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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)

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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"

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