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Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (4): 991-1003.doi: 10.3724/SP.J.1006.2024.31041

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

Spectral characteristics of winter wheat canopy and estimation of aboveground biomass under elevated atmospheric CO2 concentration

HUANG Hong-Sheng(), ZHANG Xin-Yue, JU Hui, HAN Xue()   

  1. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2023-06-25 Accepted:2023-10-23 Online:2024-04-12 Published:2023-11-16
  • Contact: * E-mail: hanxue@caas.cn
  • Supported by:
    National Key Research and Development Program of China(2019YFA0607403);Special Project of Agricultural Green Low-carbon Science Center of Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences, and the Basic Scientific Research Business Expenses of Central-level Public Welfare Research Institutes(CAAS-CSGLCA-202301);Special Project of Agricultural Green Low-carbon Science Center of Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences, and the Basic Scientific Research Business Expenses of Central-level Public Welfare Research Institutes(BSRF202202)

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Abstract:

The objective of this study is to investigate the effect of elevated atmospheric CO2 concentration on the canopy spectral characteristics of winter wheat during the whole growth period and to establish quantitative relationships between above-ground biomass (AGB) and spectral parameters based on the screened sensitive bands. For this purpose, during the winter wheat growing season of 2021-2022, two treatment levels of atmospheric CO2 concentration (ACO2, (420±20) μL L-1) and elevated CO2 concentration (ECO2, (550±20) μL L-1) were set based on the Free Atmospheric CO2 Enrichment System (Mini-FACE), and the changes of spectral features were analyzed under elevated CO2 concentration. AGB sensitive bands were screened and the estimation models of AGB were constructed based on the successive projections algorithm (SPA), stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR). The results showed that elevated CO2 concentration significantly increased AGB in winter wheat at jointing and anthesis stages. The red-edge reflectance, near red edge reflectance, and red-edge area increased at jointing stage and decreased at anthesis and maturity stages. The positions of the blue-edge, yellow-edge, and red-edge were shifted at different growth stages. The sensitive spectral bands of AGB are mainly distributed in the red-edge and near red-edge bands, and the elevated CO2 concentration narrows the range of the sensitive bands of AGB, but does not affect the estimation of AGB. The SMLR and PLSR models of AGB both achieved high estimation accuracy (R2 > 0.8), where the characteristic parameters such as R799', Dy, SDy, and PRI in the SMLR model were significantly correlated with AGB, with an R2 of 0.866. The PLSR model (R2 > 0.9) outperformed the SMLR model in terms of estimation accuracy and stability. This study can provide the theoretical basis and technical methods for the remote sensing monitoring of winter wheat growth and development under elevated CO2 concentration in the future.

Key words: elevated CO2 concentration, winter wheat, above ground biomass, canopy spectral features, regression analysis

Table 1

Definition and calculation formula of vegetation index"

植被指数
Vegetation index		 名称
Name		 计算公式
Calculation formula		 参考文献
References
ARI-1 花青素反射率指数-1
Anthocyanin reflectance index 1		 1/R550-1/R700 [21]
ARI-2 花青素反射率指数-2
Anthocyanin reflectance index 2		 R800×(1/R550-1/R700) [22]
ARVI 大气阻抗植被指数
Atmospherically resistant regetation Index		 (R810-(2×R680-R480))/(R810+(2×R680-R480)) [23]
EVI 增强植被指数
Enhanced vegetation index		 (2.5×(R782-R675))/(R782+6×R675-7.5×R445+1) [24]
mND705 改进红边归一化植被指数
Modified red edge normalized difference vegetation index		 (R750-R705)/(R750+R705-2×R445) [25]
NDVI 归一化植被指数
Normalized difference vegetation index		 (R750-R550)/(R750+R550) [26]
NDVI705 红边归一化植被指数
Red edge normalized difference vegetation index		 (R750-R705)/(R750+R705) [27]
PRI 光化学植被指数
Photochemical reflectance index		 (R570-R531)/(R570+R531) [28]
PSRI 植物衰老反射率指数
Plant senescence reflectance index		 (R680-R500)/R750 [28]
SR 比值植被指数
Simple ratio index		 R985/R745 [28]

Table 2

Definition and description of spectral characteristic parameters"

