欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2023, Vol. 49 ›› Issue (2): 485-496.doi: 10.3724/SP.J.1006.2023.21005

• 耕作栽培·生理生化 • 上一篇    下一篇

二向反射和方向半球反射光谱差异及其对小麦叶片叶绿素含量反演的影响

王雪(), 王文辉, 李栋, 姚霞, 朱艳, 曹卫星, 程涛()   

  1. 南京农业大学国家信息农业工程技术中心 / 江苏省信息农业重点实验室 / 农业农村部农作物系统分析与决策重点实验室 / 智慧农业教育部工程研究中心 / 现代作物生产省部共建协同创新中心, 江苏南京 210095
  • 收稿日期:2022-01-24 接受日期:2022-07-21 出版日期:2022-08-22 网络出版日期:2022-08-22
  • 通讯作者: 程涛
  • 作者简介:E-mail: wangxue@njau.edu.cn
  • 基金资助:
    国家自然科学基金项目(41871259)

Difference between bidirectional reflectance factor and directional-hemispherical reflectance factor spectra and its effect on the estimation of leaf chlorophyll content in wheat

WANG Xue(), WANG Wen-Hui, LI Dong, YAO Xia, ZHU Yan, CAO Wei-Xing, CHENG Tao()   

  1. National Engineering and Technology Center for Information Agriculture / Jiangsu Provincial Key Laboratory of Information Agriculture / Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs / Engineering Research Center for Intelligent Agriculture, Ministry of Education / Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing 210095, Jiangsu, China
  • Received:2022-01-24 Accepted:2022-07-21 Published:2022-08-22 Published online:2022-08-22
  • Contact: CHENG Tao
  • Supported by:
    National Natural Science Foundation of China(41871259)

RichHTML

7

PDF

818

摘要：

二向反射率因子(bidirectional reflectance factor, BRF)和方向半球反射率因子(directional-hemispherical reflectance factor, DHRF)是反射光谱的两种形式, 但在生化参数监测过程中, 多数研究忽略了BRF和DHRF光谱的差异及其对叶片叶绿素含量(leaf chlorophyll content, LCC)反演的影响。本研究以不同品种、密度及氮素处理的小麦田间小区试验为基础, 在叶片尺度获取了BRF和DHRF光谱, 并计算相应的植被指数和小波系数, 建立了基于植被指数和小波系数的LCC监测模型, 定量分析BRF和DHRF光谱、植被指数和小波系数的差异及其对LCC反演的影响。结果表明, 1) BRF和DHRF随LCC变化趋势一致, 但两种光谱存在显著差异, 且BRF光谱值高于DHRF光谱值; 2) 在一定程度上, 应用植被指数和小波系数均可消除BRF和DHRF光谱差异的影响, 其中归一化红边植被指数(normalized differential red edge vegetation index, NDRE)和红边叶绿素指数(red edge chlorophyll index, CIred-edge)可以消除BRF和DHRF光谱差异的影响(R2=0.930), 但小波系数的性能要优于植被指数(R2=0.995); 3) 基于DHRF光谱的植被指数和小波系数对LCC的估测能力优于BRF光谱, 所有植被指数中NDRE反演效果最好(DHRF: R2=0.957; BRF: R2=0.938; All: R2=0.892), 第4尺度765 nm处的小波系数WF (4, 675)反演LCC的效果优于NDRE (DHRF: R2=0.985; BRF: R2=0.971; All: R2=0.973), 且WF (4, 675)消除BRF和DHRF光谱差异对LCC反演的影响能力强于NDRE (WF(4, 675): R2=0.973; NDRE: R2=0.892)。综上所述, BRF和DHRF光谱存在差异, 且这种差异不能直接忽略。研究明确了BRF和DHRF光谱的差异, 为构建基于BRF和DHRF光谱的统一模型及提升冠层尺度LCC精确反演提供理论基础。

关键词: 小麦, 二向反射, 方向半球反射, 叶片叶绿素含量, 植被指数, 小波系数

Abstract:

