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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (2): 485-496.doi: 10.3724/SP.J.1006.2023.21005

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

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 Online:2022-08-22 Published:2022-08-22
  • Contact: CHENG Tao E-mail:wangxue@njau.edu.cn;tcheng@njau.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41871259)

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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

Fig. 1

Map of plot distribution V, D, and N represent variety, density, and nitrogen treatments, respectively."

Fig. 2

Experimental setup for the acquisition of leaf spectral by (A) the integrating sphere and (B) the leaf clip a: a leaf holder; b: a spectrometer; c, d: represent the light sources for the integrating sphere and leaf clip; e: the standard white plate, respectively. FOV is the field of view for the bara fiber optics of the spectrometer. Adapted from Li et al.[3]."

Table 1

Four vegetation indices commonly used"

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]

Fig. 3

Difference of reflectance in BRF and DHRF spectra A: the relationships of mean reflectance derived from BRF and DHRF spectra; B: the changes of different reflectance between BRF and DHRF spectra with various leaf chlorophyll content."

Table 2

t-test between BRF and DHRF spectra derived from different LCC and nitrogen applications"

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

Table 3

Paired t-test between BRF and DHRF wavelet coefficient derived from different nitrogen applications"

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

Fig. 4

Differences of different vegetation indices from the reflectance derived from BRF and DHRF Different lowercase indicate that there are significant difference at P < 0.05."

Fig. 5

Relationships of different vegetation indices from the reflectance obtained by BRF and DHRF **: P < 0.01."

Fig. 6

Relationships of different wavelet coefficient derived from BRF and DHRF spectra A: the difference of wavelet coefficient; B: the correction of wavelet coefficient. **: P < 0.01."

Fig. 7

Relationships between LCC and vegetation indices derived from BRF and DHRF spectra"

Fig. 8

Relationships between LCC and wavelet coefficient computed by BRF and DHRF spectra A: all wavelet coefficient at the fourth scale; B: the wavelet coefficient with 720 band at the fourth scale; C: the wavelet coefficient with 765 band at the fourth scale."

Table 4

Validation of LCC model based on NDRE and WF (4, 765) derived from BRF and DHRF spectra with different nitrogen applications"

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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