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In recent years it has been shown that laboratory and field visible near infrared spectroscopy (VNIRS) allows for the accurate prediction of soil organic carbon (SOC) - more rapidly, less expensively, and at larger scales than conventional soil laboratory methods. VNIRS might find application in the restoration assessment of the degraded, semi-arid subtropical thickets of the Albany Thicket Biome (ATB) of the Eastern Cape Province of South Africa. During the twentieth century, the semi-arid forms of the ATB suffered heavy browsing by goats, transforming the dense closed-canopy shrubland into an open savannah-like system. This paper presents a study dealing with SOC estimation of soil surface (0-5mm) and topsoil (0-200mm) in the degraded ATB, through the combination of soil spectroscopy and partial least square regression (PLSR). Spectroscopic measurements and soil samples were collected along a transect in the ATB. The PLSR models developed with laboratory and field spectra gave good predictions of SOC, with root mean square error of validation (RMSEV) <5.0 and 5.5gCkg-1, respectively. The use of the full visible near-infrared spectral range gave better SOC predictions than using either visible or near-infrared separately. The resampling simulation of the field surface spectra to the 232 channels of the satellite-born EnMAP sensor gave good SOC predictions for laboratory conditions (RPD>2), but low accuracy (RMSE: 9.88gCkg-1) for field model. The results of this research study indicated that, for the ATB, (i) combining soil spectroscopy and PLSR does favor accurate prediction of SOC, (ii) the predictions of surface SOC can be used as a proxy of topsoil SOC, and (iii) there is potential for future application of satellite-born hyperspectral data for SOC content predictions. © 2011 Elsevier B.V.