Bidirectional reflectance factor (BRF) and directional-hemispherical reflectance factor (DHRF) spectra are two common types of reflectance measurements. However, most studies ignored the differences between BRF and DHRF spectra and their effects on the estimation of leaf chlorophyll content (LCC) while monitoring the biochemical parameters of crops. In this study, we collected leaf-level data from field trials of winter wheat with different varieties, densities, and nitrogen rates. We calculated the vegetation indices (VIs) and wavelet coefficients (WCs) based on BRF and DHRF spectra and then established their relationships with LCC. Finally, we evaluated the ability of VIs and WCs in reducing the differences between BRF and DHRF spectra, and their effects on LCC estimation. Results were as follows: (1) changes in BRF and DHRF were consistent with the variation of LCC, but there were significant differences between the two types of spectra, and the reflectance in BRF was higher than that in DHRF. (2) To some extent, the use of either VIs or WCs could eliminate the influence of the differences between BRF and DHRF spectra. For example, the Normalized Differential Red Edge Vegetation Index (NDRE) and the Red Edge Chlorophyll Index (CIred-edge) could reduce this effect (R2=0.930), but the performance of wavelet coefficients were better than those of NDRE and CIred-edge (R2=0.995). (3) The performance of VIs and WCs based on DHRF data was better than that of BRF data for LCC estimation. NDRE was the best among all VIs evaluated (DHRF: R2=0.957; BRF: R2=0.938; All: R2=0.892). Furthermore, the WC at the fourth scale of 765 nm [WF(4, 675)] was better than NDRE (DHRF: R2=0.985; BRF: R2=0.971; All: R2=0.973), and it had a stronger ability to eliminate the influence of the spectral differences on LCC estimation than NDRE (WF (4, 675): R2=0.973; NDRE: R2 = 0.892). In summary, there was a difference between BRF and DHRF data, and this difference could not be ignored directly. To some extent, the use of suitable VIs and WCs can eliminate the difference and improve the accuracy of LCC estimation. This study determined the differences between BRF and DHRF spectra of the same leaf samples of winter wheat, which provided a theoretical basis for establishing a unified model across BRF and DHRF spectra and improving the accurate estimation of LCC at the canopy level.

Key words: wheat, BRF, DHRF, leaf chlorophyll content, vegetation index, wavelet analysis

图1

试验小区分布图 V、D、N分别表示品种、密度、氮素处理。"

图2

叶片光谱测试示意图: (A)积分球、(B)叶片夹 a: 摆放叶片; b: 为光谱仪; c: 为积分球光源; d: 为叶片夹光源; e: 为标准白板。FOV代表光谱仪裸光纤视场角。改编自Li等[3]。"

表1

常用的4种植被指数"

植被指数 Vegetation index 公式 Formulation 参考文献 Reference
归一化植被指数 NDVI (R890−R695)/(R890+R695) Chen et al. [14]
归一化红边植被指数 NDRE (R790−R720)/(R790+R720) Barnes et al. [15]
红边叶绿素指数 CIred-edge R850/R730−1 Wang et al. [16]
叶面积指数不敏感叶绿素指数 LICI R735/R720−(R573−R680)/(R573+R680) Li et al. [6]

图3

BRF和DHRF光谱的差异 A: 积分球和叶片夹测得的平均反射率光谱; B: 叶片夹和积分球光谱的差值在不同叶绿素含量的变化规律。"

表2

不同LCC和氮梯度的BRF和DHRF光谱的成对t检验"

成对差分
Paired difference		 均值
Mean		 标准差
Standard deviation		 均值的标准误
Std error of mean		 t值
t-value		 置信度
Degree of confidence		 显著性
Significance
LCC=22 μg cm-2 0.050 0.012 0.001 81.660 450 0.000
LCC=32 μg cm-2 0.049 0.014 0.001 74.340 450 0.000
LCC=42 μg cm-2 0.038 0.011 0.001 72.770 450 0.000
LCC=48 μg cm-2 0.031 0.014 0.001 46.530 450 0.000
N0 0.050 0.018 0.001 59.290 450 0.000
N1 0.047 0.014 0.001 71.520 450 0.000
N2 0.044 0.014 0.001 66.050 450 0.000

表3

不同氮水平BRF和DHRF的小波系数成对t检验"

成对差分
Paired difference		 均值
Mean		 标准差
Standard deviation		 相关系数
Correlation coefficient		 t值
t-value		 置信度
DF		 显著性
Significance
N0 0.001 0.018 0.996** 0.000 450 1.00
N1 0.002 0.016 0.998** 0.251 450 0.802
N2 0.003 0.021 0.999** 0.307 450 0.759

图4

BRF和DHRF光谱的植被指数差异 不同小写字母为显著性差异(P < 0.05)。"

图5

BRF和DHRF构建的植被指数相关性"

图6

BRF和DHRF小波系数的关系 A: 小波系数差异; B: 小波系数相关性。"

