欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2020, Vol. 46 ›› Issue (8): 1266-1274.doi: 10.3724/SP.J.1006.2020.94157

• 耕作栽培·生理生化 • 上一篇    下一篇

基于光谱指数和偏最小二乘的棉花类胡萝卜素/叶绿素a比值估算

易秋香1,2,3,*(),刘英1,2,3,常存1,2,3,钟瑞森1,2,3   

  1. 1中国科学院新疆生态与地理研究所/荒漠与绿洲生态国家重点实验室, 新疆乌鲁木齐 830011
    2新疆维吾尔自治区遥感与地理信息系统应用重点实验室, 新疆乌鲁木齐 830011
    3中国科学院大学, 北京 100049
  • 收稿日期:2019-10-22 接受日期:2020-03-24 出版日期:2020-08-12 网络出版日期:2020-04-13
  • 通讯作者: 易秋香
  • 基金资助:
    国家自然科学基金项目(41571428);国家自然科学基金项目(41871328)

Estimation of cotton Car/Chla ratio by hyperspectral vegetation indices and partial least square regression

YI Qiu-Xiang1,2,3,*(),LIU Ying1,2,3,CHANG Cun1,2,3,ZHONG Rui-Sen1,2,3   

  1. 1State Key Laboratory of Desert and Oasis Ecology/Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
    2Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, Xinjiang, China
    3University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-10-22 Accepted:2020-03-24 Published:2020-08-12 Published online:2020-04-13
  • Contact: Qiu-Xiang YI
  • Supported by:
    National Natural Science Foundation of China(41571428);National Natural Science Foundation of China(41871328)

RichHTML

10

PDF

811

摘要：

类胡萝卜素(Car)与叶绿素a含量比值(Car/Chla)的变化与植被生长发育变化、环境胁迫及叶片衰老特征等密切相关, 可作为植被生理生态及物候的监测指标。不同植被类型和植被品种其色素变化随植被生长发育呈现出不同的变化特征。为了探究适用于干旱区棉花Car/Chla比值估算的光谱指数和估算方法, 本研究通过2011年和2012年连续2年的大面积田间试验, 获取了棉花不同生育期的叶片及冠层尺度光谱反射率及色素含量信息, 对多种光谱指数及偏最小二乘回归(Partial Least Square Regression, PLSR)用于Car/Chla比值和Car估算进行了探讨。对比表明, 基于光化学指数(Photochemical Reflectance Index, PRI)的线性和一元二次模型对Car/Chla比值和Car的估算精度最高, 由PRI-Car/Chla线性模型得到的叶片和冠层尺度的Car/Chla比值估算值与实测值之间的决定系数R2大于0.6, PRI-Car的R2大于0.36; 基于PLSR模型得到的Car/Chla比值估算值与实测值之间的拟合关系略优于基于PRI的估算模型, 由其得到的叶片及冠层尺度Car/Chla比值估算值与实测值之间的决定系数R2大于0.80, Car估算值与实测值之间R2大于0.73; 不论基于PRI还是基于PLSR方法, 对Car/Chla比值的估算精度均高于Car含量, 该结论进一步证实了Car/Chla比值遥感监测的可行性, 丰富了对棉花生长高温胁迫、养分胁迫等环境胁迫及病虫害等遥感监测的依据指标。

关键词: 类胡萝卜素/叶绿素a比值, 类胡萝卜素, 光化学指数, 偏最小二乘回归, 棉花

Abstract:

Estimating the ratio between carotenoid to chlorophyll a (Car/Chla) provides an additional avenue for the assessment of physiology and phenology of plant growth and development. With the aim of assessing cotton Car/Chla ratio from hyperspectral reflectance, a wide range of carotenoid (Car) and chlorophyll a concentrations, and leaf and canopy reflectance at cotton different growth stages were measured. The performance of a variety of Car/Chla ratio related vegetation indices and partial least square regression (PLSR) for Car/Chla ratio and Car estimation were tested. Among all tested vegetation indices, PRI (Photochemical Reflectance Index) and linear PRI models had the most significant correlations with Car/Chla ratio and Car, and could accurately estimate, Car/Chla ratio (R2leaf level = 0.69 and R2canopy level = 0.67) and Car concentration (R2leaf level = 0.44 and R2canopy level = 0.36). The best estimation of the Car/Chla ratio and Car was provided by PLSR models with R2 > 0.80 between the estimated and measured value for Car/Chla ratio and R2= 0.74 for Car. Both reflectance indices and PLSR method were more successful for the estimation of Car/Chla ratio than for that of Car concentration, indicating the promising potential of Car/Chla ratio as a powerful indicator using for plant status monitoring by remote sensing. Besides, accuracy test of models using validation dataset highlighted the remarkable performance of PLSR for Car/Chla (R2leaf level = 0.87 and R2canopy level = 0.84) and Car (R2leaf level = 0.73 and R2canopy level = 0.74) estimated by hyperspectral reflectance at both the leaf and canopy levels. The results further prove the remarkable performance of hyperspectral reflectance for the estimation of Car/Chla ratio, and enrich the parameters for monitoring high temperature stress, water deficit stress, and nutrient stress and pest diseases by remote sensing in cotton.

