Ramoelo, AbelSkidmore, AKCho, Moses ASchlerf, MMathieu, Renaud SAHeitkönig, IMA2012-06-202012-06-202012-10Ramoelo, A., Skidmore, A.K., Cho, M.A., Schlerf, M., Mathieu, R. and Heitkönig, I.M.A. 2012. Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation, vol. 19, pp 151-1620303-2434http://www.sciencedirect.com/science/article/pii/S0303243412001171http://hdl.handle.net/10204/5925Copyright: 2012 Elsevier. This is the pre-print version of the work. The definitive version is published in the International Journal of Applied Earth Observation and Geoinformation, vol. 19, pp 151-162The regional mapping of grass nutrients is of interest in the sustainable planning and management of livestock and wildlife grazing. The objective of this study was to estimate and map foliar and canopy nitrogen (N) at a regional scale using a recent high resolution spaceborne multispectral sensor (i.e. RapidEye) in the Kruger National Park (KNP) and its surrounding areas, South Africa. The RapidEyesensor contains five spectral bands in the visible-to-near infrared (VNIR), including a red-edgeband centered at 710 nm. The importance of the red-edgeband for estimating foliar chlorophyll and N concentrations has been demonstrated in many previous studies, mostly using field spectroscopy. The utility of the red-edgeband of the RapidEyesensor for estimating grass N was investigated in this study. A two-step approach was adopted involving (i) vegetation indices and (ii) the integration of vegetation indices with environmental or ancillary variables using a stepwise multiple linear regression (SMLR) and a non-linear spatial least squares regression (PLSR). The model involving the simple ratio (SR) index (R805/R710) defined as SR54, altitude and the interaction between SR54 and altitude (SR54 * altitude) yielded the highest accuracy for canopy N estimation, while the non-linear PLSR yielded the highest accuracy for foliar N estimation through the integration of remote sensing (SR54) and environmental variables. The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the rededge waveband with a high spatial resolution.enGrass nitrogenSavanna ecosystemIntegrated modelingRed-edgebandRapidEyeVegetation indicesArticleRamoelo, A., Skidmore, A., Cho, M. A., Schlerf, M., Mathieu, R. S., & Heitkönig, I. (2012). Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor. http://hdl.handle.net/10204/5925Ramoelo, Abel, AK Skidmore, Moses A Cho, M Schlerf, Renaud SA Mathieu, and IMA Heitkönig "Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor." (2012) http://hdl.handle.net/10204/5925Ramoelo A, Skidmore A, Cho MA, Schlerf M, Mathieu RS, Heitkönig I. Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor. 2012; http://hdl.handle.net/10204/5925.TY - Article AU - Ramoelo, Abel AU - Skidmore, AK AU - Cho, Moses A AU - Schlerf, M AU - Mathieu, Renaud SA AU - Heitkönig, IMA AB - The regional mapping of grass nutrients is of interest in the sustainable planning and management of livestock and wildlife grazing. The objective of this study was to estimate and map foliar and canopy nitrogen (N) at a regional scale using a recent high resolution spaceborne multispectral sensor (i.e. RapidEye) in the Kruger National Park (KNP) and its surrounding areas, South Africa. The RapidEyesensor contains five spectral bands in the visible-to-near infrared (VNIR), including a red-edgeband centered at 710 nm. The importance of the red-edgeband for estimating foliar chlorophyll and N concentrations has been demonstrated in many previous studies, mostly using field spectroscopy. The utility of the red-edgeband of the RapidEyesensor for estimating grass N was investigated in this study. A two-step approach was adopted involving (i) vegetation indices and (ii) the integration of vegetation indices with environmental or ancillary variables using a stepwise multiple linear regression (SMLR) and a non-linear spatial least squares regression (PLSR). The model involving the simple ratio (SR) index (R805/R710) defined as SR54, altitude and the interaction between SR54 and altitude (SR54 * altitude) yielded the highest accuracy for canopy N estimation, while the non-linear PLSR yielded the highest accuracy for foliar N estimation through the integration of remote sensing (SR54) and environmental variables. The study demonstrated the possibility to map grass nutrients at a regional scale provided there is a spaceborne sensor encompassing the rededge waveband with a high spatial resolution. DA - 2012-10 DB - ResearchSpace DP - CSIR KW - Grass nitrogen KW - Savanna ecosystem KW - Integrated modeling KW - Red-edgeband KW - RapidEye KW - Vegetation indices LK - https://researchspace.csir.co.za PY - 2012 SM - 0303-2434 T1 - Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor TI - Regional estimation of savanna grass nitrogen using the red-edge band of the RapidEye sensor UR - http://hdl.handle.net/10204/5925 ER -