## 6 Discussion  
The correlation coefficients between grass N and remote sensing bands were moderate and significant, except for the vegetation indices, which were not significant. Indeed, studies show that leaf N correlates more with spectral bands, especially those in the red edge position [9]. This may be because multispectral optical remote sensing bands provide more estimates of vegetation characteristics, whereas microwave methods provide information on the structural characteristics of vegetation [36]. This study showed a poor correlation between vegetation indices derived from broad-based sensors and leaf N, attributed to signal saturation [37], phenology, and seasonality [18]. Leaf N and biomass have been effectively estimated by red-edge-based vegetation indices using hyperspectral [37] and multispectral remote sensing [9].  
Sentinel-2 multispectral sensor has become a very useful remote sensing tool because it is freely available and includes the red-edge bands, which are related to chlorophyll in plants [38]. It is not perplexing, therefore, that the red and red-edge bands are among the most selected bands in this study because they are related to chlorophyll, which is related to leaf N [38]. This research shows that Sentinel-2 can provide important information about vegetation spectra with results comparable to commercial hyperspectral sensors. This study shows that models predicting leaf N of grasses using higher resolution sensors could be calibrated and used for data from coarser sensors as they provide similar information on vegetation characteristics. Furthermore, this research indicates that rangeland monitoring with sensors with rangeland capability is possible [38]. Indeed, Sentinel-2 performed considerably better in predicting grass N than rapid eye, which has a higher spatial resolution sensor [19].  
Notably, the red edge 4 was the band selected for optimally estimating grass N in this study; this indicates the vegetation stress observed during our sampling period as it averages grass phenology. The relationship between red edge position (REP) and foliar N depends on nitrogen and chlorophyll [38]. Thus, shifts in the red edge position are a good indicator of changes in foliar N and water stress because changes in the REP is mainly attributed to the chlorophyll content, which peaks and decreases during the wet and dry seasons, respectively [38; 39]. Our study shows that insights into vegetation senescence are possible using Sentinel-2. This has a major implication on rangeland management because this is where plants lose their primary productivity rate, affecting grazing animals and, as a result, stocking rates and carrying capacity. Hence, using hyperspectral data, leaf N content can be estimated in the dry season with reasonable accuracy. This study shows that the multispectral sensors with the REP can achieve similar estimates.  
The prediction of grass N improved slightly in the vegetation indices model compared to the bands model, and the highest prediction was achieved in a model combining the two. This ascertains that including red-edge vegetation indices improves the estimation of foliar N [18]. It was found that the prediction accuracies of grass N by remote sensing variables decrease as they senesce [9]. However, the multiple red-edge bands of Sentinel-2 MSI provide an opportunity to estimate the biochemical concentrations of grass N across their phenology and during a specific period of their life cycle with improved accuracies. Notwithstanding, the TVIR4 bands were selected for optimally predicting grass N in this study. However, TCARIR4 and MCARIR4 had the most important variable. In univariate modeling techniques, the above vegetation indices performed poorly in predicting foliar N [18]. Our study highlights the importance of exploiting machine learning techniques in exploring the spectrum of vegetation indices derived from multiple red-edge bands of S2 MSI.