Soil Organic Carbon (SOC) plays a critical role in the global carbon cycle. Accurately estimating SOC in cultivated lands is essential for assessing their carbon sequestration potential, overall soil quality, and for developing adaptive agricultural management strategies, serving as an indicator of soil health and degradation. Traditional methods for SOC mapping are usually time-consuming, however, the rapid advancement of remote sensing technology offers a novel approach to SOC estimation. This research describes a new method for SOC mapping, evaluating the current capabilities of remote sensing technologies, focusing on multispectral data. The highly frequent revisit time Sentinel-2 data underwent extensive preprocessing, first, to isolate bare soil areas. Then, using ground truth data and remote sensing data, a dataset was created and used to train machine learning regression algorithms. Various regression methods were tested. Our results indicate that Sentinel-2 data can be effectively used for SOC estimation and that the proposed method works adequately. These findings highlight the potential of remote sensing data for SOC mapping and emphasize the need for further research in this field.