Key words: Car/Chla ratio, carotenoid, PRI (Photochemical Reflectance Index), PLSR (Partial Least Square Regression), cotton

表1

所采用的光谱指数"

指数 Index 全称 Full name 表达式 Formula 文献 Reference
PRI Photochemical Reflectance Index (R531-R570)/(R531+R570) [27]
PRI*CI Carotenoid/Chlorophyll Ratio Index (R531-R570)/(R531+R570)*((R760/R700)-1)) [23]
PSRI Plant senescence reflectance index (R678-R500)/R750 [16]
R515/R570 Simple ratio vegetation index R515/R570 [24]
CCRI Combined carotenoid/chlorophyll ratio index (R720-R521)*R705/(R750-R705)*R521 [25]

表2

用于模型构建和验证的Car/Chla比值以及Car的统计特征"

尺度
Level		 数据集
Dataset		 样本数n Car/Chla比值Car/Chla ratio 类胡萝卜素Car (μg cm-2)
平均值Mean 取值范围
Range		 标准偏差SD 平均值Mean 取值范围
Range		 标准偏差SD
叶片尺度
Leaf level		 总体All 141 0.286 0.109-0.495 0.081 13.49 3.97-23.06 3.99
验证样本Calibration 94 0.283 0.109-0.478 0.081 13.08 5.76-22.83 3.72
检验样本Validation 47 0.292 0.118-0.495 0.083 14.32 3.97-23.06 4.40
冠层尺度Canopy level 总体All 159 0.302 0.109-0.495 0.082 14.15 3.97-23.06 4.16
验证样本Calibration 104 0.300 0.109-0.440 0.082 13.88 5.76-20.93 3.91
检验样本Validation 55 0.306 0.118-0.495 0.082 14.55 3.97-23.06 4.51

表3

基于植被指数的Car/Chla比值及Car估算模型"

建模方法
Method		 叶片尺度Leaf level 冠层尺度Canopy level
比值Car/Chla ratio 类胡萝卜素Car 比值Car/Chla ratio 类胡萝卜素Car
PRI-Linear y= -0.834x+0.338
R2=0.723		 y= -30.58x+14.86
R2=0.541		 y=1.343x+0.313
R2=0.670		 y= -0.009x+0.135
R2=0.486
PRI-Polynomial y=1.897x2-1.31x+0.348
R2=0.732		 y=26.1x2-37.07x+14.9
R2=0.542		 y=3.191x2-1.679x+0.309
R2=0.681		 y=0.001x2-0.044x+0.349 R2=0.487
PRI*CI-Linear y= -0.155x+0.328
R2=0.638		 y= -5.35x +14.37
R2=0.426		 y= -0.127x + 0.32
R2=0.519		 y= -0.0718x+1.1565
R2=0.356
PRI*CI-Polynomial y=0.104x2-0.293x+0.341
R2=0.681		 y=5.38x2-12.49x+15.03
R2=0.488		 y=0.073x2-0.248x+0.321
R2=0.627		 y=0.01x2-0.34x+2.79
R2=0.431
PSRI-Linear y=3.711x + 0.323
R2=0.203		 y=155.2x + 14.47
R2=0.196		 y=1.74x + 0.293
R2=0.157		 y=0.0015x-0.0158
R2=0.093
PSRI-Polynomial y= -180.3x2-0.66x+0.314
R2=0.257		 y = -11414x2-122.7x+13.9
R2=0.314		 y= -54.95x2+2.56x+0.31
R2=0.239		 y= -0.0004x2 +0.01x-0.09
R2=0.244
CCRI-Linear y=0.173x+0.079
R2=0.501		 y=5.42x+6.38
R2=0.272		 y=0.182x+0.082
R2=0.405		 y=0.031x+0.758
R2=0.189
CCRI-Polynomial y= -0.134x2+0.49x-0.097
R2=0.580		 y= -7.09x2 +22.3x-2.89
R2=0.396		 y= -0.364x2+0.976x-0.317
R2=0.526		 y= -0.008x2 +0.239x-0.51
R2=0.417

