## 3 Results and Discussion
### Leaf N Predictive Models  
Sentinel-2's red edge based simple ratio index (SR_RE3--based on 783 and 705 nm), MTCI, CIred edge explained over 75% of the leaf N variation, with 19% error, respectively (Table 3). Red edge position based on the Linear Interpolation method yielded about 70% of the leaf N variation. Even though most of the bands yielded a significant relationship with leaf N, their coefficient of determination (\(R^{2}\)) and relative root mean square error (%) indicated poor performance as compared to the red edge based vegetation indices--SR and the NDVI. In addition, red edge based indices such as CI and MTCI performed equally well. From spectroscopy studies, it was demonstrated that the red edge based indices are significantly related to chlorophyll and N concentrations [14; 21; 26]. Often, leaf N is indirectly estimated using red edge information because it is related to chlorophyll [14; 26]. Red edge indices making use of 705 nm and any near infrared band yielded the highest accuracy for estimating leaf N concentrations (Figure 3, Table 3). The latter band (705 nm) is placed at the onset of the high reflectivity portion of the vegetation response, and is crucial for plant health estimation (e.g., leaf N or chlorophyll) while the second red edge band (740 nm) is influenced by the concerted effects of plant health (e.g., leaf N or chlorophyll) [14] and the vegetation structure (e.g., leaf area index and biomass) [14; 18]. Results for this study concurs with other studies that evaluated the applicability of the multispectral red edge bands for mapping vegetation quality or health in the grasslands or savanna [16; 17; 18; 45] subtropical forests [46] and Mediterranean forests ([PERSON] et al., 2017) [47]. In addition, the notable performance of the MTCI on the estimation of N content or concentrations has been further reported by [PERSON] et al. [46]; [PERSON] et al. [45] and [PERSON] et al. [47]. The leaf N model based on SR-RE3 was evaluated using independent leaf N and RapidEye data collected from the same area during peak productivity (March 2010) [16]. The predicted leaf N values were significantly correlated to the measured data collected in 2010, with acceptable accuracy (\(r\) = 0.48 (\(R^{2}\) = 0.23), RMSE = 0.36%). Figure 4, shows the scatterplot indicating a relationship between observed and predicted leaf N values. The performance of the SR_SR3 model (this study) is comparable with the significant model achieved using RapidEye data (\(R^{2}\) = 0.23, RMSE = 0.15%N) [16]. This further highlights the potential of the red edge band in developing transferable models. Sentinel-2 data sets are set to improve the estimation of leaf N at various scales, including the global scale, in a cost-effective manner. The strategic placement of the two red edge bands provide an opportunity to estimate and map leaf N during peak productivity. [PERSON] et al. [18], indicated that Sentinel-2's short-wave infrared bands are important in the estimation of leaf N (1610 and 2190 nm), which further provides an opportunity to estimate leaf N during the dry season. In this study, we used only the visible-near infrared (VNIR) spectral regions to further explore the utility of the two red \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Min** & **Max** & **Mean** & **CV (\%)** & **N** \\ \hline
0.54 & 2.05 & 0.88 & 36.35 & 30 \\ \hline \hline \end{tabular} CV = Coefficient of Variation.  
\end{table} 
Table 2: Descriptive statistics for leaf N (%) across the study area. edge bands. The results achieved here are comparable with that of [PERSON] et al. (2016) where red edge based indices--from RapidEye images were used to regionally map leaf N, (see also Figure 4. [PERSON] et al. (2018) and [PERSON] et al. (2018)) and indicated that WorldView-2, has the potential to estimate leaf N with comparable accuracy. The main disadvantage of using RapidEye and WorldView data is the cost, and it might not be affordable for most developing countries.