese with English abstract)
[6]	贾雯晴. 基于高光谱的小麦水分状况监测研究[D]. 南京: 南京农业大学, 2013.
	JIA W Q. Monitoring water status based on hyperspectra in wheat[D]. Nanjing: Nanjing Agricultural University, 2013. (in Chinese with English abstract)
[7]	梁亮, 张连蓬, 林卉, 等. 基于导数光谱的小麦冠层叶片含水量反演[J]. 中国农业科学, 2013, 46(1): 18-29.
	LIANG L, ZHANG L P, LIN H, et al. Estimating canopy leaf water content in wheat based on derivative spectra[J]. Scientia Agricultura Sinica, 2013, 46(1): 18-29. (in Chinese with English abstract)
[8]	潘庆梅, 张劲松, 张俊佩, 等. 不同品种核桃叶片含水量与高光谱反射率的相关性差异分析[J]. 林业科学研究, 2019, 32(6): 1-6.
	PAN Q M, ZHANG J S, ZHANG J P, et al. Analysis of correlation and differences between leaf moisture and hyperspectral reflectance among different walnut varieties[J]. Forest Research, 2019, 32(6): 1-6. (in Chinese with English abstract)
[9]	曹晓兰, 邓梦洁, 汪佩佩. 基于PLSR的苎麻叶片含水量估测模型建立及优化[J]. 激光生物学报, 2018, 27(5): 467-473.
	CAO X L, DENG M J, WANG P P. Buildling and optimizing of the PLSR-based estimation model on ramie leaf’s water content[J]. Acta Laser Biology Sinica, 2018, 27(5): 467-473. (in Chinese with English abstract)
[10]	李永梅, 张学俭. 基于光谱指数的枸杞叶片水分含量遥感监测研究[J]. 地理与地理信息科学, 2019, 35(5): 16-21.
	LI Y M, ZHANG X J. Remote sensing monitoring of leaf water content in Lycium barbarum based on spectral index[J]. Geography and Geo-Information Science, 2019, 35(5): 16-21. (in Chinese with English abstract)
[11]	吾木提·艾山江, 买买提·沙吾提, 尼加提·卡斯木, 等. 基于灰色关联法的春小麦叶片含水量高光谱估测模型研究[J]. 光谱学与光谱分析, 2018, 38(12): 3905-3911.
	UMUT H, MAMAT S, NIJAT K, et al. Hyperspectral estimation model of leaf water content in spring wheat based on grey relational analysis[J]. Spectroscopy and Spectral Analysis, 2018, 38(12): 3905-3911. (in Chinese with English abstract)
[12]	李珺, 宋文龙. 基于光谱反射特征的草莓叶片含水率模型[J]. 东北林业大学学报, 2016, 44(1): 72-74, 80.
	LI J, SONG W L. Water content model for strawberry leaves with spectral signature[J]. Journal of Northeast Forestry University, 2016, 44(1): 72-74, 80. (in Chinese with English abstract)
[13]	SEELIG H D, HOEHN A, STODIECK L S, et al. The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared[J]. International Journal of Remote Sensing, 2008, 29(13): 3701-3713. DOI URL
[14]	贾方方, 洪权春, 宋唯一. 基于去包络线法的番茄叶霉病发病程度估测方法[J]. 中国生态农业学报, 2017, 25(6): 805-811.
	JIA F F, HONG Q C, SONG W Y. Continuum removal method for monitoring Fulvia fulva morbidity using hyperspectral data[J]. Chinese Journal of Eco-Agriculture, 2017, 25(6): 805-811. (in Chinese with English abstract)
[15]	竞霞, 王纪华, 宋晓宇, 等. 棉花黄萎病病情严重度的连续统去除估测法[J]. 农业工程学报, 2010, 26(1): 193-198.
	JING X, WANG J H, SONG X Y, et al. Continuum removal method for cotton verticillium wilt severity monitoring with hyperspectraldata[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(1): 193-198. (in Chinese with English abstract)
[16]	张雪红, 田庆久. 基于连续统去除法的冬小麦叶片氮积累量的高光谱评价[J]. 生态学杂志, 2010, 29(1): 181-186.
	ZHANG X H, TIAN Q J. Hyperspectral evaluation of nitrogen accumulation in winter wheat leaves based on continuum-removed method[J]. Chinese Journal of Ecology, 2010, 29(1): 181-186. (in Chinese with English abstract)
[17]	张金恒. 基于连续统去除法的水稻氮素营养光谱诊断[J]. 植物生态学报, 2006, 30(1): 78-82. DOI
	ZHANG J H. Rice nitrogen nutrition diagnosis using continuum-removed reflectance[J]. Journal of Plant Ecology, 2006, 30(1): 78-82. (in Chinese with English abstract)
[18]	张雪红, 刘绍民, 何蓓蓓. 基于包络线消除法的油菜氮素营养高光谱评价[J]. 农业工程学报, 2008, 24(10): 151-155.
	ZHANG X H, LIU S M, HE B B. Hyperspectral evaluation of rape nitrogen nutrition using continuum-removed method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(10): 151-155. (in Chinese with English abstract)
[19]	韩兆迎, 朱西存, 王凌, 等. 基于连续统去除法的苹果树冠SPAD高光谱估测[J]. 激光与光电子学进展, 2016, 53(2): 023001.
	HAN Z Y, ZHU X C, WANG L, et al. Hyperspectral evaluation of SPAD value of apple tree canopy based on continuum-removed method[J]. Laser & Optoelectronics Progress, 2016, 53(2): 023001. (in Chinese with English abstract)
[20]	郑煜, 常庆瑞, 王婷婷, 等. 基于连续统去除和偏最小二乘回归的油菜SPAD高光谱估算[J]. 西北农林科技大学学报(自然科学版), 2019, 47(8): 37-45.
	ZHENG Y, CHANG Q R, WANG T T, et al. Hyperspectral estimation of SPAD value in oilseed rape based on continuum removal and partial least squares regression[J]. Journal of Northwest A & F University (Natural Science Edition), 2019, 47(8): 37-45. (in Chinese with English abstract)
[21]	林波, 杨玉静. 利用连续统去除方法遥感反演冠层水分含量的比较研究[J]. 气象研究与应用, 2012, 33(S1): 181-184.
	LIN B, YANG Y J. Comparison of remote sensing inversion of canopy water content using continuum removal methods[J]. Journal of Meteorological Research and Application, 2012, 33(S1): 181-184. (in Chinese)
[22]	张佳华, 许云, 姚凤梅, 等. 植被含水量光学遥感估算方法研究进展[J]. 中国科学: 技术科学, 2010, 40(10): 1121-1129.
	ZHANG J H, XU Y, YAO F M, et al. Advances in estimation methods of vegetation water content based on optical remote sensing techniques[J]. Scientia Sinica(Technologica), 2010, 40(10): 1121-1129. (in Chinese)
[23]	JACQUEMOUD S, USTIN S L, VERDEBOUT J, et al. Estimating leaf biochemistry using the PROSPECT leaf optical properties model[J]. Remote Sensing of Environment, 1996, 56(3): 194-202. DOI URL
[24]	刘畅, 孙鹏森, 刘世荣. 水分敏感的反射光谱指数比较研究: 以锐齿槲栎为例[J]. 植物生态学报, 2017, 41(8): 850-861. DOI
	LIU C, SUN P S, LIU S R. A comparison of spectral reflectance indices in response to water: a case study of Quercus aliena var. acuteserrata[J]. Chinese Journal of Plant Ecology, 2017, 41(8): 850-861. (in Chinese with English abstract) DOI URL