图1

预测误差平方和(PRESS)与潜变量之间的变化关系"

图2

基于建模样本的Car/Chla-PLSR和Car-PLSR估算模型的估算值与实测值之间的线性关系"

表4

基于检验样本的Car/Chla比值及Car含量估算模型精度检验结果"

尺度
Level		 方法
Method		 参数
Parameter		 决定系数
R2		 均方根误差RMSE 均方根误差
RMSE (%)		 斜率
a		 截距
b
叶片尺度Leaf level PRI Car/Chla 0.69 0.05 15.8 0.66 0.09
Carotenoid 0.44 3.57 24.9 0.37 7.71
PLSR Car/Chla 0.87 0.03 10.7 0.83 0.04
Carotenoid 0.73 2.39 16.7 0.67 4.02
冠层尺度Canopy level PRI Car/Chla 0.67 0.05 15.3 0.67 0.09
Carotenoid 0.36 3.62 24.9 0.37 8.74
PLSR Car/Chla 0.84 0.03 10.1 0.84 0.05
Carotenoid 0.74 2.19 14.9 0.72 3.87

图3

基于检验样本的Car/Chla比值及Car含量估算值与实测值之间的拟合关系"

图4

PROSPECT和SAILH模型模拟的叶片及冠层PRI指数与Car/Chl之间的拟合关系"

