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

doi:10.3724/SP.J.1006.2024.31041

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

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()

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()

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)

摘要/Abstract

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′、D_y、SD_y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 CO₂concentration (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', D_y, SD_y, 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

黄宏胜, 张馨月, 居辉, 韩雪. 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[J]. 作物学报, 2024, 50(4): 991-1003.

HUANG Hong-Sheng, ZHANG Xin-Yue, JU Hui, HAN Xue. Spectral characteristics of winter wheat canopy and estimation of aboveground biomass under elevated atmospheric CO₂ concentration[J]. Acta Agronomica Sinica, 2024, 50(4): 991-1003.

图/表 11

表1

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
R_x				在x nm处的冠层光谱反射率。 Canopy spectral reflectance at x nm.
R_x'				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.
R_g	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.
λ_g	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.
D_b	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.
λ_b	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.
D_y	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.
λ_y	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.
D_r	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.
λ_r		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.
SD_b		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.
SD_y		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.
SD_r		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.
SD_r/SD_b			红边与蓝边面积的比值。 Ratio of the red edge area to the blue edge area.
SD_r/SD_y			红边与黄边面积的比值。 Ratio of the red edge area to the yellow edge area.
(SD_r-SD_b)/ (SD_r+SD_b)			(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).
(SD_r-SD_y)/ (SD_r+SD_y)			(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).