图7

BRF和DHRF光谱构建的植被指数和LCC的关系"

图8

BRF和DHRF光谱构建的小波系数与LCC相关性 A: 所有波长; B: WF (4, 720); C: WF (4, 765)。"

表4

不同氮梯度BRF和DHRF构建的NDRE和WF (4, 765)在反演LCC中验证精度"

光谱
Spectra		 氮处理
Nitrogen treatment		 归一化红边植被指数 NDRE 第4尺度765波段的小波系数 WF (4, 765)
决定系数 R2 均方根误差 RMSE (μg cm-2) 决定系数 R2 均方根误差 RMSE (μg cm-2)
BRF N0 0.845** 2.630 0.951** 1.904
N1 0.896** 2.410 0.940** 1.829
N2 0.902** 2.401 0.963** 1.714
All 0.901** 2.041 0.971** 1.513
DHRF N0 0.869** 2.224 0.948** 1.458
N1 0.904** 2.004 0.952** 1.326
N2 0.924** 1.943 0.974** 1.223
All 0.926** 1.842 0.985** 1.099
[1] Lyu Z F, Liu X J, Cao W X, Zhu Y. Climate change impacts on regional winter wheat production in main wheat production regions of China. Agric For Meteorol, 2013, 171: 234-248.
[2] Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol, 2003, 160: 271-282.
doi: 10.1078/0176-1617-00887
[3] Li D, Tian L, Wan Z F, Jia M, Yao X, Tian Y C, Zhu Y, Cao W X, Cheng T. Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra. Remot Sens Environ, 2019, 231: 111240.
doi: 10.1016/j.rse.2019.111240
[4] Dong L, Cheng T, Zhou K, Zheng H B, Yao X, Tain Y C, Zhu Y, Cao W X. WREP: a wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS J Photogramm, 2017, 129: 103-117.
doi: 10.1016/j.isprsjprs.2017.04.024
[5] Gitelson A A. Remote estimation of canopy chlorophyll content in crops. Geophy Res Lett, 2005, 32: 8403.
[6] Li D, Chen J M, Zhang X, Yan Y, Zhu J, Zheng H B, Zhou K, Yao X, Tian Y C, Zhu Y, Cheng T, Cao W X. Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time. Remot Sens Environ, 2020, 248: 111985.
doi: 10.1016/j.rse.2020.111985
[7] Jay S, Maupas F, Bendoula R, Gorretta N. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Res, 2017, 210: 33-46.
doi: 10.1016/j.fcr.2017.05.005
[8] Gitelson A A, Merzlyak M N. Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens, 1997, 18: 2691-2697.
doi: 10.1080/014311697217558
[9] 童倩倩, 李莉婕, 赵泽英, 岳延滨, 刘海. 基于离散小波算法定量反演贵州百香果叶片叶绿素含量的研究. 西南农业学报, 2020, 33: 2927-2932.
Tong Q Q, Li L J, Zhao Z Y, Yue Y B, Liu H. Quantitative inversion of chlorophyll content in passiflora edulis leaves based on discrete wavelete algorithm in Guizhou province. Southest China J Agric Sci, 2020, 33: 2927-2932 (in Chinese with English abstract).
[10] Yoder B J, Pettigrew-Crosby R E. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remot Sens Environ, 1995, 53: 199-211.
doi: 10.1016/0034-4257(95)00135-N
[11] 王浩云, 曹雪莲, 孙云晓, 闫明壮, 王江波, 徐焕良. 基于光学特性参数反演的绿萝叶绿素含量估测研究. 农业机械学报, 2021, 52(3): 202-209.
Wang H Y, Cao X L, Sun Y X, Yan M Z, Wang J B, Xu H L. Estimation of chlorophyll content of epipremnum aureum based on optical characteristic parameter inversion. Trans CSAM, 2021, 52(3): 202-209. (in Chinese with English abstract)
[12] Ma Z G, Chen X, Wang Q, Li P H, Jiapaer G L. Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an application to Populus euphratica polymorphic leaves. J Arid Land, 2012, 4: 52-62.
doi: 10.3724/SP.J.1227.2012.00052
[13] Li P H, Wang Q. Retrieval of leaf biochemical parameters using PROSPECT inversion: a new approach for alleviating ill-posed problems. IEEE Trans Geosci Remot, 2011, 49: 2499-2506.