[1] Demmig-Adams B. Survey of thermal energy dissipation and pigment composition in sun and shade leaves. Plant Cell Physiol, 1998,39:474-482.
[2] Zarco-Tejada P J, Miller J R, Harron J, Hu B, Noland T L, Goel N. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sens Environ, 2004,89:189-199.
[3] Zhang Y, Chen J M, Miller J, Noland T. Needle chlorophyll content retrieval from airborne hyperspectral imagery. Remote Sens Environ, 2008,112:3234-3247.
[4] Hunt E R, Doraiswamy P C, McMurtrey J E, Daughtry C S T, Perry E M, Akhmedov B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth OBS, 2013,21:103-112.
[5] Zarco-Tejada P J, Hornero A, Beck P S A, Kattenborn T, Kempeneers P, Hernández-Clemente R. Chlorophyll content estimation in an open-canopy conifer forest with Sentienl-2A and hyperspectral imagery in the context of forest decline. Remote Sens Environ, 2019,223:320-335.
pmid: 31007289
[6] Gitelson A A, Chivkunova O B, Merzlyak M N. Non-destructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. Am J Bot, 2009,96:1861-1868.
pmid: 21622307
[7] Young A, Britton G. Carotenoids and stress. In: Alscher R G Jr, Cumming J R, eds. Stress Response in Plants: Adaptation and Acclimation Mechanisms. New York: Wiley-Liss, 1990. pp 87-112.
[8] Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens Environ, 1998,66:273-285.
[9] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ, 2002,81:337-354.
doi: 10.1016/S0034-4257(02)00010-X
[10] Féret J B, François C, Gitelson A, Gregory P A, Barry K M, Panigada C, Richardson A D, Jacquemoud S. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens Environ, 2011,115:2742-2750.
[11] Hendry G A F, Houghton J D, Brown S B. The degradation of chlorophyll: a biological enigma. New Phytol, 1987,107:255-302.
[12] Biswall B. Carotenoid catabolism during leaf senescence and its control by light. J Photochem Photobiol B: Biol, 1995,30:3-14.
[13] Buchanan-Wollastin V. The molecular biology of leaf senescence. J Exp Bot, 1998,49:181-199.
[14] Gitelson A A, Merzlyak M N. Spectral reflectance changes associate with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol, 1994,143:286-292.
[15] Gitelson A A, Merzlyak M N. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B: Biol, 1994,22:247-252.
[16] Merzlyak M N, Gitelson A A, Chivkunova O B, Rakitin V Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plant, 1999,106:135-141.
[17] Sanger J E. Quantitative investigations of leaf pigments from their inception in buds through autumn coloration to decomposition in falling leaves. Ecology, 1972,52:1075-1089.
[18] Peñuelas J, Field C, Griffin K, Gamon J. Assessing community type, plant biomass, pigment composition and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sens Environ, 1993,46:1-25.
[19] Peñuelas J, Gamon J, Freeden A, Merino J, Field C. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sens Environ, 1994,48:135-146.
doi: 10.1016/0034-4257(94)90136-8
[20] 周贤峰. 色素含量比值进行作物氮素营养状况诊断方法研究. 中国科学院大学博士学位论文, 北京, 2017.
Zhou X F. Research on the Methods of Crop Nitrogen Status Diagnosis Based on Carotenoid and Chlorophyll Ratio Values. PhD Dissertation of University of Chinese Academy of Sciences, Beijing, China, 2017 (in Chinese with English abstract).
[21] Peñuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 1995,31:221-230.
[22] Nakaji T, Oguma H, Fujinuma Y. Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles. Int J Remote Sens, 2006,27:493-509.
[23] Garrity S R, Eitel J U H, Vierling L A. Disentangling the relationships between plant pigments and the photochemical reflectance index reveals a new approach for remote estimation of carotenoid content. Remote Sens Environ, 2011,115:628-635.
[24] Hernández-Clemente R, Navarro-Cerrillo R M, Zarco-Tejada P J. Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT + DART simulations. Remote Sens Environ, 2012,127:298-315.
[25] Zhou X F, Huang W J, Zhang J C, Kong W P, Casa R, Huang Y B. A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phonological status. Int J Appl Earth OBS, 2019,76:128-142.
[26] 蒋德安, 朱诚. 植物生理学实验指导. 成都: 成都科技大学出版社, 1999. pp 20-23.
Jiang D A, Zhu C. Guide for Plant Physiology Experiment. Chengdu: Chengdu University of Science and Technology Press, 1999. pp 20-23(in Chinese).
[27] Gamon J A, Peñuelas J, Field C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ, 1992,41:35-44.