表2

图1

图2

表3

表4

图3

表5

图4

表6

图5

参考文献 41

[1]	王卓妮, 袁佳双, 庞博, 黄磊. IPCC AR6《气候变化2022: 减缓气候变化》主要结论和启示. 气候变化研究进展, 2022, 18: 531-537.
	Wang Z N, Yuan J S, Pang B, Huang L. The interpretation and highlights of IPCC AR6 WGIII report climate change 2022:mitigation of climate change. Climate Chang Res, 2022, 18: 531-537. (in Chinese with English abstract)
[2]	Ainsworth E A, Long S P. What have we learned from 15 years of free air CO₂ enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO₂. New Phytol, 2005, 165: 351-371. doi: 10.1111/nph.2005.165.issue-2
[3]	Sinha P G, Kapoor R, Uprety D C, Bhatnagar A K. Impact of elevated CO₂ concentration on ultrastructure of pericarp and composition of grain in three Triticum species of different ploidy levels. Environ Exp Bot, 2009, 66: 451-456. doi: 10.1016/j.envexpbot.2009.04.006
[4]	Li B, Xu X, Zhang L, Han J, Bian C, Li G, Liu J, Jin L. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS J Photogramm Remote Sens, 2020, 162: 161-172. doi: 10.1016/j.isprsjprs.2020.02.013
[5]	Miller G J, Morris J T, Wang C. Estimating aboveground biomass and its spatial distribution in coastal wetlands utilizing planet multispectral imagery. Remote Sens, 2019, 11: 1-16. doi: 10.3390/rs11010001
[6]	Tian H, Shi S, Wang H, Li F, Li Z, Alva A, Zhang Z. Estimation of sugar beet aboveground biomass by band depth optimization of hyperspectral canopy reflectance. J Indian Soc Remote Sens, 2016, 45: 795-803. doi: 10.1007/s12524-016-0632-z
[7]	Xie Y, Wang C, Yang W, Feng M, Qiao X, Song J. Canopy hyperspectral characteristics and yield estimation of winter wheat (Triticum aestivum) under low temperature injury. Sci Rep, 2020, 10: 244-254. doi: 10.1038/s41598-019-57100-8 pmid: 31937859
[8]	Dawson T P, Curran P J. A new technique for interpolating the reflectance red edge position. Int J Remote Sens, 1998, 1: 2133-2139.
[9]	郑红平, 邹红玉. 浅述植被“红边”效应及其定量分析方法. 遥感信息, 2010, 25(4): 112-116.
	Zheng H P, Zou H Y. The effect and method of quantitative analysis of “Red Edge” of vegetation. Remote Sens Inf, 2010, 25(4): 112-116. (in Chinese with English abstract)
[10]	蔡瑶, 缪宇轩, 吴浩, 王丹. 高CO₂浓度下冬小麦的高光谱特征及其与叶面积指数和SPAD值的反演. 浙江农业学报, 2022, 34: 582-589. doi: 10.3969/j.issn.1004-1524.2022.03.19
	Cai Y, Miao Y X, Wu H, Wang D. Hyperspectral characteristics and leaf area index (LAI) and SPAD value inversion of winter wheat under elevated CO₂ concentration. Acta Agric Zhejiangensis, 2022, 34: 582-589. (in Chinese with English abstract)
[11]	杨璐璐, 华开, 张学霞. 不同CO₂浓度及干旱胁迫下高羊茅的生理响应和光谱特征. 中国草地学报, 2014, 36(4): 72-78.
	Yang L L, Hua K, Zhang X X. The impacts of mowing on photosynthesis and water physiological conditions of Ceratoides latens. Chin J Grassland, 2014, 36(4): 72-78. (in Chinese with English abstract)
[12]	Sun Q, Gu X, Sun L, Yang G, Zhou L, Guo W. Dynamic change in rice leaf area index and spectral response under flooding stress. Paddy Water Environ, 2020, 18: 223-233. doi: 10.1007/s10333-019-00776-5
[13]	Ren P, Feng M C, Yang W D, Wang C, Liu T T, Wang H Q. Response of winter wheat (Triticum aestivum L.) hyperspectral characteristics to low temperature stress. Spectr Spect Anal, 2014, 34: 2490-2494.
[14]	Xu D Q, Liu X L, Wang W, Chen M, Kan H C, Li C F, Zheng S F. Hyper spectral characteristics and estimation model of leaf chlorophyll content in cotton under waterlogging stress. J Appl Ecol, 2017, 28: 3289-3296.
[15]	Tschannerl J, Ren J, Yuen P, Sun G, Zhao H, Yang Z, Wang Z, Marshall S. MIMR-DGSA: unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm. Inf Fusion, 2019, 51: 189-200. doi: 10.1016/j.inffus.2019.02.005
[16]	Steve D B, Pieter K, Paul S. Walter D. A band selection technique for spectral classification. IEEE Geosci Remote Sens Lett, 2005, 2: 319-323. doi: 10.1109/LGRS.2005.848511
[17]	Gao H Z, Lu Q P, Ding H Q, Peng Z Q. Choice of characteristic near-infrared wavelengths for soil total nitrogen based on successive projection algorithm. Spectr Spect Anal, 2009, 29: 2951-2954.
[18]	Liu J, Xie J, Meng T, Dong H. Organic matter estimation of surface soil using successive projection algorithm. Agron J, 2022, 114: 1944-1951. doi: 10.1002/agj2.v114.4
[19]	Xie Y, Feng M, Wang C, Yang W, Sun H, Yang C, Jing B, Qiao X, Saleem K M, Song J. Hyperspectral monitor on chlorophyll density in winter wheat under water stress. Agron J, 2020, 112: 3667-3676. doi: 10.1002/agj2.v112.5
[20]	El-Hendawy S, Al-Suhaibani N, Alotaibi M, Hassan W, Elsayed S, Tahir M U, Mohamed A I, Schmidhalter U. Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis. Sci Rep, 2019, 9: 16473. doi: 10.1038/s41598-019-52802-5 pmid: 31712701
[21]	Gitelson A A, Merzlyak M N, Chivkunova O B. Optical properties and non-destructive estimation of anthocyanin content in plant leaves? Photochem Photobiol, 2001, 74: 38-45. doi: 10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2 pmid: 11460535
[22]	Merzlyak M N, Solovchenko A E, Gitelson A A. Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biol Technol, 2003, 27: 197-211. doi: 10.1016/S0925-5214(02)00066-2
[23]	Kaufman Y J, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens, 1992, 30: 261-270. doi: 10.1109/36.134076
[24]	Huete A R, Liu H Q, Batchily K, van Leeuwen W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens Environ, 1997, 59: 440-451. doi: 10.1016/S0034-4257(96)00112-5
