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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)

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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"

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