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Abstract: A detailed knowledge on the heterogeneity of the soil organic carbon (SOC) content in agricultural soils is required to support applications such as precision agriculture and soil C monitoring. Imaging spectroscopy in the visible (VIS) and near-infrared (NIR) region has proven to be highly sensitive to organic soil components and can efficiently provide data with high spatial resolution. The objectives of our study were (i) to test the suitability of airborne hyperspectral imaging for the characterisation of the spatial heterogeneity of the SOC content at the field-scale, (ii) to investigate the impact of various soil surface conditions (roughness, vegetation) on SOC prediction and (iii) to produce SOC maps for arable fields on a pixel-wise basis. The soil reflectance was recorded by the aircraft-mounted hyperspectral sensor HyMap (450–2500nm) on test sites with the following varying soil surface conditions: bare soil, fine seed-bed; ploughed, bare soil; volunteer crops; straw residues. A partial least squares regression (PLSR) was performed for data analysis. Our results reveal an accurate prediction of the SOC content at a comparatively small concentration range (8.3 to 18.5gSOCkg−1) on long-term uniformly cultivated fields. Site-specific characteristics influenced the calibration models; highest prediction accuracy was performed over a bare, fine soil (RMSEP=0.76gSOCkg−1; RPD=2.08). A generated pixel-wise map (8m×8m) allows the detection of small-scale spatial variability of SOC content and comparatively more realistic than an interpolated map. Thus, airborne hyperspectral imaging constitutes a substantial progress compared to point observations and facilitates well-directed applications in precision agriculture. [Copyright &y& Elsevier]