Abstract
Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping.
Highlights
Rangelands cover about 51% of the Earth’s land surface [1,2] and provide food production for millions of the world population
The variation of leaf nitrogen (leaf N) across the study areas was moderate with a co-efficient of variation about 36% (Table 2)
Lower values are associated with the granite, while the higher end of the leaf N values represents the gabbro and basalt geological sampling sites
Summary
Rangelands cover about 51% of the Earth’s land surface [1,2] and provide food production for millions of the world population. 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 exacerbated by continued global climatic change [6]. 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
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