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

基于连续统去除法的枸杞叶片含水率高光谱估算

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

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 24

相关文章 15

编辑推荐

Metrics

本文评价

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

[1]	蔡瑶, 缪宇轩, 吴浩, 王丹. 高CO₂浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演[J]. 浙江农业学报, 2022, 34(3): 582-589.
[2]	郭晗, 徐敏贤, 徐飞飞, 罗明, 陆洲, 张序. 基于机载高光谱数据的田间尺度胡敏酸含量估算[J]. 浙江农业学报, 2021, 33(12): 2358-2369.
[3]	肖志云, 王伊凝. 基于RF-VR的紫丁香叶片叶绿素含量高光谱反演[J]. 浙江农业学报, 2021, 33(11): 2164-2173.
[4]	钱家炜, 刘晓青, 张静静, 周卫红, 李建龙. 张家港市农田土壤重金属含量高光谱遥感监测模型构建[J]. 浙江农业学报, 2020, 32(8): 1437-1445.
[5]	陆锡昆, 罗亚辉, 蒋蘋, 胡文武. 基于高光谱的油茶籽含水量检测方法[J]. 浙江农业学报, 2020, 32(7): 1302-1310.
[6]	张小彦, 何静, 侯彩霞, 张树衡. 枸杞根腐病菌拮抗菌株的筛选与鉴定[J]. 浙江农业学报, 2020, 32(5): 858-865.
[7]	刘予煊, 程焕, 叶兴乾, 刘慧燕, 方海田. 不同菌株发酵枸杞汁中生物活性物质与香气组成物质含量变化[J]. 浙江农业学报, 2020, 32(3): 499-509.
[8]	禹文杰, 王彩霞, 乔芦, 王松磊, 贺晓光. 基于高光谱成像技术的泾源黄牛肉色度PLSR预测模型构建[J]. 浙江农业学报, 2020, 32(3): 527-533.
[9]	王念一, 于丰华, 许童羽, 杜文, 郭忠辉, 张国圣. 基于机器学习的粳稻叶片叶绿素含量高光谱反演建模[J]. 浙江农业学报, 2020, 32(2): 359-366.
[10]	宋春雨, 甘淑, 袁希平, 胡琳, 李雁, 闫馨方. 滇中典型乔木植被高光谱特征[J]. 浙江农业学报, 2020, 32(11): 1978-1986.
[11]	方向, 金秀, 朱娟娟, 李绍稳. 基于可见-近红外光谱预处理建模的土壤速效氮含量预测[J]. 浙江农业学报, 2019, 31(9): 1523-1530.
[12]	孟庆龙, 张艳, 尚静. 基于高光谱成像的猕猴桃表面疤痕无损识别[J]. 浙江农业学报, 2019, 31(8): 1372-1378.
[13]	王美玲, 焦琳琳, 王晓红, 吴兵, 肖星星. 曹妃甸湿地典型植被光谱特征差异性分析[J]. 浙江农业学报, 2019, 31(6): 963-969.
[14]	王彦翔, 张艳, 杨成娅, 孟庆龙, 尚静. 基于深度学习的农作物病害图像识别技术进展[J]. 浙江农业学报, 2019, 31(4): 669-676.
[15]	孟幼青, 翁海勇, 岑海燕, 李红叶, 何勇. 潜伏期柑橘黄龙病宿主糖代谢及近红外光谱特征[J]. 浙江农业学报, 2019, 31(3): 428-435.