I downloaded TM, ETM+ and OLI Surface Reflectance Higher-Level Data Products (atmospheric corrected) based on L1TP Landsat images (radiometrically calibrated, orthorectified). I need to include images from those three different sensors to get a complete time series for my study area.
Based on the description given by the USGS and a very helpful paper i read regarding pre-processing (https://www.researchgate.net/publication/312202874_A_survival_guide_to_Landsat_preprocessing) i conclude that most of the usual pre-processing steps are unnecessary in this case by simply using the Level 2/Surface reflectance product, except for:
1) Cloud masking 2) Radiometric Normalization to account for radiometric differences between the three sensors and their different atmospheric correction methods (LEDAPS and LaSRC)
Now my question:
Will a "Relative Radiometric Normalization" (f.ex. PIF or the relnorm() function in R) help me to get rid of the aforementioned differences and make a time series analyses based on these different sensors possible?