光谱特征参数
Spectral
characteristics parameters		 名称
Name		 说明
Illustration
Rx 在x nm处的冠层光谱反射率。
Canopy spectral reflectance at x nm.
Rx' 在x nm处的冠层光谱反射率的一阶微分。
First order differentiation of canopy spectral reflectance at x nm.
AR 红边反射率平均值
Average red edge reflectance		 冠层光谱680~760 nm波段的平均值。
Mean values of the 680-760 nm band of the canopy spectrum.
Rg 绿峰反射率
Green peak reflectance		 冠层光谱510~560 nm波段内最大光谱反射率值。
Maximum spectral reflectance values in the 510-560 nm band of the canopy spectrum.
λg 绿峰位置
Green peak location		 冠层光谱510~560 nm波段内最大光谱反射率对应的位置。
Location of maximum spectral reflectance in the 510-560 nm band of the canopy spectrum.
Db 蓝边幅值
Blue edge amplitude		 冠层光谱490~530 nm波段内一阶微分的最大值。
Maximum value of the first order differential in the 490-530 nm band of the canopy spectrum.
λb 蓝边位置
Blue edge location		 冠层光谱490~530nm波段内一阶微分最大值对应波长的位置。
Location of the first order differential maximum in the 490-530 nm band of the canopy spectrum.
Dy 黄边幅值
Yellow edge amplitude		 冠层光谱560~640 nm波段内一阶微分的最大值。
Maximum value of the first-order differential in the 560-640 nm band of the canopy spectrum.
λy 黄边位置
Yellow edge location		 冠层光谱560~640 nm波段内一阶微分最大值对应波长的位置。
Location of the first order differential maximum in the 560-640 nm band of the canopy spectrum.
Dr 红边幅值
Red edge amplitude		 冠层光谱680~760 nm波段内一阶微分的最大值。
Maximum value of the first order differential in the 680-760 nm band of the canopy spectrum.
λr 红边位置
Red edge location		 冠层光谱680~760 nm波段内一阶微分最大值对应波长的位置。
Location of the first order differential maximum in the 680-760 nm band of the canopy spectrum.
SDb 蓝边面积
Blue edge area		 蓝边反射率一阶微分曲线围成的面积。
Area enclosed by the first order differential curve of blue edge reflectance.
SDy 黄边面积
Yellow edge area		 黄边范围内一阶微分波所包围的面积。
Area enclosed by the first order differential curve of yellow edge reflectance.
SDr 红边面积
Red edge area		 红边范围内一阶微分波所包围的面积。
Area enclosed by the first order differential curve of red edge reflectance.
SDr/SDb 红边与蓝边面积的比值。
Ratio of the red edge area to the blue edge area.
SDr/SDy 红边与黄边面积的比值。
Ratio of the red edge area to the yellow edge area.
(SDr-SDb)/ (SDr+SDb) (红边面积-蓝边面积)/(红边面积+蓝边面积)。
(Red edge area-Blue edge area)/(Red edge area + Blue edge area).
(SDr-SDy)/ (SDr+SDy) (红边面积-黄边面积)/(红边面积+黄边面积)。
(Red edge area-Yellow edge area)/(Red edge area+Yellow edge area).

Fig. 1

Effects of different concentrations of CO2 on aboveground biomass of winter wheat ACO2: atmospheric carbon dioxide concentration, ECO2: elevated carbon dioxide concentration. The error line represents the standard deviation. “NS” indicates no significantly different among the treatments in the same stage at P < 0.05. “*”indicates significantly different among the treatments in the same stage at P < 0.05."

Fig. 2

Canopy reflectance of winter wheat during the growth season under different CO2 concentrations"

Table 3

Table of spectral characteristics parameters of winter wheat at different CO2 concentrations"

