## 3 Results
### Regression Modeling Analysis for AGB of Different Growth Stages Combination  
In this study, in combining WOR growth period, the growth stages of WOR could be divided into two stages: the vegetative stage (including seeding and bolting stages) and reproductive stage (including flowering and pod stages). The AGB estimation of different combined growth stages could test the robustness of NDVI (800, 1200) and the CIred edge.  
The results showed that the CIred edge was effective in estimating AGB at the vegetative stage, with the values of \(\mathrm{R^{2}_{cal}}\), \(\mathrm{R^{2}_{val}}\), RMSEC, RMSEV, and RPD being 0.84, 0.75, 525.35 kg/hm\({}^{2}\), 643.48 kg/hm\({}^{2}\), and 2.01. However, the inversion of the CIred edge and AGB showed a low coefficient of determination at the reproductive stage and the total for four stages, with the value of \(\mathrm{R^{2}_{cal}}\), \(\mathrm{R^{2}_{val}}\), RMSEC, RMSEV, and RPD being 0.08, 0.06, 3507.79 kg/hm\({}^{2}\), 3447.41 kg/hm\({}^{2}\), and 1.03 at the total for four stages.  
NDVI (800, 1200) had good robustness at the vegetative and reproductive stage, and it significantly improved the AGB estimation accuracy at the total for four stages; \(\mathrm{R^{2}_{cal}}\) ranged from 0.71 to 0.83, \(\mathrm{R^{2}_{val}}\) ranged from 0.52 to 0.82, RMSEC ranged from 702.88 to 2226.83 kg/hm\({}^{2}\), RMSEV ranged from  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Growth Period** & **Model** & **Vegetation Index** & **Model** & **R\({}_{val}\)2** & **RMSEC** & **R\({}_{val}\)2** & **RMSEV** & **RPD** \\ \hline
**Seeding stage** & 29 & NDVI (800, 1200) & \(\chi=16.4\sigma^{2}\)\({}^{1802}\) & 0.64 & 622.82 & 0.61 & 650.03 & 1.45 \\
**Bolting stage** & 30 & NDVI (800, 1200) & \(\chi=25314.87\chi^{2}-3866.43\chi^{2}+1223.7\) & 0.70 & 964.67 & 0.80 & 963.94 & 1.96 \\
**Hovering stage** & 31 & NDVI (800, 1200) & \(\chi=301878.73\chi^{2}+3179.73\chi^{2}+867.19\) & 0.67 & 1582.82 & 0.74 & 1351.36 & 1.99 \\
**Pod stage** & 32 & NDVI (800, 1200) & \(\chi=464172.083\chi^{2}-11536.42\chi^{2}+8971.06\) & 0.77 & 2200.45 & 0.62 & 2944.28 & 1.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression modeling analysis between NDVI (800, 1200) and AGB.  
Figure 6: The partial least squares regression variable importance in projection (PLSR VIP) of each wavelength with the combination of four different grown stages data889.44 to 2232.90 kg/hm\({}^{2}\), and RPD ranged from 1.45 to 2.37. Through comparing the accuracy of models, NDVI (800, 1200) proved to have good fitting characteristics for the estimation of AGB at different stages.  
The VIs-AGB scatter plots showed the distribution of calibration and validation dataset. The result of model 35 indicated that NDVI (800, 1200) was more effective and accurate than CIred edge in AGB estimation at the reproductive stage, and the distribution of scatter points was also more regular. When data from all four growing stages were used in the modeling, AGB estimation precision of model 37 increased significantly, with the value of R\({}^{2}\)cal, R\({}^{2}\)val, RMSEC, RMSEV, and RPD were 0.83, 0.82, 1521.84 kg/hm\({}^{2}\), 1501.34 kg/hm\({}^{2}\), and 2.37. However, the distribution scatter points of model 38 were clearly divided into two parts with a poor fitting performance. The above results indicated that NDVI (800, 1200) could be used to obtain stable AGB estimation models.