[25]	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
[26]	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. doi: 10.1016/0034-4257(92)90059-S
[27]	Gitelson A, Merzlyak M N. Spectral reflectance changes associated 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. doi: 10.1016/S0176-1617(11)81633-0
[28]	Schlerf M, Atzberger C, Hill J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens Environ, 2005, 95: 177-194. doi: 10.1016/j.rse.2004.12.016
[29]	张华东, 阮陆宁. 偏最小二乘回归在R软件中的实现及其优缺点剖析. 科技广场, 2015, (11): 12-17.
	Zhang H D, Ruan L N. Realization and advantages and disadvantages analysis of partial least-squares regression in the R software. Sci Mos, 2015, (11): 12-17. (in Chinese with English abstract)
[30]	王石言, 王力, 张静, 张林森. 黄土旱塬主要农林用地土壤水文特征对比. 中国水土保持科学, 2016, 14(3): 10-18.
	Wang S Y, Wang L, Zhang J, Zhang L S. Comparison of soil hydrological characteristics for main cropland and orchard in dry highland of the Loess Tableland. Sci Soil Water Conserv, 2016, 14(3): 10-18. (in Chinese with English abstract)
[31]	Xie Y, Feng M, Wang C, Yang W, Sun H, Yang C, Jing B, Qiao X, Saleem Kubar M, Song J. Hyperspectral monitor on chlorophyll density in winter wheat under water stress. Agron J, 2020, 112: 3667-3676. doi: 10.1002/agj2.v112.5
[32]	Liu J, Xie J, Meng T, Dong H. Organic matter estimation of surface soil using successive projection algorithm. Agron J, 2022, 114: 1944-1951. doi: 10.1002/agj2.v114.4
[33]	Högy P, Kottmann L, Schmid I, Fangmeier A. Heat, wheat and CO₂: the relevance of timing and the mode of temperature stress on biomass and yield. J Agron Crop Sci, 2019, 205: 608-615. doi: 10.1111/jac.v205.6
[34]	Högy P, Brunnbauer M, Koehler P, Schwadorf K, Breuer J, Franzaring J, Zhunusbayeva D, Fangmeier A. Grain quality characteristics of spring wheat (Triticum aestivum) as affected by free-air CO₂ enrichment. Environ Exp Bot, 2013, 88: 11-18. doi: 10.1016/j.envexpbot.2011.12.007
[35]	Liu C, Hu Z H, Islam A, Kong R, Yu L F, Wang Y Y. Hyperspectral characteristics and inversion model estimation of winter wheat under different elevated CO₂ concentrations. Int J Remote Sens, 2021, 42: 1035-1053. doi: 10.1080/01431161.2020.1823038
[36]	杨熙来, 朱榴骏, 冯兆忠. 臭氧胁迫下冬小麦叶片高光谱特征和叶绿素含量估算. 生态学报, 2023, 43: 3213-3223.
	Yang X L, Zhu L J, Feng Z Z. Hyperspectral characteristics and chlorophyll content estimation of winter wheat under ozone stress. Acta Ecol Sin, 2023, 43: 3213-3223. (in Chinese with English abstract)
[37]	Bandyopadhyay K K, Pradhan S, Sahoo R N, Singh R, Gupta V K, Joshi D K, Sutradhar A K. Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices. Agric Water Manag, 2014, 146: 115-123. doi: 10.1016/j.agwat.2014.07.017
[38]	Zhang C, Ren H, Dai X, Qin Q, Li J, Zhang T, Sun Y. Spectral characteristics of copper stressed vegetation leaves and further understanding of the copper stress vegetation index. Int J Remote Sens, 2019, 40: 4473-4488. doi: 10.1080/01431161.2018.1563842
[39]	Estrada F, Flexas J, Araus J L, Mora P F, Gonzalez T J, Castillo D, Matus I A, Méndez E A M, Garriga M, Araya R C, Douthe C, Castillo B, Del P A, Lobos G A. Exploring plant responses to abiotic stress by contrasting spectral signature changes. Front Plant Sci, 2023, 13: 1-17.
[40]	Ohsowski B M, Dunfield K E, Klironomos J N, Hart M M. Improving plant biomass estimation in the field using partial least squares regression and ridge regression. Botany, 2016, 94: 501-508. doi: 10.1139/cjb-2016-0009
[41]	Xie Y, Feng M, Wang C, Yang W, Sun H, Yang C, Jing B, Qiao X, Saleem K M, Song J. Hyperspectral monitor on chlorophyll density in winter wheat under water stress. Agron J, 2020, 112: 3667-3676. doi: 10.1002/agj2.v112.5

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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]

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
R_g	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
λ_g	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
D_b	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
λ_b	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^*
D_y	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
λ_y	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
D_r	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
λ_r	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^*
SD_b	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
SD_y	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
SD_r	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
SD_r/SD_b	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
SD_r/SD_y	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

CO₂	训练组 Train group					测试组Test group
CO₂	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

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
训练组Train	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
测试组 Test	ECO₂	12		0.806	0.787	243.3		<0.01

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'-7360.8D_y-302.8SD_y-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

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

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

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