A primer on false-color imagery

May 12, 2023 by Katie Tyler
Imagery Credit: USDA NAIP 2021 Near Infrared
Table of Contents

In this third installment of our education series, we are diving into the world of false-color imagery! This is where remote sensing really starts to give us, mere humans, superpowers. Typically, we only have access to visual information from the visible light portion of the electromagnetic spectrum. With false-color imagery in remote sensing, however, we get to ~expand our perceptual horizons~ by peering into the infrared portion of the electromagnetic spectrum. 

What is False-color imagery?

You may or may not be familiar with the concept of truecolor imagery. This is often the bread and butter of remote sensing imagery. These truecolor images display visible light (the red, green, and blue bands) in their true colors, meaning that the red wavelength captured by the satellite is then displayed on your computer screen using red light, the green band is displayed in green light, and the blue band displayed in blue. In simple terms: an ocean will look blue, and a healthy tree will look green. These images are therefore more intuitive to interpret given that objects appear in the typical colors we have come to associate with them.

False-color, on the other hand, doesn’t necessarily match wavelengths to their respective colors for display. This is because false-color, also referred to as ‘color infrared’, involves the display of one or more bands from the infrared portion of the electromagnetic spectrum. Since we cannot see infrared light, we use visible light (red, green, or blue) to show the amount of reflectance in that portion of the spectrum. 

There are different types of false-color imagery, using different combinations of infrared and visible light bands. The rest of this article will cover the types of false-color imagery available in Lens.

Truecolor; NIR, Red, Green; SWIR, NIR, Green; Blue, SWIR. Copernicus Sentinel data 2022.

NIR, R, G (Near Infrared, Red, Green)

The NIR, Red, Green false-color imagery displays the near-infrared portion of the spectrum (also referred to as a ‘band’) in red light, the red band using green light, and the green band using blue light. 

Tip: The names we use for false-color in Lens always list the bands that are being displayed in the order of being displayed using red, green, and blue light.

What does this actually mean for interpreting the NIR, Red, Green false-color imagery? 

  • Healthy vegetation will appear bright red. This is because healthy vegetation has very high reflectance in the infrared portion of the electromagnetic spectrum, much higher than in the green visible light portion even. And since infrared is displayed with red light, vegetation is… you guessed it, bright red!
  • Clear water will appear black and sedimented water will appear more blue or cyan.
  • Bare ground and urban areas will appear tan.

This layer can be particularly useful for differentiating between bare ground and sedimented water. In truecolor imagery, bare ground and sedimented water may appear in identical shades of brown or tan. In this false-color layer bare ground will still appear tan, but sedimented water will appear dark blue or cyan. This means we can more easily identify the extent of flooding, like in this example from the 2022 Yellowstone floods.

Another helpful application of this particular false-color layer is using it to distinguish deciduous and coniferous trees. Deciduous trees have more photosynthetic activity and reflect more in the near-infrared band, meaning they will appear brighter red compared to the slightly darker coniferous trees. Using this layer we can go from an entirely green truecolor image, to a high contrast image with bright and dark red showing us deciduous and coniferous trees - it’s almost like magic (or science 😉)!

SWIR, NIR, G (Shortwave Infrared, Near Infrared, Green)

The SWIR, NIR, Green false-color imagery displays the shortwave infrared band in red light, the near-infrared band using green light, and the green band using blue light. 

So, how do we interpret the SWIR, NIR, Green false-color imagery? 

  • Healthy vegetation will appear in bright - almost fluorescent - green. Again, this is because healthy vegetation has very high reflectance in the infrared portion of the electromagnetic spectrum, which in this false-color layer is displayed in green.
  • Clear water will appear black and sedimented water will appear blue. This layer, like the NIR, Red, Green layer, can be useful for monitoring floods and water extent because of the high contrast between water (even sedimented water) and vegetation or bare ground.
  • Bare ground will typically range from tan to pink and developed areas tend to range from white/silver to a grayish purple. 

Perhaps the most useful application of this layer is looking at active fires and recently burned land. Recently burned areas will reflect high amounts of shortwave infrared, so they will appear brighter red than their surroundings in this false-color imagery. This makes it easy to quickly identify burn scars and the extent of fires, like in this example from the 2022 NCAR fire near Boulder, CO. 

Taking it one step further, if the timing of imagery is right, we can even spot active fires with this false-color imagery. Hot areas, like lava flows or active fires, reflect very high amounts of shortwave infrared meaning they will be a glowing red or orange. In the example below from the 2018 Ferguson Fire in California we can see through the smoke much more clearly than in the truecolor image to precisely where there is active burning. We can even tell that the fire is moving northwest based on the presence of bright green vegetation to the northwest and recently burned red vegetation to the southeast.

B, SWIR (Blue, Shortwave infrared)

The Blue, SWIR false-color imagery displays the blue band in red light and two different shortwave infrared bands, one in green light and one in blue light.

With this layer, water (in liquid form) will appear very dark, almost black. Why specify liquid form? Well, this layer is most useful in differentiating snow, ice and clouds. Ice will appear bright red, while snow will range from orange to red, and clouds will usually appear white ranging to dark peach. In the example below, we see that in the truecolor imagery it is challenging to identify where the snow ends and clouds begin. In the false-color imagery, though, we see a stark contrast between the white clouds and red snow.


I hope this primer on false-color imagery and the examples shown above have you keen to get into Lens and explore your properties through the world of infrared and false-color. With our new flexible compare mode, you can easily compare truecolor and false-color imagery side by side (like in all of the examples of this article) to combine forces and the value of both types of data for increased and improved insights! And, of course, remember that remote monitoring is a skill like any other that can improve with practice - the more time you spend with these false-color layers, the more confident you’ll be interpreting these layers. And before you know it you may even find other use cases specific to your properties with these layers.

Up next, we’ll move on from false-color imagery to using infrared for advanced insights with index layers - stay tuned!


Riebeek, Holli. “Why is That Forest Red and That Cloud Blue?” NASA Earth Observatory, 4 March 2014. https://earthobservatory.nasa.gov/features/FalseColor