## 3 Results
### Crop Nitrogen Prediction Maps  
To create the crop nitrogen prediction maps, raster layers of each variable were required. The VI raster layers were created using the raster calculator in PCI Geomatica Banff from the MicaSense band rasters mosaicked from Pix4 Dmapper. To extract raster layers for height, the Phantom 4 RTK flight processed in Pix4 Dmapper includes the option for a digital surface model (DSM) output. The DSM captures the natural and built features of the environment. By subtracting the DEM raster from the DSM raster pixel values using ArcGIS raster calculator, the output raster layer has the height data of the crop [65]. Generating an LAI raster layer is a more involved process. The process is based on methodology proposed by [PERSON] et al. (2020) using a simulated observation of a point cloud Multiview angle (SOPC-M) designed to obtain a 3D spatial distribution of vegetation and bare ground points to calculate the gap fraction, followed by the crop's effective LAI (LAIe), from a UAV-based 3D point cloud dataset [66]. In ArcGIS, the point data layer was converted to a raster layer using the \"Point to Raster\" conversion tool to a 2 m \(\times\) 2 m resolution. From the linear regression equation, the digital hemispherical photography (DHP) LAIe \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: VIs \& 4 PlanetScope bands} & RF & 15 & 0.96 & 1.21 \\  & SVR & 15 & 0.77 & 2.81 \\ \hline \multirow{2}{*}{Top 6: Height, BLUE, LAI, BINDVI, RDVI, OSAVI} & RF & 6 & 0.96 & 1.18 \\  & SVR & 6 & 0.84 & 2.31 \\ \hline \multirow{2}{*}{Top 10: Top 6 + ISR, MTVI2, NIR, RED} & RF & 10 & 0.96 & 1.20 \\  & SVR & 10 & 0.84 & 2.35 \\ \hline \multirow{2}{*}{Top 13: Top 10 + MSR, GREEN, RBGVI} & RF & 13 & 0.96 & 1.21 \\  & SVR & 13 & 0.81 & 2.45 \\ \hline \multirow{2}{*}{Top 17: Top 13 + RVI, VDVI, NDVI, WDVI} & RF & 17 & 0.96 & 1.26 \\  & SVR & 17 & 0.82 & 2.63 \\ \hline \hline \end{tabular} \({}^{1}\) All models are significant at _p_-value \(<\)0.001.  
\end{table} 
Table 11: Statistics for the calibration of the PlanetScope canopy nitrogen model with 12, 20, and 27 May data and different combinations of variables (_n_ = 100) \({}^{1}\). \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: VIs \& 4 PlanetScope bands} & RF & 15 & 0.66 & 3.20 \\  & SVR & 15 & 0.69 & 3.09 \\ \hline \multirow{2}{*}{Top 6: Height, BLUE, LAI, BINDVI, RDVI, OSAVI} & RF & 6 & 0.72 & 2.90 \\  & SVR & 6 & 0.85 & 2.17 \\ \hline \multirow{2}{*}{Top 10: Top 6 + ISR, MTVI2, NIR, RED} & RF & 10 & 0.83 & 2.16 \\  & SVR & 10 & 0.84 & 2.06 \\ \hline \multirow{2}{*}{Top 13: Top 10 + MSR, GREEN, RBGVI} & RF & 13 & 0.87 & 2.20 \\  & SVR & 13 & 0.86 & 2.39 \\ \hline \multirow{2}{*}{Top 17: Top 13 + RVI, VDVI, NDVI, WDVI, WDVI} & RF & 17 & 0.92 & 1.75 \\  & SVR & 17 & 0.91 & 1.85 \\ \hline \hline \end{tabular} \({}^{1}\) All models are significant at _p_-value \(<\)0.001.  
\end{table} 
Table 12: Statistics for the validation of the PlanetScope canopy nitrogen model with 12, 20, and 27 May data and different combinations of variables (_n_ = 44) \({}^{1}\). could be calculated using the ArcGIS raster layer to create the final LAI raster used in the model. For field W3, the LAI layer processing area error unfortunately only covered 12 of the 16 sample points; thus, the final map produced did not cover all sample points. Prior to running the regression models in R, the raster layers needed to have the same resolution and extent for the functions to stack them. All raster layers were run through the ArcGIS \"Resample\" tool to 1 m \(\times\) 1 m resolution, as the CNW variable is based on a 1 m\({}^{2}\) area.