Visible and near-infrared (VIS-NIR) non-imaging spectroscopy has been widely applied to estimate soil organic carbon (SOC) content. Due to the high demand for soil sample pretreatments, VIS-NIR non-imaging spectroscopy easily suffers from soil roughness in practical application. This study explored the potential of imaging spectroscopy to estimate SOC content with high soil roughness. With soil samples collected in Iowa State, United States, imaging spectra were utilized to measure the VIS-NIR spectra of soil samples with and without ground. With five spectral pre-processing including continuum removed (CR), absorbance transformation (AB), S-G smoothing (SG), standard normal variate (SNV), and multiplicative scatter correction (MSC), partial least squares regression (PLSR) and support vector regression (SVR) were used to build estimation models to analyze the potential of imaging spectra. Non-imaging spectra were also applied to build PLSR and SVR models as a comparison. Results demonstrated that imaging spectra could achieve SOC content estimation for soil samples with high roughness, but non-imaging spectra could not successfully estimate that. The best PLSR and SVR model developed by imaging spectra could reach 0.739 and 0.712 of R² for SOC content estimation of soil samples with high roughness, while that established by non-imaging spectra could achieve 0.344 and 0.311 of R². Based on the imaging spectra after the four pre-processing methods of AB, SG, SNV, and MSC, the performance of the PLSR model established before soil sample grinding was better than that of the PLSR model established after soil sample grinding, while the performance of the SVR model was just the opposite. For non-imaging spectra, the accuracies of PLSR and SVR models established after soil samples grinding were always better than that of models established before soil samples grinding. For these two spectral data and the two estimation models, different spectral pre-processing methods had different abilities to improve the estimation accuracy of the model. The performance of imaging spectroscopy outperformed non-imaging spectra before or after being ground soil samples. Imaging spectra could enhance the correlation coefficient between VIS-NIR spectra and SOC for soil samples with high roughness, there by improving PLSR model’s performance. Our findings provide a new way to estimate SOC content on large-scale yield because imaging spectra could overcome the influence of soil roughness.