###### Abstract  
The role of biodiversity in improving the primary productivity within terrestrial ecosystems is well documented. Each species in an ecosystem has a role to play in the overall productivity of an ecosystem. Grass species nitrogen (N) estimation is essential in rangelands, especially in rugged terrain such as mountainous regions. It is an indicator of forage quality, which has nutritional implications for grazing animals. This research sought to improve and test the predictability of grass N by applying a combination of remotely sensed spectral bands and vegetation indices as input. Recursive feature selection was used to select the optimal spectral bands and vegetation indices for predicting grass N. Subsequently, the selected vegetation indices and bands were used as input into the non-parametric random forest (RF) regression to predict grass N. The prediction of grass N improved slightly in the vegetation indices model (81%) compared to the bands model (80%), and the highest prediction was achieved by combining the two (85%). This research ascertains that including red-edge-based vegetation indices improves the prediction of grass N. S2 MSI remains the ideal remote sensing tool for estimating grass N because of its strategically positioned red-edge bands, which are highly correlated with chlorophyll content in plants. nutrients; rangeland; conservation; modeling; earth observation; remote sensing +
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quantity and can provide pivotal information for farmers, land planners, and managers [9]. Estimating vegetation's biophysical and biochemical variables is essential for improved carrying capacity models [10]. Despite achieving moderate accuracies (\(R^{2}<0.65\)), high spatial resolution sensors are used to predict tree biomass for integration in carrying capacity models in the heterogenous Savanna biome [10]. By using similar remote sensing techniques, a substantially higher prediction accuracy was achieved when estimating tree and grass biomass of the Savanna biome, over 89%, respectively [9]. The insights on the role of freely available multispectral sensors in predicting grass N of homogenous grassland are warranted, especially in undulated mountainous landscapes. In African protected areas, nutrient deposition by large grazing mammals is pivotal for enhancing carrying capacity [7; 11]. Conversely, excessive grazing can reduce mineralization rates via changes in vegetation dynamics, i.e., species composition, cover, and diversity, thus affecting forage quality and quantity [12]. Nonetheless, studies have indicated the mutual effect of grazing on grassland biodiversity and productivity [11]. The distribution and abundance of large grazing mammals in South African rangelands are strongly influenced by nutritionally sufficient forages and vegetation dynamics [7]. For example, these animals prefer to forage on swards enriched in minerals, which is pivotal for their nutrition and reproduction [13]. Determining habitat conditions, usage [14], and mineral nutrients [13] has significant implications on stocking rates and carrying capacity. Remote sensing data are constantly used with field data at multiple scales to estimate rangeland indicators [15]. Some attempts to estimate grass N were made using hyperspectral in situ [16] and ex situ [17] data. Remote sensing satellite imagery could be used to predict rangeland indicators such as plant species cover, structure, and composition; in fact, a combination of Landsat (coarser spatial resolution sensor) and rapid-eye (high spatial resolution sensor) predicted a rangeland's vegetation and bare ground cover with an accuracy of over 80% [15], further cementing the role of remote sensing data in producing spatially explicit and continuous surface estimates of rangeland indicators. It has been shown that vegetation indices from high spatial remote sensing data explained the leaf nitrogen content in South African Savanna rangelands [9].