On false colors


On various occasions i have written about the problem of accurate colors in satellite images before. However what often makes satellite imagery such a seemingly complicated and difficult matter are usually not these subtle details of color fidelity and perception. What makes images frequently difficult to understand is that what is widely advertised as satellite imagery is often not images or photos is a strict sense but a broad range of visualizations and artwork (if you want to call it art) based on remote sensing data. Such pictures are generally indiscriminately presented as satellite images, often without explanation and no established boundary exists where the domain of photos ends and abstract visualizations begin.

Compare that with what you see in a newspaper for example – you have photos there, you have occasionally drawings of real world situations like from court room procedures where no cameras are allowed. And you of course have things like diagrams, caricatures and other graphics that are not meant to directly depict a real world view. Having these clearly distinguished types of pictures is in many ways central to the concept and self image of journalism.

Ironically science marketing and science journalism are one of the fields where this distinction tends to get blurred most, especially when it comes to satellite data.

It is difficult to make reliable estimates but i would say at least half of what you usually run across in newspapers, television, on websites etc. as satellite images are not photos in the sense of an attempt to create a reproduction of a certain imaging measurement with even the most rudimentary effort of faithfulness.

About half of these non-photographic satellite images are completely abstract depictions, usually some kind of 2d data set based on remotely sensed data visualized in some map coordinate system that allows you to recognize geographic features. This includes for example shaded depictions of relief data (yes, i have seen those being called ‘satellite image’) and radar data visualizations (which are usually based on run time/phase information and not a 2d imaging process).

The other half is what you usually call false color images and i want to discuss this topic in more detail here.

False color

False color images are a concept that predates digital imagery. It has its origin in classic chemical photography, specifically color infrared films that are sensitive in various parts of the visible and infrared spectrum but develop into visual range colors. This concept was then later reused in digital images.

To show what false color images mean first for comparison a Landsat image in normal visual colors:

This shows parts of the western Caucasus mountains with a large variety of surface features in the image. You can see good contrast between some surface types, like rock/snow and vegetation and even forests and grassland/agricultural areas can be well differentiated. On the other hand contrast between vegetation and water is rather small.

The visual range sensor channels of the satellite are here directly mapped to the red, green and blue channels of the image file and ultimately the display device. No matter how well the sensor sensitivities match the definition of the display color space and the sensitivities of the human eye – this is generally called true color or visual color. This does not necessarily mean the image shows realistic colors of course.

In addition to the blue, green and red color channels the satellites also record a number of other channels across the electromagnetic spectrum, most of them in the infrared (that is longer wavelengths than the visible light):

Just like with color infrared film you can map these to visual range colors to visualize these measurements. A traditional mapping is

NIR → red
red → green
green → blue

which suited early earth observation satellites which often had a green, red and near infrared channel and which is also close to the characteristics of color infrared film. Water surfaces are in much better contrast to vegetation here:

The important thing to realize is that since such a mapping has absolutely no physical basis it is ultimately completely arbitrary – you can map your data to any combination of visual range colors, as i for example showed in context of the ASTER instrument it is sometimes preferred to have the vegetation in green and not red so the same data can also be mapped like this:

And you do not even need to have a one-to-one mapping of the spectral channels to the red, green and blue color components – you can use an arbitrary linear or even non-linear mapping. For example here is a mapping that retains brightness from the visual range but defines the color tone using infrared data:

For false color images to be of any practical use and not just abstract art there need to be conventions on their definition and you need to learn to read them. This is a huge problem because the definitions are often quite hairy and the need to learn how to read these images is widely ignored and underestimated. Showing a false color image is a bit like printing an article in Chinese in a German newspaper – the main difference is that with the text the reader obviously realizes it is not understandable while with the false color image you can easily get a false impression of understanding. But in fact it is even worse – even with a lot of experience and training at using a certain type of false color image you always have a high risk of erroneously believing to correctly interpret certain features while this can easily be an optical illusion or a cognitive fallacy. Since training and experience with false color satellite image is always based exclusively on looking at satellite images and lacks the real life context of normal visual color perception it can never reach the level of reliability and robustness of reading true color images.

So with these immense disadvantages why do people none the less use false color images? This is because

  • there is a lot of information available in the infrared data that is not present in the visual bands,
  • using the full range of a color display and our visual perception can be helpful when visualizing complex multidimensional data and
  • the human mind can be trained to recognize similarities and patterns in color images, even if they are based on artificial colors.

Here is – for the area previously shown – the today most common false color band combination for multispectral satellite images. This is based on the mapping

SWIR → red
NIR → green
red → blue

This band combination is so popular because it manages to compress a real lot of information into a single color image. Since it does not use the blue and green visual range bands there is fairly little atmospheric influence, you can see quite well even through veiling clouds. For basic orientation the following basic properties:

  • Vegetation is generally green – different tones of green primarily indicate different vegetation density and different states in the growth cycle, different types of vegetation are often not so easy to distinguish.
  • Water is generally dark blue, dry snow and ice is bright blue to cyan, wet snow is darker.
  • Bare ground appears in various tones of brown, red and gray. Differences between different geological settings are often better differentiated than in the visible range, for example in the area shown here the limestone mountains south of the main ridge appear significantly brighter than the crystalline/metamorphic rocks further north. You need to be careful however since soil humidity also influences this quite a lot.
  • Clouds are usually fairly colorless bright gray and white although they can sometimes also be somewhat bluish or reddish.

Although this type of band combination is routinely used with data from many different satellites the results vary since where exactly the NIR and SWIR bands are located varies, much more than it does for the visual range bands which are obviously often tied to the human perception. Here is a diagram of current earth observation satellites, an extension of what i showed for the visual range previously.

There are a huge number of other false color band mappings – many of them variants of the SWIR-NIR-visual mapping above with either the other SWIR band or another visual range band substituted – these all look fairly similar at the first glance but vary in the details. There are also a few completely different mappings you occasionally see, for example the pure infrared mapping SWIR2-SWIR1-NIR and a blue-SWIR1-SWIR2 combination:


All of this is only with the most common spectral bands – ultimately the possibilities are endless and it is easy to turn this into a kind of secret code with images only a few initiated experts can read. But the use of such endeavors is highly questionable – as i hope i made clear reliably interpreting even the most common false color image types is full of difficulties. If you think certain things can be reliably determined from the appearance in a false color depiction it is often better to formulate this conclusion in a quantitative way based on the data values. This also has the advantage you can take into account more than the three variables you can independently represent in a color image.


To sum things up the following recommendations

  • Do not use false color satellite images in publications or marketing.
  • If you use false color satellite images publicly include also a true color image for reference and include a clear warning that colors are artificial.
  • Consider using single band images or single value processings like band ratios or difference indices, possibly with a well designed color gradient (no rainbow colors!) instead of a false color image.
  • When interpreting false color satellite images be wary of the possibility of misconceptions and verify your interpretation is consistent with the true color appearance if possible.
  • Be aware that there are serious differences between different satellites with equivalent false color band combinations.

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