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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)

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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
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