Sensor and Platform Technologies

Potential of Sentinel-2 spectral configuration to assess rangeland quality

[+] Author Affiliations
Abel Ramoelo

Council for Scientific and Industrial Research, Earth Observation Group, Natural Resources and Environment, P.O. Box 395, Pretoria 0001, South Africa

University of Limpopo, Risk and Vulnerability Assessment Centre, Private Bag X1106, Sovenga 0727, South Africa

Moses Cho

Council for Scientific and Industrial Research, Earth Observation Group, Natural Resources and Environment, P.O. Box 395, Pretoria 0001, South Africa

University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa

Renaud Mathieu

Council for Scientific and Industrial Research, Earth Observation Group, Natural Resources and Environment, P.O. Box 395, Pretoria 0001, South Africa

University of Pretoria, Department of Geography, Geoinformatics and Meteorology, Private Bag X20 Hatfield, Pretoria 0028, South Africa

Andrew K. Skidmore

University of Twente, Faculty of Geoinformation and Earth Observation (ITC), P.O. Box 217, 7500 AE Enschede, The Netherlands

J. Appl. Remote Sens. 9(1), 094096 (Aug 07, 2015). doi:10.1117/1.JRS.9.094096
History: Received March 9, 2015; Accepted July 8, 2015
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Abstract.  Sentinel-2 is intended to improve vegetation assessment at local to global scales. Today, estimation of leaf nitrogen (N) as an indicator of rangeland quality is possible using hyperspectral systems. However, few studies based on commercial imageries have shown a potential of the red-edge band to accurately predict leaf N at the broad landscape scale. We intend to investigate the utility of Sentinel-2 for estimating leaf N concentration in the African savanna. Grass canopy reflectance was measured using the analytical spectral device (ASD) in concert with leaf sample collections for leaf N chemical analysis. ASD reflectance data were resampled to the spectral bands of Sentinel-2 using published spectral response functions. Random forest (RF), partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) were used to predict leaf N using all 13 bands. Using leave-one-out cross validation, the RF model explained 90% of leaf N variation, with a root mean square error of 0.04 (6% of the mean), which is higher than that of PLSR and SMLR. Using RF, spectral bands centered at 705 nm (red edge) and two shortwave infrared bands centered at 2190 and 1610 nm were found to be the most important bands in predicting leaf N.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Abel Ramoelo ; Moses Cho ; Renaud Mathieu and Andrew K. Skidmore
"Potential of Sentinel-2 spectral configuration to assess rangeland quality", J. Appl. Remote Sens. 9(1), 094096 (Aug 07, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.094096


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