doi: 10.1109/TGRS.2011.2109390
[14] Chen J. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remot Sens Environ, 2004, 91: 332-344.
doi: 10.1016/j.rse.2004.03.014
[15] Barnes E M, Clarke T R, Richards S E, Colaizzi P D, Thompson T. Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground Based Multispectral Data. Proceedings of the 5th International Conference on Precision Agriculture and other resource management July 16-19, 2000, Bloomington, MN USA 2000.
[16] Wang W H, Wu Y P, Zhang Q F, Zheng H B, Yao X, Zhu Y, Cao W X, Cheng T. AAVI: a novel approach to estimating leaf nitrogen concentration in rice from unmanned aerial vehicle multispectral imagery at early and middle growth stages. IEEE J-STARS, 2021, 14: 6716-6728.
[17] Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Jacquemoud S, Ustin S L. Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS J Photogr, 2014, 87: 28-38.
doi: 10.1016/j.isprsjprs.2013.10.009
[18] Cheng T, Rivard B, Sánchez-Azofeifa A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens Environ, 2010, 115: 659-670.
doi: 10.1016/j.rse.2010.11.001
[19] Huang J F, Blackburn G A. Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data. Int J Remote Sens, 2011, 32: 9375-9396.
doi: 10.1080/01431161.2011.558130
[20] 李辰, 王延仓, 李旭青, 杨秀峰, 顾晓鹤. 基于小波技术的冬小麦植株组分含水率估测模型. 农业机械学报, 2021, 52(3): 193-201.
Li C, Wang Y C, Li X Q, Yang X F, Gu X H. Quantitative inversion of water content of plant components in winter wheat based on wavelet technology. Trans CSAM, 2021, 52(3): 193-201. (in Chinese with English abstract)
[21] 张骁, 闫岩, 王文辉, 郑恒彪, 姚霞, 朱艳, 程涛. 基于小波分析的水稻籽粒直链淀粉含量高光谱预测. 作物学报, 2021, 47: 1563-1580.
doi: 10.3724/SP.J.1006.2021.02063
Zhang X, Yan Y, Wang W H, Zheng H B, Yao X, Zhu Y, Cheng T. Application of continuous wavelet analysis to laboratory reflectance spectra for the prediction of grain amylose content in rice. Acta Agron Sin, 2021, 47: 1563-1580. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.02063
[22] Yan G, Bo D, Zhu R S, Wu X T, Ma Y, Peng Y. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis. Plant Methods, 2018, 14: 70.
doi: 10.1186/s13007-018-0338-z pmid: 30151031
[23] Croft H, Arabian J, Chen J M, Shang J L, Liu J G. Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery. Prec Agric, 2020, 21: 856-880.
doi: 10.1007/s11119-019-09698-y
[24] Zhang Y Q, Chen J M, Miller J R, Noland T L. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sens Environ, 2008, 112: 3234-3247.
doi: 10.1016/j.rse.2008.04.005
[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] Main R, Cho M A, Mathieu R, Kennedy M M O, Ramoelo A, Koch S. An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J Photogr, 2011, 66: 751-761.
doi: 10.1016/j.isprsjprs.2011.08.001
[27] Féret J B, François C, Gitelson A, Asner G P, Barry K M, Panigada C, Richardson A D, Jacquemoud S. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens Environ, 2011, 115: 2742-2750.
doi: 10.1016/j.rse.2011.06.016
