## 4 Discussion  
For this study, the RF and SVM regression methods were used to predict the CNW of wheat using UAV MicaSense band reflectances, PlanetScope band reflectances, selected VIs, plant height, LAI, soil moisture, and topographic metrics. The models created were grouped according to UAV-based and satellite-based data. For the UAV RF and SVR regression models, calibration was conducted with 28 variables from single and multi-date datasets. Evaluating the validation models of each dataset, the performance of UAV single-date models was poor with R\({}^{2}\) values of, at most, 0.25 and overall non-significant results. Combining UAV multi-date data yielded better results, with the best performance from the RF three-date model of 12, 20, and 27 May resulting in an R\({}^{2}\) of 0.74 and an RMSE of 2.76 g/m\({}^{2}\). For the PlanetScope RF and SVR models, the calibration of the models used 24 variables for single and multi-date datasets. Of the validation models, the single-date models of 20 May and 4 June had the lowest performance. However, the other PlanetScope single-date models' performances were much better overall compared to the UAV single-date models. In general, the PlanetScope multi-date models did not have significantly better results than its single-date models. The best-performing PlanetScope model was that which was based on three dates, 12, 20, and 27 May, using RF, with an R\({}^{2}\) of 0.83 and an RMSE of 1.77 g/m\({}^{2}\). Both the best UAV and best satellite models were from 12, 20, and 27 May data, during which the wheat crops were in the BBCH 23-41 growth stages, mainly defined by tillering, stem elongation, and the beginning of the booting stage. As  
Figure 8: Satellite-based prediction maps of canopy nitrogen weight derived from top 17 variables in the 12, 20, and 27 May RF model for (**a**) W1, (**b**) W2, and (**c**) W3 wheat fields. noted by [PERSON] (2017), the application of nitrogen fertilizer during these early growth stages before flowering is most conducive to efficient nitrogen use and yield response [67]. The ability to accurately estimate the nitrogen levels of crops during early growth stages would be most beneficial for farmers. In the RF variance importance plot of the best-performing UAV model, of all variables, plant height was the most important predictor of CNW. [PERSON] and [PERSON] (2019) also noted that plant height is useful in estimating phenology, biomass, and yield in addition to nitrogen uptake in wheat [68]. On the plot, LAI was the second most important predictor of CNW. LAI has been used extensively in studies to successfully predict crop chlorophyll content, biomass, and yield [69; 70]. The study by [PERSON] et al. (2014) found a significant positive relationship between LAI and differences in crop nitrogen content across wheat growth stages [71]. The gap fraction method of calculating LAI is more accurate during the earlier growth stages of a crop when the canopy is not so dense, allowing for contrast between the vegetation and soil or vegetation and sky images [68]. Among the VI's used in the model, the red-edge VIs (NDRE, RVI2, and CI_RE) were amongst the most important. The red-edge region (680-800 nm) has been shown to encompass sharp changes in the canopy reflectance and can be used to identify important biophysical parameters of the crop. Nitrogen levels have shown the sensitivity of the red-edge region in estimating leaf chlorophyll content due to the high absorption of red radiation and high reflectance of NIR radiation [69; 72]. Of the MicaSense bands individually, the NIR band was of highest importance in the model while other individual bands had little effect. Soil moisture also appeared as a variable of high importance, and subsequent variables on the plot had noticeably lower importance. Studies have noted the importance of soil moisture in soil nitrogen mineralization, crop nitrogen uptake, and utilization [73; 74]. Of the topographic metrics, the topographic wetness indices were most important, while the remaining metrics had little effect.