光谱特征参数
Spectral characteristics parameter		 CO2 拔节期
Jointing		 开花期
Anthesis		 灌浆期
Grain filling
AR ACO2 16.17±2.77 16.25±1.68 16.76±1.66
ECO2 18.05±2.41* 15.53±1.24 17.20±1.85
Rg ACO2 5.62±1.06 3.72±0.30 6.53±1.16
ECO2 6.23±1.11 3.82±0.76 7.09±1.85
λg ACO2 552.92±1.67 552.50±0.52 558.08±2.11
ECO2 550.58±2.40 552.58±0.67 558.58±1.88
Db ACO2 0.10±0.02 0.08±0.01 0.09±0.01
ECO2 0.13±0.05* 0.08±0.01 0.09±0.02
λb ACO2 521.92±0.51 521.42±0.51 519.00±0.60
ECO2 522.33±0.78 523.00±0.74* 520.42±0.51*
Dy ACO2 -0.07±0.01 -0.05±0.01 -0.03±0.02
ECO2 -0.07±0.01 -0.05±0.01 -0.02±0.03
λy ACO2 568.33±0.98 568.83±0.39 592.08±2.92*
ECO2 568.25±1.29 568.58±0.67 581.83±1.80
Dr ACO2 0.69±0.16 0.96±0.11 0.59±0.12*
ECO2 0.76±0.16 0.87±0.09 0.46±0.10
λr ACO2 729.25±0.75 734.92±0.67 727.17±0.94
ECO2 728.67±0.65 736.50±0.67* 729.92±0.79*
SDb ACO2 2.14±0.38 1.55±0.21 2.34±0.38
ECO2 2.43±0.46 1.59±0.31 2.44±0.55
SDy ACO2 1.88±0.40 1.63±0.26 0.97±0.35
ECO2 2.02±0.50 1.61±0.22 1.08±0.41
SDr ACO2 29.10±1.44 39.08±1.67* 24.44±1.79*
ECO2 32.68±1.61* 35.90±1.26 22.59±1.95
SDr/SDb ACO2 13.70±2.72 25.30±2.07 10.67±2.68
ECO2 13.63±2.75 23.34±5.03 9.66±2.83
SDr/SDy ACO2 15.49±1.43 24.11±1.92 26.68±4.75
ECO2 16.55±2.18 22.66±3.49 23.39±8.22

Table 4

Descriptive statistical analysis of partial least squares regression of above-ground biomass of winter wheat"

CO2 训练组 Train group 测试组Test group
NComps CV Adj CV X-Var (%) Y-Var (%) R2 Adj R2 RMSE P
ACO2 6 0.289 0.285 99.09 98.16 0.939 0.933 217.3 <0.01
ECO2 6 0.472 0.466 99.11 98.74 0.894 0.877 115.4 <0.01

Fig. 3

Significant spectral bands of above-ground biomass of winter wheat based on two methods"

Table 5

Descriptive statistical analysis of successive projections algorithm + stepwise multiple linear regression for aboveground biomass of winter wheat"

组
Group		 CO2 样品数
Sample number		 敏感光谱波段
Sensitive spectral bands		 R2 Adj R2 RMSE Max VIF P
训练组Train ACO2 24 R682, R727, R771, R985, R1078, R1083, R1100 0.967 0.953 175.8 20.755 <0.01
ECO2 24 R678, R711, R757, R972, R1060 0.855 0.815 302.8 12.958 <0.01
测试组
Test		 ACO2 12 0.932 0.925 174.0 <0.01
ECO2 12 0.806 0.787 243.3 <0.01

Fig. 4

Correlation coefficients between spectral parameters and above-ground biomass * indicate significant difference at the 0.05 probability level."

Table 6

Stepwise multiple linear regression model for above-ground biomass"

回归方式
Regression method		 分组
Groupings		 自变量
Independent variable		 回归方程
Regression equation		 R2 Adj R2 RMSE Max vif P
CR+SMLR 训练组Train 光谱参数
Spectral parameters		 y = 387.1+27619.5R799'-7360.8Dy
-302.8SDy-5437PRI		 0.866 0.854 287.2 3.65 <0.01
测试组
Test		 测量值
Measure		 y = 1.0194x-129.81 0.897 0.865 238.6 <0.01
SPA+SMLR 训练组
Train		 光谱反射率
Spectral
reflectance		 y = 490.48+1258.94R599-1078.41R677
-237.54R736-194.43R781+194.87R1078+146.82R1083		 0.841 0.818 320.3 49.29 <0.01
测试组
Test		 测量值
Measure		 y = 0.873x+245.764 0.838 0.830 285.7 <0.01

Fig. 5

Fitting relationships between the training and test groups of the PLSR model for aboveground biomass of winter wheat a, b: the above-ground biomass partial least squares regression model based on spectral reflectance as the independent variable, with a as the training group and b as the test group; c, d: the above-ground biomass partial least squares regression model based on spectral parameters as the independent variable, with c as the training group and d as the test group; ncomps: the number of components; Y-Var: the cumulative variance explained percentage of dependent variables."

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doi: 10.1002/agj2.v112.5
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