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Journal of Applied Remote Sensing

Potential of Sentinel-2 spectral configuration to assess rangeland quality
Author(s): Abel Ramoelo; Moses Cho; Renaud Mathieu; Andrew K. Skidmore
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Paper 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.

Paper Details

Date Published: 7 August 2015
PDF: 11 pages
J. Appl. Remote Sens. 9(1) 094096 doi: 10.1117/1.JRS.9.094096
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Abel Ramoelo, Council for Scientific and Industrial Research (South Africa)
Univ. of Limpopo (South Africa)
Moses Cho, Council for Scientific and Industrial Research (South Africa)
Univ. of KwaZulu-Natal (South Africa)
Renaud Mathieu, Council for Scientific and Industrial Research (South Africa)
Univ. of Pretoria (South Africa)
Andrew K. Skidmore, Univ. Twente (The Netherlands)


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