I want to do a time-series analysis on Landsat surface reflectance data for an extended period of time (2005-2018), which requires integration of Landsat 5, 7, and 8. Is there any procedure to make the data from ETM+ and OLI sensors compatible so they can be used in a single time series together? I looked at the scholarly publications but only found one paper dated 2014, suggesting a linear regression technique for converting OLI to ETM+.


  • Your question is unclear. What you want as the output? Is there any procedure to bring the data from different sensors to the same ground? (the ground is the same). Commented Feb 25, 2019 at 18:45
  • By "ground" I didn't mean the Earth. Sorry for confusion, I edited my post to make my question more clear.
    – Shahriar49
    Commented Feb 25, 2019 at 22:21

2 Answers 2


This can be accomplished in Google Earth Engine. See this tutorial on cloud masking and harmonizing a time series of Landsat surface reflectance images from TM, ETM+, and OLI sensors. It includes inter-sensor harmonization method citation from Roy et al., 2016.



You do not really give enough information. The systems are generally highly comparable and designed this way. If you are using unsupervised or supervised approaches you may need to do no changes at all. The same for other landscape methods. If you are trying to get identical TOA or SR values from the same ground pixel in the same bands then you many need some adjustment on some bands, although it is very nominal on the most often used bands.

The regressions equations provided in your link are for one country and a type of landscape. They cannot be applied elsewhere. It is also an MDPI journal.

Differences between the two systems appear entirely nominal in regards Top-of-Atmosphere and Surface Reflectance for the visual bands For almost all cases the following bands can be used as is:


2 - 1

3 - 2

4 - 3

6 - 5

Adjustment For the band below a linear regression adjustment is suggested in the MDPI paper but it is time, locations, and landscape specific; but as I said it may not be required depending on your approach and it may not work on your landscape.

5 - 4 (regression equation from your link)

Flood, Neil. (2014). Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape. Remote Sensing. 6. 7952-7970. 10.3390/rs6097952.

  • Many thanks for your reply and important point about the paper (being location specific). Is there any other problem with MDPI journal?
    – Shahriar49
    Commented Feb 26, 2019 at 2:13
  • And I knew the figure you gave above. As you see, only B, G, and R bands are good match between ETM and OLI and the bandwidth of other bands (especially for NIR and SWIR-1 bands) are very different. How the measurements can be compatible then?
    – Shahriar49
    Commented Feb 26, 2019 at 2:15
  • @Shahriar49 MDPI are considered a predatory publisher by many. They used to be on the predatory list. en.wikipedia.org/wiki/MDPI Commented Feb 26, 2019 at 4:00
  • @Shahriar49. As they do not need to be. As long as they are internally consistent to themselves then you can use them in most LULC change approaches. You are not usually interested in the values but what the values represent. Commented Feb 26, 2019 at 4:03
  • 2
    Regardless of their association with MDPI, the journal Remote Sensing has a fairly good reputation, impact factor with many good peer-reviewed articles so, please do not intimate that it is an invalid journal. And, no i do not have any affiliation but have reviewed a few papers for tham and guarantee you that it was treated as any other of the couple dozen journals i regularly review for. Commented Nov 23, 2019 at 18:28

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