## 3 Results
### Regression Models with Selected Parameters
#### 3.5.2 PlanetScope Regression Models  
For the UAV regression models using single and multi-date datasets, the best-performing combination was the model using the 12, 20, and 27 May data with RF. Evaluating based on thresholds of variable importance, as shown in Figure 6, the selected variable groups for further model testing included the top 6, 10, 13, 17, and a group containing only PlanetScope band reflectances and VIs. A proportion of 70% of each variable group dataset was used for model calibration (Table 11). Of the calibration models, all RF models had the same high R\({}^{2}\) value with low RMSE compared to all SVR models. The best model performance was RF with the top six variables, with an R\({}^{2}\) at 0.96 and an RMSE 1.18 g/m\({}^{2}\). For the SVR models, the best-performing was the combination of the top six variables, resulting in an R\({}^{2}\) of 0.84 and an RMSE of 2.31 g/m\({}^{2}\). From the calibration models, the spectral-only SVR model had the poorest performance with an R\({}^{2}\) of 0.77 and an RMSE of 2.81 g/m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Variables** & **Model** & **Number of Variables** & **R\({}^{2}\)** & **RMSE (g/m\({}^{2}\))** \\ \hline \multirow{2}{*}{Spectral-only: All VIs \& 5 MicaSense bands} & RF & 19 & 0.50 & 3.83 \\  & SVR & 19 & 0.48 & 3.87 \\ \hline \multirow{2}{*}{Top 7: Height, LAI, NDRE, RVI2, BNDVI, CI\_RE, NIR} & RF & 7 & 0.73 & 2.81 \\  & SVR & 7 & 0.80 & 2.62 \\ \hline \multirow{2}{*}{Top 8: Top 7 + Soil Moisture} & RF & 8 & 0.73 & 2.78 \\  & SVR & 8 & 0.78 & 2.82 \\ \hline \multirow{2}{*}{Top 9: Top 8 + RDVI} & RF & 9 & 0.74 & 2.77 \\  & SVR & 9 & 0.77 & 2.80 \\ \hline \multirow{2}{*}{Top 13: Top 9 + OSAVI, WDRVI, VDVI, RGBVI} & RF & 13 & 0.75 & 2.72 \\  & SVR & 13 & 0.77 & 2.76 \\ \hline \multirow{2}{*}{Top 16: Top 13 + ISR, RVI, MTVI2} & RF & 16 & 0.74 & 2.73 \\  & SVR & 16 & 0.76 & 2.81 \\ \hline \hline \end{tabular} \({}^{1}\) All models are significant at \(p\)-value < 0.001.  
\end{table}
Table 10: Statistics for the validation of the canopy nitrogen model with 12, 20, and 27 May MicaSense data and different combinations of variables (\(n\) = 39) \({}^{1}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Variables** & **Model** & **Number of Variables** & **R\({}^{2}\)** & **RMSE (g/m\({}^{2}\))** \\ \hline \multirow{2}{*}{Spectral-only: All VIs \& 5 MicaSense bands} & RF & 19 & 0.92 & 1.50 \\  & SVR & 19 & 0.66 & 3.04 \\ \hline \multirow{2}{*}{Top 7: Height, LAI, NDRE, RVI2, BNDVI, CI\_RE, NIR} & RF & 7 & 0.96 & 1.15 \\  & SVR & 7 & 0.86 & 2.05 \\ \hline \multirow{2}{*}{Top 8: Top 7 + Soil Moisture} & RF & 8 & 0.96 & 1.12 \\  & SVR & 8 & 0.85 & 2.21 \\ \hline \multirow{2}{*}{Top 9: Top 8 + RDVI} & RF & 9 & 0.96 & 1.07 \\  & SVR & 9 & 0.85 & 2.13 \\ \hline \multirow{2}{*}{Top 13: Top 9 + OSAVI, WDRVI, VDVI, RGBVI} & RF & 13 & 0.96 & 1.07 \\  & SVR & 13 & 0.85 & 2.10 \\ \hline \multirow{2}{*}{Top 16: Top 13 + ISR, RVI, MTVI2} & RF & 16 & 0.96 & 1.11 \\  & SVR & 16 & 0.85 & 2.11 \\ \hline \end{tabular} \({}^{1}\) All models are significant at \(p\)-value < 0.001.  
\end{table}
Table 9: Statistics for the calibration of the canopy nitrogen model with 12, 20, and 27 May MicaSense data and different combinations of variables (\(n\) = 89) \({}^{1}\).  
The remaining 30% of variable datasets were used for the validation models (Table 12). The poorest model performances for RF and SVR were from the spectral-only variable group, resulting in the lowest R\({}^{2}\) and highest RMSE values out of all tested. SVR had better performance compared to RF in the spectral-only, top 6, and top 10 groups, but for the top 13 and top 17 groups, RF was better. The best-performing model was RF with the top 17 variables, with an R\({}^{2}\) of 0.92 and an RMSE of 1.75 g/m\({}^{2}\).