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Remote Sens. 2018, 10(2), 269; https://doi.org/10.3390/rs10020269

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

1
Earth Observation Research Group, Natural Resources and the Environment Unit, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa
2
Risk and Vulnerability Assessment Centre, University of Limpopo, Sovenga 0727, South Africa
3
Department of Plant and Plant Science, University of Pretoria, Pretoria 0001, South Africa
*
Author to whom correspondence should be addressed.
Received: 7 December 2017 / Revised: 26 January 2018 / Accepted: 5 February 2018 / Published: 9 February 2018
(This article belongs to the Collection Sentinel-2: Science and Applications)
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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. View Full-Text
Keywords: leaf nitrogen; grass quality; field spectrometer; Sentinel-2; mapping; red edge band leaf nitrogen; grass quality; field spectrometer; Sentinel-2; mapping; red edge band
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ramoelo, A.; Cho, M.A. Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model. Remote Sens. 2018, 10, 269.

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