Band Combinations for Landsat 8

Landsat 8 has been online for a couple of months now, and the images look incredible. While all of the bands from previous Landsat missions are still incorporated, there are a couple of new ones, such as the coastal blue band water penetration/aerosol detection and the cirrus cloud band for cloud masking and other applications. Here’s a rundown of some common band combinations applied to Landsat 8, displayed as a red, green, blue (RGB):

Natural Color 4 3 2
False Color (urban) 7 6 4
Color Infrared (vegetation) 5 4 3
Agriculture 6 5 2
Atmospheric Penetration 7 6 5
Healthy Vegetation 5 6 2
Land/Water 5 6 4
Natural With Atmospheric Removal 7 5 3
Shortwave Infrared 7 5 4
Vegetation Analysis 6 5 4

Here’s how the new bands from Landsat 8 line up with Landsat 7:

Landsat 7

Landsat 8

Band Name Bandwidth (µm) Resolution (m) Band Name Bandwidth (µm) Resolution (m)
Band 1 Coastal

0.43 – 0.45


Band 1 Blue

0.45 – 0.52


Band 2 Blue

0.45 – 0.51


Band 2 Green

0.52 – 0.60


Band 3 Green

0.53 – 0.59


Band 3 Red

0.63 – 0.69


Band 4 Red

0.64 – 0.67


Band 4 NIR

0.77 – 0.90


Band 5 NIR

0.85 – 0.88


Band 5 SWIR 1

1.55 – 1.75


Band 6 SWIR 1

1.57 – 1.65


Band 7 SWIR 2

2.09 – 2.35


Band 7 SWIR 2

2.11 – 2.29


Band 8 Pan

0.52 – 0.90


Band 8 Pan

0.50 – 0.68


Band 9 Cirrus

1.36 – 1.38


Band 6 TIR

10.40 – 12.50


Band 10 TIRS 1

10.6 – 11.19


Band 11 TIRS 2

11.5 – 12.51


For the most part, the bands line up with what we’re used to, with some minor tweaking of the spectral ranges. The thermal infrared band from Landsat 7 is now split into two bands for Landsat 8. Whereas before you had one thermal band that was acquired at 60 m resolution (and resampled to 30 m) now you have increased spectral resolution at the cost of spatial resolution. It wouldn’t be remote sensing without tradeoffs, right?

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Leave a Reply


  1. franzpc says:

    Do you know if I can make a DEM with Band 8 Pan? How to …

    • kevin_butler says:

      Short answer is no. Landsat would have to have a forward and backward looking band to generate a stereo image for dem purposes. All of its bands are at nadir. Check out Aster (30 m globally) or SRTM (90 m globally).

  2. joabelb says:

    Hi Kevin. First of all congratulations for this excellent post.
    I would like to know if ESRI is planning to release a basemap of Landsat 8 images (time enabled).
    I just saw something like that in this website . It would be awesome to have this basemap on my ArcGIS Desktop…

  3. alexanderwandl says:

    Hi Kevin,
    I have a question concerning the calculation of the NDVI. switching the bands 3 to 4 and 4 to 5 in the image analyses extension doesn’t provide reasonable results, any advice?

  4. horizonsweb says:

    How can I combine the Landsat 8 bands in ArcGIS 10.1?

    • kevin_butler says:

      If you’re working with one Landsat scene, load all of the bands onto your map. From the Image Analysis Window, select all of them and then click the composite bands function in the processing pane.
      If you have multiple scenes, the easiest way to do this without scripting anything is to create a mosaic dataset of each band and then use composite bands on each of the mosaic datasets.

  5. William says:


    May I ask what is the best band combination to display a scene where the land cover includes forest, bare soil, crops and urban areas

    • kevin_butler says:

      If the true color composite doesn’t work for you, I would try something along the lines of 7 6 4. Bands 2 or 3 could also work in the blue band. If the vegetation isn’t sufficiently highlighted, try switching the green band from 6 to 5.

  6. naveenjayanna says:

    How to Layer Stack Landsat 8 bands……

    plz respond soon……

    Thank you

  7. gauravhegde24 says:

    Hello Kevin,

    I needed guidelines on how a particular object/class looks like in various band combinations for landsat images.
    for example crop looks bright red, fallow land looks yelow in fcc..

    I need this for image classification. Hope I can get some reference for all major band combinations.

