Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (8): 1266-1274.doi: 10.3724/SP.J.1006.2020.94157


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 Online:2020-08-12 Published:2020-04-13
  • Contact: Qiu-Xiang YI E-mail:yiqx@ms.xjb.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41571428);National Natural Science Foundation of China(41871328)


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

Table 1

Vegetation indices used for Car/Chla ratio and carotenoids estimation"

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]

Table 2

Statistical information of the Car/Chla ratio and Car for calibration and validation datasets"

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
标准偏差SD 平均值Mean 取值范围
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

Table 3

Vegetation indices predictive models for Car/Chla ratio and Car"

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
y= -30.58x+14.86
y= -0.009x+0.135
PRI-Polynomial y=1.897x2-1.31x+0.348
y=0.001x2-0.044x+0.349 R2=0.487
PRI*CI-Linear y= -0.155x+0.328
y= -5.35x +14.37
y= -0.127x + 0.32
y= -0.0718x+1.1565
PRI*CI-Polynomial y=0.104x2-0.293x+0.341
PSRI-Linear y=3.711x + 0.323
y=155.2x + 14.47
y=1.74x + 0.293
PSRI-Polynomial y= -180.3x2-0.66x+0.314
y = -11414x2-122.7x+13.9
y= -54.95x2+2.56x+0.31
y= -0.0004x2 +0.01x-0.09
CCRI-Linear y=0.173x+0.079
CCRI-Polynomial y= -0.134x2+0.49x-0.097
y= -7.09x2 +22.3x-2.89
y= -0.364x2+0.976x-0.317
y= -0.008x2 +0.239x-0.51

Fig. 1

Relationships between the number of latent variables and the predicted residual sums of squares (PRESS)"

Fig. 2

Measured vs. estimated Car/Chla ratio and Car for PLSR models using calibration dataset"

Table 4

Cross-validation results for the Car/Chla and Car predictive models"

均方根误差RMSE 均方根误差
RMSE (%)
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

Fig. 3

Measured vs. estimated Car/Chla ratio and Car using validation dataset"

Fig. 4

Relationship of PROSPECT and SAILH simulated PRI with Car/Chl ratio"

[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] ZHOU Jing-Yuan, KONG Xiang-Qiang, ZHANG Yan-Jun, LI Xue-Yuan, ZHANG Dong-Mei, DONG He-Zhong. Mechanism and technology of stand establishment improvements through regulating the apical hook formation and hypocotyl growth during seed germination and emergence in cotton [J]. Acta Agronomica Sinica, 2022, 48(5): 1051-1058.
[2] SUN Si-Min, HAN Bei, CHEN Lin, SUN Wei-Nan, ZHANG Xian-Long, YANG Xi-Yan. Root system architecture analysis and genome-wide association study of root system architecture related traits in cotton [J]. Acta Agronomica Sinica, 2022, 48(5): 1081-1090.
[3] YAN Xiao-Yu, GUO Wen-Jun, QIN Du-Lin, WANG Shuang-Lei, NIE Jun-Jun, ZHAO Na, QI Jie, SONG Xian-Liang, MAO Li-Li, SUN Xue-Zhen. Effects of cotton stubble return and subsoiling on dry matter accumulation, nutrient uptake, and yield of cotton in coastal saline-alkali soil [J]. Acta Agronomica Sinica, 2022, 48(5): 1235-1247.
[4] ZHENG Shu-Feng, LIU Xiao-Ling, WANG Wei, XU Dao-Qing, KAN Hua-Chun, CHEN Min, LI Shu-Ying. On the green and light-simplified and mechanized cultivation of cotton in a cotton-based double cropping system [J]. Acta Agronomica Sinica, 2022, 48(3): 541-552.
[5] ZHANG Yan-Bo, WANG Yuan, FENG Gan-Yu, DUAN Hui-Rong, LIU Hai-Ying. QTLs analysis of oil and three main fatty acid contents in cottonseeds [J]. Acta Agronomica Sinica, 2022, 48(2): 380-395.
[6] ZHANG Te, WANG Mi-Feng, ZHAO Qiang. Effects of DPC and nitrogen fertilizer through drip irrigation on growth and yield in cotton [J]. Acta Agronomica Sinica, 2022, 48(2): 396-409.
[7] ER Chen, LIN Tao, XIA Wen, ZHANG Hao, XU Gao-Yu, TANG Qiu-Xiang. Coupling effects of irrigation and nitrogen levels on yield, water distribution and nitrate nitrogen residue of machine-harvested cotton [J]. Acta Agronomica Sinica, 2022, 48(2): 497-510.
[8] ZHAO Wen-Qing, XU Wen-Zheng, YANG Liu-Yan, LIU Yu, ZHOU Zhi-Guo, WANG You-Hua. Different response of cotton leaves to heat stress is closely related to the night starch degradation [J]. Acta Agronomica Sinica, 2021, 47(9): 1680-1689.
[9] YUE Dan-Dan, HAN Bei, Abid Ullah, ZHANG Xian-Long, YANG Xi-Yan. Fungi diversity analysis of rhizosphere under drought conditions in cotton [J]. Acta Agronomica Sinica, 2021, 47(9): 1806-1815.
[10] ZENG Zi-Jun, ZENG Yu, YAN Lei, CHENG Jin, JIANG Cun-Cang. Effects of boron deficiency/toxicity on the growth and proline metabolism of cotton seedlings [J]. Acta Agronomica Sinica, 2021, 47(8): 1616-1623.
[11] GAO Lu, XU Wen-Liang. GhP4H2 encoding a prolyl-4-hydroxylase is involved in regulating cotton fiber development [J]. Acta Agronomica Sinica, 2021, 47(7): 1239-1247.
[12] MA Huan-Huan, FANG Qi-Di, DING Yuan-Hao, CHI Hua-Bin, ZHANG Xian-Long, MIN Ling. GhMADS7 positively regulates petal development in cotton [J]. Acta Agronomica Sinica, 2021, 47(5): 814-826.
[13] XU Nai-Yin, ZHAO Su-Qin, ZHANG Fang, FU Xiao-Qiong, YANG Xiao-Ni, QIAO Yin-Tao, SUN Shi-Xian. Retrospective evaluation of cotton varieties nationally registered for the Northwest Inland cotton growing regions based on GYT biplot analysis [J]. Acta Agronomica Sinica, 2021, 47(4): 660-671.
[14] ZHOU Guan-Tong, LEI Jian-Feng, DAI Pei-Hong, LIU Chao, LI Yue, LIU Xiao-Dong. Efficient screening system of effective sgRNA for cotton CRISPR/Cas9 gene editing [J]. Acta Agronomica Sinica, 2021, 47(3): 427-437.
[15] HAN Bei, WANG Xu-Wen, LI Bao-Qi, YU Yu, TIAN Qin, YANG Xi-Yan. Association analysis of drought tolerance traits of upland cotton accessions (Gossypium hirsutum L.) [J]. Acta Agronomica Sinica, 2021, 47(3): 438-450.
Full text



No Suggested Reading articles found!