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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?

  • Don't have an answer to your question but this paper could be useful sciencedirect.com/science/article/pii/… – Walshe_d Nov 13 '18 at 21:58
  • Thanks for the input. I'm already familiar with this paper and it indicates that normlization via OLS-regression (you can apply this via the relnorm()-function in R apparently) does indeed work. My issue however is, that in this paper they use the old Level 1 products and not the new Level 2/surface reflectance ones. I'm wondering if normalization is also possible with the Level 2 images, given the fact that those are generated with different correction algorithms. – M. Sen. Nov 14 '18 at 13:53

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