    • kevin_butler says:

      I hate to break it to you, but it’s really not that simple. There are a ton of factors that go into how features look, and if you want to do a classification, me telling you that vegetation is bright in the NIR is not going to get you where you want to be. My suggestion is load up the imagery basemap from Arcgis Online and use that as a reference. Some stuff may have changed if there is a big difference in when the landsat imagery and the highres imagery was acquired, but most things are consistent. When you see a feature that you’re interested in, start playing with the band combinations and get a feel for how those features change as you change the band combinations. What are you classifying for?

      • gauravhegde24 says:

        Thanks for the suggestions Kevin.
        I am trying to classify a Landsat8 image to detect temporal changes in a mining area.
        I am trying to figure out how to be sure about signature pixels I choose..


        • kevin_butler says:

          You won’t be sure about your training sites until you actually run the classification. It tends to be an iterative process that involves a lot of tweaking to the training sites until you are satisfied with the output. Start with pixels that seem representative of the feature you want to classify. If they are too “pure” you’ll miss classify some of the mixed pixels. If they’re too “mixed” you’ll miss out on the pixels that only have that feature. It becomes as much of an art as science at this point.

          If you use the MLClassify function (from the image analysis window, not the geoprocessing tool) it will display your results on the fly. I find that this saves time when I’m going through and trying to perfect a classification.

  8. mervynlotter says:

    Hi Kevin
    Two questions if I may, (1) using the new segmentation functionality in 10.3 and ArcGIS Pro, I would like to better understand the the ‘spatial detail’ properties. Reading the help file suggests that it is similar to spectral detail (which I do understand). Could you please try and differentiate between them for me or at least expand on the ‘spatial detail’? And (2) with segmentation I am limited to 3 bands in the analysis, which 3 bands would you recommend for classification of vegetation communities? 654?
    Love your work …

    • kevin_butler says:

      Hi Mervyn,

      Great question! It’s a subtle nuance. Basically, spatial detail is used when you have a very mixed scene. For example, in an urban scene, you could classify an impervious surface using a smaller spatial detail, or you could classify buildings and roads as separate classes using a higher spatial detail. So you have lots of different features and you want to recognize them as different. If you lower the spatial value, you will get a smoother output.
      Spectral detail is used to differentiate between features that are close together and spectrally similar. Looking at a forest and want to pick out tree species? Set this to a high value.
      As for the bands, near infrared and shortwave infrared are going to give you the most information about the vegetation. The third band depends on the other dominant features in your image. Don’t worry about water, but if you have a lot of soil, you might want to try the red band.
      Life pro tip: Zoom in to 1:1 and use the raster function to spot check your segments before running the geoprocessing tool. You do not want to run this function on an entire Landsat scene! Unless of course, you get paid by the hour and can log computational time. The way to do this is zoom in to an area of interest at a 1:1 resolution and then apply the segmentation function. Tweak the spectral and spatial parameters accordingly. Then, turn that layer off in the table of contents. Move to another area and repeat until you’re happy with the spectral and spatial detail levels. Then run the geoprocessing tool to create the segmented image that you will use for classification.

  9. aryan1992 says:

    How can we derive the vegetation indices for Landsat 8 data?

  10. nadaalansari says:

    Hi Kevin,

    Please, how can I download Landsat8 image?
    Thank you in advance.

  11. nourmadi says:


    i have a question regarding landsat 8 bandwidth ranges. some ranges are not clear whether they are inclusive to the value or not.
    eg. band 1 [0.43-0.45] and band 2 [0.45-0.51]. in this case which band does the value of 0.45 belong to?
    second, some values are not included in any range.
    eg. value of 0.52 falls between band 2 and band 3. (similar to other values such as 0.60,0.61,0.62,0.63….)

    how can you explain these ranges and is there a remedy to include all the values?

    thank you

    • kevin_butler says:

      Hi Nour,

      To answer your first question about where one band ends and the other begins, what the sensor is doing is collecting all of the energy within that range, so .045 can be in both.

      To answer your second question about the gaps, it’s something that is actually better to not collect energy for the entire electromagnetic spectrum. If you go back and look at the band ranges Landsat 1 through Landsat 8, you’ll see that the ranges get narrower. The remote sensing community has figured out over time that you get a better signal to noise ratio within these narrower ranges. Here’s a USGS site with the band specs:

  12. anhpt200 says:

    here I make video clip for combining landsat bands

  13. vivian22 says:

    Amzin post.
    I have a question, I am trying to detect temporary changes in my area. I would like to know how I can subtract with “Map Algebra” between two images if they are of different Landsat (7 and 8)

  14. marton_tud says:

    I would like to show my student what are wave length values in pixels of Lansdat 8 band rasters. How to calculate new raster with real wave length from downloaded raster?

  15. moderhassan says:

    can you help i study master i need to some information