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.
