## 4 Discussion
In the RF variance importance plot of the best-performing satellite model, similar to the UAV model, plant height was the most important predictor of CNW, with LAI following closely. Interestingly, the PlanetScope blue band was the second most important variable. As the PlanetScope blue band has greater width compared to MicaSense, perhaps the wider bandwidth captured a change of canopy reflectance in the blue-edge region (480-517 nm) which was previously noted in the study by [PERSON] et al. (2008) to be related to crop nitrogen [75]. Other PlanetScope bands in the model had varying levels of importance interspersed amongst the 11 VIs used. On the plot, other non-spectral variables of soil moisture and topographic metrics were of least importance to the model. From the UAV-based RF variance importance plot, groups of variables were selected for testing in models. Groups of the top 7, 8, 9 13, 16, and spectral-only variables were modeled, with the group of top seven variables of the SVR model performing the best with an R\({}^{2}\) of 0.80 and an RMSE of 2.62 g/m\({}^{2}\). The top seven variables included plant height, LAI, all three red-edge VIs, BNDVI, and the MicaSense NIR band. Compared to the best models from studies by [PERSON] et al. (2021), [PERSON] et al. (2019), and [PERSON] et al. (2018) using UAV-based spectral variables to estimate wheat nitrogen content, their models had lower R\({}^{2}\) values, ranging from 0.76 to 0.63, and greater RMSE values [24; 26; 27]. With the UAV-based best model in this study, significantly lower RMSE is a major advantage in terms of reducing the costs of nitrogen fertilizer. The satellite-based variance importance plot was used to select variable groups for model testing including the top 6, 10, 13, 17, and spectral-only variables. The RF model group of the top 17 variables had the best performance with an R\({}^{2}\) of 0.92 and an RMSE of 1.75 g/m\({}^{2}\). The height, LAI, all four PlanetScope bands, and total 11 VIs were the variables in the best-performing model. For both the UAV and satellite best-performing selected-variable models, plant height and LAI were the only non-spectral variables. With methods for deriving the height and LAI of a wheat crop field from the UAV Phantom 4 RTK imagery, all variables in the top models can be obtained from in-situ, non-destructive, remote sensing data. For both the UAV and satellite spectral-only variable groups, the results were poor with R\({}^{2}\) values <0.50 and significantly higher RMSE values compared to other tested variable groups. This is consistent with the studies by [PERSON] et al. (2021) and [PERSON] et al. (2016), in which it was noted that, within wheat crops, UAV imagery was limited for the observation of nitrogen status but had good performance in the monitoring of biophysical parameters [27, 28]. For the UAV validation spectral-only model, the RMSE dropped by 32% as compared to the best-performing top seven variable model. In the satellite validation spectral-only model, the RMSE dropped by 45% compared to the best-performing top 17 variable model. In the final validation of canopy nitrogen models with variable combinations, the UAV SVR models mostly had greater R\({}^{2}\) values but also greater RMSE values compared to the RF models. In the UAV spectral-only variable group models, RF had better results than SVR. Considering studies with spectral-only variables for crop nitrogen models, the results are consistent, with RF yielding better nitrogen level prediction compared to SVR models [25, 29, 76]. Only in the UAV best-performing model of top seven variables was SVR performance better in terms of having both a higher R\({}^{2}\) value and a lower RMSE compared to RF. Of the satellite variable combination models, SVR had better performance than RF except for the top 13 and 17 variable groups. The best-performing satellite model was RF with the top 17 variable group.