The launch of Landsat-8 with its Operational Land Imager sensor (OLI) in 2013, followed by Landsat-9 satellite (OLI-2) in 2021, filled many gaps of handling many remotely sensed analyses and applications due to its superior technical design compared to previous Landsat series of satellites. It collected more images at a substantially higher signal-to-noise ratio (S/N or SNR), higher spectral bands configuration, and higher radiometric resolution in a way that has made some analyses such as Time Series based Change Detection Analysis one of the most significant research topics of remote sensing worldwide. However, some researchers have been misconducted when they applied such analysis and ignored its rightful procedures. When performing bi-temporal or multi-temporal time series change detection comparison algorithms, a prior performance of several processing aspects should be produced such as dealing with radiometric resolution. A conversion process should be applied, if differential radiometric multi-temporal datasets occurred,. This research generates a parametrically simple ratio radiometric conversion (SRRC) method that can be applied successfully not only for the research's chosen experimented OLI dataset and for other Landsat satellite series imagery but also for different spatially, spectrally, radiometrically and temporally satellite datasets. Finally, a quantitatively assessing accuracy method is created in two phases: Standard Deviation-To-Mean Ratio & Subtraction Index (S.I). The final results are significantly promising and reached the perfect accuracy with a zero loss of data.