[28] Maccioni A, Agati G, Mazzinghi P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol Biol, 2001, 61: 52-61.
doi: 10.1016/S1011-1344(01)00145-2
[29] Datt B. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. Int J Remote Sens, 1999, 20: 2741-2759.
doi: 10.1080/014311699211778
[30] Cheng T, Ria O D, Ustin S L. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sens Environ, 2014, 143: 39-53.
doi: 10.1016/j.rse.2013.11.018
[31] Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Ustin S L. Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. J Plant Physiol, 2012, 169: 1134-1142.
doi: 10.1016/j.jplph.2012.04.006
[32] Cheng T, Rivard B, Sánchez-Azofeifa G A, Feng J, Calvo-polanco M. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens Environ, 2010, 114: 899-910.
doi: 10.1016/j.rse.2009.12.005
[33] Wang Y Y, Zhang K, Tang C L, Cao Q, Tian Y C, Zhu Y, Cao W X, Liu X J. Estimation of rice growth parameters based on linear mixed-effect model using multispectral images from fixed-wing unmanned aerial vehicles. Remote Sens-based, 2019, 11: 1371.
[34] Li S Y, Liu X J, Tian Y C, Zhu Y, Cao Q. Comparison RGB digital camera with active canopy sensor based on UAV for rice nitrogen status monitoring. In: 2018 7th International Conference on Agro-geoinformatics, 2018. pp 1-6.
[35] Grohs D S, Bredemeier C, Mundstock C M, Poletto N. Model for yield potential estimation in wheat and barley using the GreenSeeker sensor. Eng Agr-jaboticabal, 2009, 29: 101-112.
[1] 杨斌, 乔玲, 赵佳佳, 武棒棒, 温宏伟, 张树伟, 郑兴卫, 郑军. 小麦旗叶叶绿素含量的QTL定位及验证[J]. 作物学报, 2023, 49(3): 744-754.
[2] 王雪, 谷淑波, 林祥, 王威雁, 张保军, 朱俊科, 王东. 微喷补灌水肥一体化对冬小麦产量及水分和氮素利用效率的影响[J]. 作物学报, 2023, 49(3): 784-794.
[3] 高春华, 冯波, 李国芳, 李宗新, 李升东, 曹芳, 慈文亮, 赵海军. 施氮量对花后高温胁迫下冬小麦籽粒淀粉合成的影响[J]. 作物学报, 2023, 49(3): 821-832.
[4] 刘晓颖, 张驰, 王雪晴, 杨晨晓, 王光钰, 卞云迪, 方芳, 王颖, 王振英. 栽培小麦Brock中抗白粉病相关基因TaRPP13-1B的克隆及功能分析[J]. 作物学报, 2023, 49(2): 392-401.
[5] 刘方方, 万映秀, 曹文昕, 李耀, 张琪琪, 李炎, 张平治. 小麦倒春寒抗性评价方法研究[J]. 作物学报, 2023, 49(2): 438-446.
[6] 孟雨, 田文仲, 温鹏飞, 丁志强, 张学品, 贺利, 段剑钊, 刘万代, 郭天财, 冯伟. 基于不同发育阶段协同的小麦品种抗旱性综合评判[J]. 作物学报, 2023, 49(2): 570-582.
[7] 张翔宇, 胡鑫慧, 谷淑波, 林祥, 殷复伟, 王东. 减氮条件下分期施钾对冬小麦籽粒产量和氮素利用效率的影响[J]. 作物学报, 2023, 49(2): 447-458.
[8] 李秀, 李刘龙, 李慕嵘, 尹立俊, 王小燕. 不同小麦品种旗叶叶绿素含量、叶片显微结构及产量对花后遮光的响应机制[J]. 作物学报, 2023, 49(1): 286-294.
[9] 陈嘉军, 林祥, 谷淑波, 王威雁, 张保军, 朱俊科, 王东. 花后叶面喷施尿素对冬小麦氮素吸收利用和产量的影响[J]. 作物学报, 2023, 49(1): 277-285.
[10] 王海琪, 王荣荣, 蒋桂英, 尹豪杰, 晏世杰, 车子强. 施氮量对滴灌春小麦叶片光合生理性状的影响[J]. 作物学报, 2023, 49(1): 211-224.
[11] 张一铎, 李国强, 孔忠新, 王玉泉, 李小利, 茹振钢, 贾海燕, 马正强. 基因聚合选育抗赤霉病小麦新品系百农4299[J]. 作物学报, 2022, 48(9): 2221-2227.
[12] 谭照国, 苑少华, 李艳梅, 白建芳, 岳洁茹, 刘子涵, 张天豹, 赵福永, 赵昌平, 许本波, 张胜全, 庞斌双, 张立平. 小麦TaPIP1基因克隆及其在花药开裂中潜在功能分析[J]. 作物学报, 2022, 48(9): 2242-2254.
[13] 冯子恒, 李晓, 段剑钊, 高飞, 贺利, 杨天聪, 戎亚思, 宋莉, 尹飞, 冯伟. 基于特征波段选择和机器学习的小麦白粉病高光谱遥感监测[J]. 作物学报, 2022, 48(9): 2300-2314.
[14] 曹际玲, 曾青, 朱建国. 不同品种小麦灌浆期旗叶光合特性及光合基因表达对臭氧浓度升高的响应[J]. 作物学报, 2022, 48(9): 2339-2350.
[15] 李永波, 崔德周, 黄琛, 隋新霞, 樊庆琦, 楚秀生. 高度特异性小麦ATG8抗体的研制及其在细胞自噬检测中的应用[J]. 作物学报, 2022, 48(9): 2390-2399.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2