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dc.contributor.author | Ramoelo, Abel![]() |
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dc.contributor.author | Cho, Moses Azong![]() |
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dc.date.accessioned | 2018-08-30T08:00:11Z | |
dc.date.available | 2018-08-30T08:00:11Z | |
dc.date.issued | 2018-02-09 | |
dc.description.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. | en_ZA |
dc.description.department | Plant Production and Soil Science | en_ZA |
dc.description.librarian | am2018 | en_ZA |
dc.description.sponsorship | CSIR, National Research Foundation (NRF)—SASSCAL Project and European Union’s Horizon 2020 research and innovation programme under grant agreement No. 641762 (ECOPOTENTIAL Project). | en_ZA |
dc.description.uri | http://www.mdpi.com/journal/remotesensing | en_ZA |
dc.identifier.citation | Ramoelo, A. & Cho, M.A. 2018, 'Explaining leaf nitrogen distribution in a semi-arid environment predicted on sentinel-2 imagery using a field spectroscopy derived model', Remote Sensing, vol. 10, art. no. 269, pp. 1-15. | en_ZA |
dc.identifier.issn | 2072-4292 (online) | |
dc.identifier.other | 10.3390/rs10020269 | |
dc.identifier.uri | http://hdl.handle.net/2263/66377 | |
dc.language.iso | en | en_ZA |
dc.publisher | MDPI Publishing | en_ZA |
dc.rights | © 2018 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en_ZA |
dc.subject | Leaf nitrogen | en_ZA |
dc.subject | Grass quality | en_ZA |
dc.subject | Field spectrometer | en_ZA |
dc.subject | Sentinel-2 | en_ZA |
dc.subject | Mapping | en_ZA |
dc.subject | Red edge band | en_ZA |
dc.subject | Analytical spectral device (ASD) | en_ZA |
dc.subject | Higher-plant leaves | en_ZA |
dc.subject | Spectral reflectance | en_ZA |
dc.subject | Regression | en_ZA |
dc.subject | Grass nitrogen | en_ZA |
dc.subject | Forage quality | en_ZA |
dc.subject | Multispectral data | en_ZA |
dc.subject | Chlorophyll (Chl) | en_ZA |
dc.subject | South Africa (SA) | en_ZA |
dc.title | Explaining leaf nitrogen distribution in a semi-arid environment predicted on sentinel-2 imagery using a field spectroscopy derived model | en_ZA |
dc.type | Article | en_ZA |