[28] Yi Q X, Jiapaer G L, Chen J M, Bao A M, Wang F M. Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression. ISPRS J Photogramm, 2014,91:72-74.
[29] 依尔夏提·阿不来提, 买买提·沙吾提, 白灯莎·买买提艾力, 安申群, 马春玥. 基于随机森林法的棉花叶片叶绿素含量估算. 作物学报, 2019,45:81-90.
Ershat A, Mamat S, Baidengsha M, An S Q, Ma C Y. Estimation of leaf chlorophyll content in cotton based on the random forest approach. Acta Agron Sin, 2019,45:81-90 (in Chinese with English abstract).
[30] Geladi P, Kowalski B R. Partial least-squares regression: a tutorial. Analy Chimica Acta, 1986,185:1-17.
[31] Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab, 2001,58:109-130.
[32] Shao J. Linear model selection by cross-validation. J Am Stat Assoc, 1993,88:486-494.
[33] Chen S, Hong X, Harris C J, Sharkey P M. Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE Trans Syst Man Cybern B Cybern, 2004,34:898-911.
doi: 10.1109/tsmcb.2003.817107 pmid: 15376838
[34] Féret J B, Gitelson A, Noble S D, Jacquemoud S. PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens Environ, 2017,193:204-215.
[35] Kuusk A. The angular distribution of reflectance and vegetation indices in barley and clover canopies. Remote Sens Environ, 1991,37:143-151.
[36] Filella I, Porcar-Castell A, Munné-Bosch S, Back J, Garbulsky M F, Peñuelas J. PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int J Remote Sens, 2009,30:4443-4455.
[37] Stylinski C D, Gamon J A, Oechel W C. Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species. Oecologia, 2002,131:366-374.
doi: 10.1007/s00442-002-0905-9 pmid: 28547708
[38] Garbulsky M F, Peñuelas J, Papale D, Filella I. Remote estimation of carbon dioxide uptake of terrestrial ecosystems. Global Change Biol, 2008,14:2860-2867.
[39] Gamon J A, Serrano L, Surfus J S. The photochemical reflectance index: an optical indicator of photosynthesis radiation use efficiency across species, functional types and nutrient levels. Oecologia, 1997,112:492-501.
doi: 10.1007/s004420050337 pmid: 28307626
[40] Richardson A D, Berlyn G P. Spectral reflectance and photosynthetic properties of Betula papyrifera(Betulaceae) leaves along an elevational gradient on Mt. Mansfield, Vermont, USA. Am J Bot, 2002,89:88-94.
doi: 10.3732/ajb.89.1.88 pmid: 21669715
[41] Filella I, Amaro T, Araus J L, Peñuelas J. Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI). Physiol Plant, 1996,96:211-216.
[42] Trotter G M, Whitehead D, Pinkney E J. The photochemical reflectance index as a measure of photosynthetic light use efficiency for plants with varying foliar nitrogen contents. Int J Remote Sens, 2002,6:1207-1212.
[43] Nichol C J, Huemmrich K F, Black T A, Jarvis P G, Walthall C L, Grace J, Hall F G. Remote sensing of photosynthetic-light-use efficiency of boreal forest. Agric For Meteorol, 2000,101:131-142.
doi: 10.1016/S0168-1923(99)00167-7
[44] Peñuelas J, Inoue Y. Reflectance assessment of canopy CO2 uptake. Int J Remote Sens, 2000,21:3353-3356.
doi: 10.1080/014311600750019958
[1] 周静远, 孔祥强, 张艳军, 李雪源, 张冬梅, 董合忠. 基于种子萌发出苗过程中弯钩建成和下胚轴生长的棉花出苗壮苗机制与技术[J]. 作物学报, 2022, 48(5): 1051-1058.
[2] 孙思敏, 韩贝, 陈林, 孙伟男, 张献龙, 杨细燕. 棉花苗期根系分型及根系性状的关联分析[J]. 作物学报, 2022, 48(5): 1081-1090.
[3] 闫晓宇, 郭文君, 秦都林, 王双磊, 聂军军, 赵娜, 祁杰, 宋宪亮, 毛丽丽, 孙学振. 滨海盐碱地棉花秸秆还田和深松对棉花干物质积累、养分吸收及产量的影响[J]. 作物学报, 2022, 48(5): 1235-1247.
[4] 郑曙峰, 刘小玲, 王维, 徐道青, 阚画春, 陈敏, 李淑英. 论两熟制棉花绿色化轻简化机械化栽培[J]. 作物学报, 2022, 48(3): 541-552.
[5] 张艳波, 王袁, 冯甘雨, 段慧蓉, 刘海英. 棉籽油分和3种主要脂肪酸含量QTL分析[J]. 作物学报, 2022, 48(2): 380-395.
[6] 张特, 王蜜蜂, 赵强. 滴施缩节胺与氮肥对棉花生长发育及产量的影响[J]. 作物学报, 2022, 48(2): 396-409.
[7] 赵文青, 徐文正, 杨锍琰, 刘玉, 周治国, 王友华. 棉花叶片响应高温的差异与夜间淀粉降解密切相关[J]. 作物学报, 2021, 47(9): 1680-1689.
[8] 岳丹丹, 韩贝, Abid Ullah, 张献龙, 杨细燕. 干旱条件下棉花根际真菌多样性分析[J]. 作物学报, 2021, 47(9): 1806-1815.
[9] 曾紫君, 曾钰, 闫磊, 程锦, 姜存仓. 低硼及高硼胁迫对棉花幼苗生长与脯氨酸代谢的影响[J]. 作物学报, 2021, 47(8): 1616-1623.
[10] 马欢欢, 方启迪, 丁元昊, 池华斌, 张献龙, 闵玲. 棉花GhMADS7基因正调控棉花花瓣发育[J]. 作物学报, 2021, 47(5): 814-826.
[11] 许乃银, 赵素琴, 张芳, 付小琼, 杨晓妮, 乔银桃, 孙世贤. 基于GYT双标图对西北内陆棉区国审棉花品种的分类评价[J]. 作物学报, 2021, 47(4): 660-671.
[12] 周冠彤, 雷建峰, 代培红, 刘超, 李月, 刘晓东. 棉花CRISPR/Cas9基因编辑有效sgRNA高效筛选体系的研究[J]. 作物学报, 2021, 47(3): 427-437.
[13] 卢合全, 唐薇, 罗振, 孔祥强, 李振怀, 徐士振, 辛承松. 商品有机肥替代部分化肥对连作棉田土壤养分、棉花生长发育及产量的影响[J]. 作物学报, 2021, 47(12): 2511-2521.
[14] 王晔, 刘钊, 肖爽, 李芳军, 吴霞, 王保民, 田晓莉. 转PSAG12-IPT基因对棉花叶片衰老及产量和纤维品质的影响[J]. 作物学报, 2021, 47(11): 2111-2120.
[15] 杨琴莉, 杨多凤, 丁林云, 赵汀, 张军, 梅欢, 黄楚珺, 高阳, 叶莉, 高梦涛, 严孙艺, 张天真, 胡艳. 棉花花器官突变体的鉴定及候选基因的克隆[J]. 作物学报, 2021, 47(10): 1854-1862.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2