## 3 Results
### Regression Models with All Parameters
#### 3.3.2 PlanetScope Regression Models  
For the PlanetScope regression models, 24 variables were used in the first step, including: the four PlanetScope band reflectances, 11 VIs, plant height, soil moisture, LAI, topographic slop, aspect, profile curvature, plan curvature, TWI #1, and TWI #2. To test the temporal effects on the models, single-date and multi-date dataset combinations were used. Taking 70% of the dataset to calibrate the RF and SVR models, overall RF had better performance to SVR with higher R\({}^{2}\) and lower RMSE values (Table 7). Of all models, RF with the combination of the 12, 20, and 27 May data had best performance with an R\({}^{2}\) of 0.96 and an RMSE of 1.10 g/m\({}^{2}\). Of the SVR models alone, the 12, 20, and 27 May model had the highest performance with an R\({}^{2}\) of 0.87 and an RMSE of 2.12 g/m\({}^{2}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Model** & **R\({}^{2}\)** & _p-Value \({}^{1}\)_ & **RMSE (g/m\({}^{2}\))** & _(n)_ \\ \hline
5 and 12 May & RF & 0.41 & \(<\)0.001 & 0.99 & 24 \\  & SVR & 0.52 & \(<\)0.001 & 0.81 & 24 \\ \hline
12 and 20 May & RF & 0.66 & \(<\)0.001 & 1.72 & 29 \\  & SVR & 0.71 & \(<\)0.001 & 1.56 & 29 \\ \hline
20 and 27 May & RF & 0.67 & \(<\)0.001 & 2.26 & 24 \\  & SVR & 0.55 & \(<\)0.001 & 2.38 & 24 \\ \hline
27 May, 4 June & RF & 0.45 & \(<\)0.001 & 5.80 & 24 \\  & SVR & 0.30 & 0.001 & 6.44 & 24 \\ \hline
5, 12, and 20 May & RF & 0.37 & \(<\)0.001 & 1.58 & 39 \\  & SVR & 0.52 & \(<\)0.001 & 1.32 & 39 \\ \hline
12, 20, and 27 May & RF & 0.74 & \(<\)0.001 & 2.76 & 39 \\  & SVR & 0.61 & \(<\)0.001 & 3.50 & 39 \\ \hline
20 and 27 May, 4 June & RF & 0.74 & \(<\)0.001 & 4.12 & 39 \\  & SVR & 0.71 & \(<\)0.001 & 4.46 & 39 \\ \hline \hline \end{tabular} \({}^{1}\) NS = non-significant.  
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Model** & **R\({}^{2}\)** & **RMSE (g/m\({}^{2}\))** & _(n)_ \\ \hline
5 May & RF & 0.94 & 0.40 & 33 \\  & SVR & 0.82 & 0.60 & 33 \\ \hline
12 May & RF & 0.92 & 0.30 & 33 \\  & SVR & 0.71 & 0.49 & 33 \\ \hline
20 May & RF & 0.85 & 1.23 & 33 \\  & SVR & 0.52 & 2.03 & 33 \\ \hline
27 May & RF & 0.90 & 1.89 & 33 \\  & SVR & 0.60 & 3.41 & 33 \\ \hline
4 June & RF & 0.91 & 2.23 & 33 \\  & SVR & 0.70 & 3.80 & 33 \\ \hline
5 and 12 May & RF & 0.95 & 0.55 & 67 \\  & SVR & 0.76 & 0.83 & 67 \\ \hline
12 and 20 May & RF & 0.94 & 0.78 & 67 \\  & SVR & 0.78 & 1.48 & 67 \\ \hline
20 and 27 May & RF & 0.94 & 1.27 & 67 \\  & SVR & 0.75 & 2.57 & 67 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics for the calibration of the PlanetScope canopy nitrogen models with 24 variables as a function of the date and the modelling approach (RF or SVR) \({}^{1}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Model** & **R\({}^{2}\)** & **RMSE (g/m\({}^{2}\))** & **(\(n\))** \\ \hline
27 May, 4 June & RF & 0.94 & 1.93 & 67 \\  & SVR & 0.74 & 3.87 & 67 \\ \hline
5, 12, and 20 May & RF & 0.93 & 0.75 & 100 \\  & SVR & 0.74 & 1.34 & 100 \\ \hline
12, 20, and 27 May & RF & 0.96 & 1.10 & 100 \\  & SVR & 0.87 & 2.12 & 100 \\ \hline
20 and 27 May, 4 June & RF & 0.95 & 1.87 & 100 \\  & SVR & 0.83 & 3.68 & 100 \\ \hline \end{tabular} \({}^{1}\) All models are significant at _p_-value < 0.001.  
\end{table} 
Table 7: _Cont._