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作物学报 ›› 2024, Vol. 50 ›› Issue (4): 991-1003.doi: 10.3724/SP.J.1006.2024.31041

所属专题： 小麦：耕作栽培·生理生化

• 耕作栽培·生理生化 • 上一篇    下一篇

大气CO2浓度升高背景下冬小麦冠层光谱特征和地上生物量估算

黄宏胜(), 张馨月, 居辉, 韩雪()   

  1. 中国农业科学院农业环境与可持续发展研究所, 北京 100081
  • 收稿日期:2023-06-25 接受日期:2023-10-23 出版日期:2024-04-12 网络出版日期:2023-11-16
  • 通讯作者: * 韩雪, E-mail: hanxue@caas.cn
  • 作者简介:E-mail: 82101215240@caas.cn
  • 基金资助:
    国家重点研发计划项目(2019YFA0607403);中国农业科学院科技创新工程农业绿色低碳科学中心专项项目和中央级公益性科研院所基本科研业务费(CAAS-CSGLCA-202301);中国农业科学院科技创新工程农业绿色低碳科学中心专项项目和中央级公益性科研院所基本科研业务费(BSRF202202)

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 Published:2024-04-12 Published online: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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摘要：

本研究旨在探究大气CO2浓度升高对冬小麦全生育时期冠层光谱特征的影响, 并基于筛选的敏感波段建立地上生物量(AGB)与光谱参数的定量关系。为此, 在2021—2022年的冬小麦生长季, 利用开放式CO2富集系统(Mini-FACE), 设定大气CO2浓度(ACO2, (420±20) μL L-1)和高CO2浓度(ECO2, (550±20) μL L-1)两个处理水平, 分析了高CO2浓度下光谱特征变化, 基于连续投影算法(SPA)、逐步多元线性回归(SMLR)和偏最小二乘法回归(PLSR)筛选AGB敏感波段并构建估算模型。结果表明: CO2浓度升高使冬小麦拔节期和开花期AGB显著增加。红边和近红边反射率及红边面积在拔节期增加, 在开花期和灌浆期降低, 蓝边、黄边和红边位置在不同生育时期均发生移动; AGB的敏感光谱波段主要分布在红边和近红边区域, CO2浓度升高缩小了AGB敏感波段范围, 但不影响AGB的估算; AGB的SMLR和PLSR模型均取得了较高的估算精度(R2>0.8), 其中SMLR模型中的R799′、Dy、SDy和PRI等特征参数与AGB显著相关, R2为0.866。PLSR模型(R2>0.9)在估算精度和稳定性上优于SMLR模型。本研究可为未来高CO2浓度下冬小麦生长发育的遥感监测提供理论基础和技术方法。

关键词: CO2浓度升高, 冬小麦, 地上生物量, 冠层光谱特征, 回归分析

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

表1

植被指数的定义及计算公式"

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]

表2

光谱特征参数的定义及说明"

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).

图1

不同浓度CO2对冬小麦地上生物量的影响 ACO2表示大气二氧化碳浓度, ECO2表示升高二氧化碳浓度。误差线表示标准差。“NS”表示在同一生育时期不同处理间差异未达到显著水平(P > 0.05)。“*”表示在同一生育时期不同处理间差异达到显著水平(P < 0.05)"

图2

不同CO2浓度下冬小麦全生育期冠层光谱反射率"

表3

不同CO2浓度下冬小麦光谱特征参数表"

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

表4

冬小麦地上生物量偏最小二乘法回归描述统计分析"

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

图3

基于两种方法下的冬小麦地上生物量重要的敏感光谱波段"

表5

冬小麦地上生物量连续投影算法+逐步多元线性回归描述统计分析"

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

图4

光谱参数与地上生物量的相关性系数 *表示在0.05概率水平差异显著。"

表6

地上生物量逐步多元线性回归模型"

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

图5

冬小麦地上生物量PLSR模型训练组和测试组拟合关系 a, b: 基于光谱反射率为自变量的地上生物量偏最小二乘法回归模型, a为训练组, b为测试组; c, d: 基于光谱参数为自变量的地上生物量偏最小二乘法回归模型, c为训练组, d为测试组; ncomps: 最佳成分个数; Y-Var: 因变量累积方差解释百分数。"

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