## 3 Results
### Crop Nitrogen Prediction Maps
Resampling was conducted with the bilinear technique, which is a bilinear interpolation and determines the new value of a cell based on a weighted distance average of the four nearest input cell centers. As the LAI raster extent was the smallest of all rasters, it was used as the output extent reference layer for the ArcGIS \"Clip Raster\" tool with the selected option to maintain clipping extent. The columns and rows of the output raster were adjusted, and the pixels were resampled to match the reference layer exactly. Next, the ArcGIS \"Extract by Mask\" tool was used to extract the cell values specifically where the LAI raster extent pixels held values greater than zero. Thus, each variable layer was prepared for the R modelling. For the UAV best-performing model, SVR with the top seven variables included height, LAI, NDRE, RVI2, BNDVI, CI_RE, and NIR. In R, the \"raster\", \"rgdal\", and \"rasterVis\" libraries were used to generate the final prediction map outputs. The variable raster layers were stacked using the \"raster:stack\" function, and the \"raster::predict\" tool was used with the selected UAV-based SVR model using the top seven variables. The resulting prediction rasters for each field were exported as GeoTiff files into ArcGIS to create the final prediction maps with 1 m \(\times\) 1 m resolution (Figure 7). The low and high CNW areas are displayed in red and green, respectively, for distinct contrast between the nitrogen levels. From the prediction rasters, the predicted CNW values around each sample point were extracted and compared to the measured values in-field. The resulting RMSE values for W1, W2, and W3 were 4.27, 2.32, and 3.08 g/m\({}^{2}\), respectively. The best-performing satellite model used RF with the top 17 variables: height, LAI, 11 VIs, and the four PlanetScope bands. Following the same processing steps as the UAV-based model, the satellite variable raster images were processed to have the same extent and 1 m \(\times\) 1 m resolution. As the satellite images began with 3 m \(\times\) 3 m resolution, much larger compared to the UAV images, the resulting raster layers had a smoothed appearance. The final prediction rasters for each field are displayed as maps in Figure 8 with 1 m \(\times\) 1 m resolution. From the prediction rasters, the predicted CNW values around each sample point were extracted and compared to the measured values in-field. The resulting RMSE values for W1, W2, and W3 were 3.12, 1.79, and 3.08 g/m\({}^{2}\), respectively. Comparing the maps in Figures 7 and 8, it can be seen that there were differences in the spatial distribution and values of CNW. It appears that the UAV maps show more areas with low canopy nitrogen weight, while PlanetScope shows more areas with high CNW. This is likely due to the resampling method, as the UAV's centimeter-level image resolution includes spectral data of the visible soil between crop rows, while for PlanetScope images with coarser meter-level resolution, the pixel values average towards the spectral values of the crop rather than the soil. The UAV-based nitrogen prediction maps may have lower performance compared to satellite-based maps because of the volatile weather conditions during the fieldwork conducted on the week of May 27 th. The UAV was flown on different dates between May 26 th and May 29 th for each field whenever the weather was suitable, and the crop spectral characteristics may have changed. However, the satellite images for the three fields for May 27 th were all from the same date and present a more consistent relationship with the fieldwork data. Figure 7: UAV-based prediction maps of canopy nitrogen weight derived from the top 7 variables in the 12, 20, and 27 May SVR model for (**a**) W1, (**b**) W2, and (**c**) W3 wheat fields.