Sentinel-2 satellite Multispectral Image (MSI) is one of the recent advancement of satellite optical imaging for detecting and tracking oil spills. MSI equipped with enhanced radiometric and spatial resolutions, apart from relatively high temporal resolution of every 5 days revisit capability. Both systematic errors of the geometric and radiometric of level 1 and 2 data were successfully treated before any data download for users’ levels applications. As such, leaving the random errors, crucially to be minimized to enable oil spill detection and tracking due to non-discernible absolute signatures of spills against the scene background and the look-alikes. The magnitude of these random errors’ minimization and the efficacy of the MSI absolute signatures within visible bands for oil spills is very crucial. However, it is rarely reported; in fact, it is a new issue to be addressed accordingly. The calibrating tool was created with oil spill spots revealed by the official authorities. Whereas, the spill pixels are identified in the corresponding pre-processed Sentinel MSI image using region growing segmentation algorithm. These spill pixels grown were analyzed against the RGB bands, logistically regressed against the oil spill via a spectral library of the crude oil type. Originated from Arabian Gulf region with an average film thickness of 0.5 to 4 mm; reporting a calibrating function in a form gain and bias corrections for RGB bands, respectively. The results indicated that calibrated MSI spill pixels have higher correlation (r² > 0.85, p < 0.001). As the signature variations were used to formulate calibration matrices for spills identified from satellite images which can be used for processing of spill monitoring system.