## 1 Introduction  
Rangelands cover about 51% of the Earth's land surface [1; 2] and provide food production for millions of the world population. Most of the millions of people relying on rangelands for their daily sustenance, are rural and sometimes poor communities. The human population in 2050 is projected to be more than 9 billion and most of the increase is estimated to be in developing countries, with more than half in Africa [3]. Rapid increases in population will cause changes in land cover and land use, which impact rangelands and food security through land degradation [4; 5]. Land degradation is regarded as a threat to the productivity of rangelands [5]. Degradation or loss of rangeland potential to provide grazing resources is also exacerbated by continued global climatic change [6]. Climate change induces erratic rainfall and increases temperatures. As a result, disasters such as drought become prevalent in Africa, affecting a high proportion of livestock production by reducing the availability and quality of grazing forage resources. Drought as a consequence of climate change is devastating rangelands and livestock systems in Africa. The occurrence of drought limits the carrying capacity and stocking rates of the rangelands. Livestock mortality, which is common during this period has many implications for the livelihood of the local community. The development of techniques to examine the state, extent and quality of rangeland is critical. In this context, leaf nitrogen (leaf N) could be useful to assess the quality of rangeland, and thus inform decision makers on planning and management. Leaf N concentration is an essential component for understanding biogeochemical cycling and a good indicator of vegetation vitality ([PERSON], 2014) and grass or forage quality (i.e., protein) which is important for understanding the movements and feeding patterns of herbivores ([PERSON], 2014; [PERSON], 2014). Leaf N is a key component for determining vegetation conditions, and is useful for determining livestock carrying capacity and stocking rates. It is critical to have the latter indicators on order to circumvent the problems of over-grazing, soil erosion and land degradation which impact negatively on rangeland conditions and hence livestock production. The conventional approach to assessing the spatial and temporal distribution of leaf N in rangelands is reliant on extensive field data collection which is expensive, labour intensive and time consuming, especially for wider geographic areas. Satellite remote sensing provides an alternative approach for mapping leaf N for wide geographic areas and over time. The estimation of leaf N in grass has been successful using hyperspectral data with both field spectrometer and airborne data ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2016; [PERSON] et al., 2017). The latter was possible because of the development of the second generation of vegetation indices such as the red edge position (REP) ([PERSON] et al., 2016; [PERSON] et al., 2016) and narrow band indices which are sensitive to subtle changes in leaf chlorophyll content in contrast to the traditional Normalized Difference Vegetation Index (NDVI) which saturates at high green vegetation canopy cover ([PERSON] et al., 2014; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). The new generation of satellite constellations has strategically incorporated the red edge band to improve vegetation and crop condition monitoring. These include commercial satellites such as WorldView-2/3 and RapidEye. However, the data from these sensors comes at an almost prohibitive cost, especially for the developing world. On the other hand, Sentinel-2, a multispectral satellite developed and launched by the European Space Agency (ESA) which features 13 spectral bands in the visible to shortwave infrared and including two red edge bands (705 nm and 740 nm) provides freely available images.