## 4 Discussion  
### Estimating WOR AGB Using the Seven Commonly Used VIs  
In this study, different nitrogen rates in WOR experiments were conducted in Wuxue city and Shayang city, Hubei province, central China from 2014 to 2016 to create different biomass condition. Canopy hyperspectral data and AGB samples were collected at four growth stages from seeding through pod stage. The linear and non-linear statistical models that simulated the quantitative relation between the seven commonly used VIs (CIred edge, Clgreen, NDVI, GNDVI, RVI, DVI, and SAVI) and AGB at different growth stages.  
Through comparing the accuracy of models (Table 5), we could see that the growth stage had a strong influence on the sensitivity to different wavelengths and performance of VIs for estimating AGB [49; 50]. The non-linear models based on the CIred edge showed excellent performance, with higher accuracy (R\({}^{2}\)cal ranged from 0.75-0.90, R\({}^{2}\)val ranged from 0.60-0.95, respectively) when compared to the other six VIs at the seeding and bolting stage. However, CIred edge performed significantly worse (R\({}^{2}\)cal ranged from 0.51-0.60, R\({}^{2}\)val ranged from 0.57-0.73, respectively) at the flowering and pod stage. The CIred edge was effective in estimating AGB at the vegetative stage, while it had relatively lower precision in the reproductive stage (Table 7). Additionally, the scatterplots also showed that, in the reproductive stage, the samples were distributed away from the regression curve (Figure 7d).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Growth Period**} & **Model** & \multirow{2}{*}{**Vegegation Index**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**RMSEC**} & \multirow{2}{*}{**R\({}_{\rm val}\)\({}^{2}\)**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**RMSEV**} & \multirow{2}{*}{**RPD**} \\  & **Number** & & & & & & & \\ \hline \multirow{2}{*}{**Vegative stage**} & 33 & NDVI (800, 1200) & Y = 165728.69\(\chi^{2}-10401.76\times 391.91\) & 0.71 & 702.88 & 0.52 & 889.44 & 1.45 \\  & 34 & CIred edge & Y = 15403.76\(\chi^{2}-6617.28\times 1101.59\) & 0.84 & 525.35 & 0.75 & 643.48 & 2.01 \\ \hline \multirow{2}{*}{**Reproductive stage**} & 35 & NDVI (800, 1200) & Y = 467.84e\({}^{11.51\times}\) & 0.73 & 2226.83 & 0.72 & 2231.90 & 1.85 \\  & 36 & CIred edge & Y = 20042.96\(\chi^{2}+7467.16\times 377.43\) & 0.46 & 3159.01 & 0.48 & 2938.05 & 1.41 \\ \hline \multirow{2}{*}{**Total for four stages**} & 37 & NDVI (800, 1200) & Y = 197455.98\(\chi^{2}-17815.49\times 779.08\) & 0.83 & 1521.84 & 0.82 & 1501.34 & 2.37 \\  & 38 & CIrededge & Y = \(-5472.86\times 12188.17\times 77.08\) & 0.08 & 3507.79 & 0.06 & 3447.41 & 1.03 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Regression modeling analysis between NDVI (800, 1200), CIred edge, and AGB.  
Leaves dominated WOR AGB in the vegetative stage, while the stem, flowers, and pod biomass contributed the most of AGB at reproductive stage [39; 51]. As Figure 3 showed that flowers affected the canopy spectral reflectance, with visible bands (500-730 nm) increasing significantly and NIR bands (800-1300 nm) decreasing slightly. Clred edge is calculated by the red-edge band (725 nm) and NIR band (800 nm), which relies primarily on the sensitivity of red edge bands to leaf chlorophyll absorption [52], the red-edge region could provide a possible way to avoid the spectral saturation for vegetation with moderate to high density [29]. Therefore, the Clred edge could effectively estimate AGB when WOR were in a period of vigorous green leaves [53], but it was not suitable for estimating AGB at the reproductive stage.