Welcome to Acta Agronomica Sinica，

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

• TILLAGE & CULTIVATION · PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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)

RichHTML371

11

PDF

1248

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

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"

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

Table 3

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

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

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"

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

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] Peng Jia-Luo, Li Ying, Li Dan-Dan, Yang Jun-Ning, Guo Xue-Feng, Zhang Wen-Jiao, Yu Xiao-Xue, Zhou Ya-Rong, Wang Zhen-Yu, Wang Cai-Xiang, Ma Xiong-Feng, Su Jun-Ji. Identification of class I LBD family members in upland cotton and function and haplotype analyses of GhLBD6 in regulating flowering period [J]. Acta Agronomica Sinica, 2026, 52(6): 1682-1697.
[2] Zhao Jia-Xue, Zhou Long-Hao, Guo Qi-Yuan, Shang Lun-Xiao, Wang Han, Liu Zhi-Tao, Chen Xi, Zhang Xiao-Pei, Song Xian-Liang, Ahmedov Miraziz Baltaevich, Mao Li-Li. Long-term stubble return and subsoiling enhance cotton yields in coastal saline-alkali soils by improving soil conditions and photosynthetic characteristics [J]. Acta Agronomica Sinica, 2026, 52(5): 1548-1560.
[3] Zhang Xi, Wang Guang-En, Li Shao-Qi, Liu Yi, Li Jun-Lan, Qian Yu-Yuan. Transcriptome sequencing-based analysis on the formation mechanism of fiber micronaire differences between two sister lines derived from Gossypium hirsutum-G. barbadense hybrid [J]. Acta Agronomica Sinica, 2026, 52(5): 1442-1458.
[4] Zhou Qi-Xiang, Zhu Yan, Wang Chu-Bo, Zhu Bo-Lin, Li Jun-Bo, Song Li-Bing. Modeling the effects of climate change on cotton phenology and potential yield in Xinjiang based on the DSSAT model [J]. Acta Agronomica Sinica, 2026, 52(2): 590-602.
[5] LI Yi-Qian, XU Shou-Zhen, LIU Ping, MA Qi, XIE Bin, CHEN Hong. Genome-wide association study of yield components using a 40K SNP array and identification of a stable locus for boll weight in upland cotton (Gossypium hirsutum L.) [J]. Acta Agronomica Sinica, 2025, 51(8): 2128-2138.
[6] GUO Dong-Cai, LYU Tao, CAI Yong-Sheng, MAI WU-LU-DA·AI He-Mai-Ti, CHEN Quan-Jia, QU Yan-Ying, ZHENG Kai. Meta-analysis of QTL and identification of candidate genes for fiber quality in cotton [J]. Acta Agronomica Sinica, 2025, 51(6): 1445-1466.
[7] WANG Ya-Wen, QI Zheng-Yang, YOU Jia-Qi, NIE Xin-Hui, CAO Juan, YANG Xi-Yan, TU Li-Li, ZHANG Xian-Long, WANG Mao-Jun. Preparation of cotton 60K functional locus gene chip and its application to genetic research [J]. Acta Agronomica Sinica, 2025, 51(5): 1178-1188.
[8] DING Jun-Feng, XU Ying-Fei, ZHANG Xiang, CHEN Yuan, CHEN De-Hua. Effects of the plant growth regulator IBA on the survival and growth of substrate- grown transplanted cotton seedlings [J]. Acta Agronomica Sinica, 2025, 51(12): 3331-3341.
[9] HALIHASHI Yibati, ZHANG Yan, LI Qing-Jun, XU Xin-Peng, HE Ping. Study on smart fertilizer recommendation methods based on yield response and agronomic efficiency for cotton [J]. Acta Agronomica Sinica, 2025, 51(11): 3052-3064.
[10] ZHAO Hai-Hong, LI Meng-Yuan, LIU Jin-Jing, WANG Yuan-Yuan, DU Lei, WANG Juan, DONG Cheng-Guang, LI Cheng-Qi. Detection of QTNs and QTN-by-environment interactions for plant height in upland cotton (G. hirsutum L.) using the 3VmrMLM method [J]. Acta Agronomica Sinica, 2025, 51(10): 2619-2631.
[11] LI Ya-Wei, XU Ying-Ying, ZUO Chun-Yang, LIU Ruo-Nan, LIANG Ya-Jun, KONG Jie, ZHANG Xian-Long, MIN Ling. Construction of a meiotic progression identification system in cotton and analysis of its response to high-temperature stress [J]. Acta Agronomica Sinica, 2025, 51(10): 2570-2580.
[12] CHEN Jia-Wei, LIN Yan, ZHANG Ming-Xing, ZHOU Shi-Jing, RAO Li-Qun, ZHOU Chi, LI Xin. Effects of Bacillus velezensis YCH92 on the rhizosphere microbial community and yield of cotton [J]. Acta Agronomica Sinica, 2025, 51(10): 2821-2835.
[13] WANG Yuan, XU Jia-Yin, DONG Er-Wei, WANG Jin-Song, LIU Qiu-Xia, HUANG Xiao-Lei, JIAO Xiao-Yan. Effects of manure replacement of chemical fertilizer nitrogen on yield, nitrogen accumulation, and quality of foxtail millet [J]. Acta Agronomica Sinica, 2025, 51(1): 149-160.
[14] XIE Zhang-Shu, XIE Xue-Fang, TU Xiao-Ju, LIU Ai-Yu, DONG He-Zhong, ZHOU Zhong-Hua. Research progress in phytohormone regulation of square and boll shedding in cotton [J]. Acta Agronomica Sinica, 2025, 51(1): 1-29.
[15] XIN Ming-Hua, MI Ya-Di, WANG Guo-Ping, LI Xiao-Fei, LI Ya-Bing, DONG He-Lin, HAN Ying-Chun, FENG Lu. Effect of row spacing configuration and density regulation on dry matter production and yield in cotton [J]. Acta Agronomica Sinica, 2025, 51(1): 221-232.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd