Understanding how satellite images are created begins with sensors. Satellite sensors, unlike our eyes, capture much more information and relay it back to us in a format quite different from the photographs we are used to.
Satellites capture data by assigning a digital value to each pixel based on the reflectance of the corresponding area on the ground (and above the atmosphere) within a predetermined band within the light spectrum captured by the sensor. When presented as an image, a high reflectance means a high value, and a low reflectance means a low value. Thus, satellite data is in black and white before processing.
A snowy mountain top, for example, will appear white in all light spectrum images, as snow reflects all visible light. However, it will appear darker in infrared images since the snow has less reflectance in infrared.
Creating composite images
Colour composites are produced by merging data from several bands of the spectrum and assigning a colour to each band. As even natural colour images tend to have low contrast and a blue, hazy hue — making it hard to distinguish features — most satellite images you see have been altered in some way.
In a true-colour composite image, such as the ones found on Google Maps, red, green, and blue bands are gathered by satellites to simulate the range of vision for the human eye, showing us images that are closer to a photograph.
Since Google Maps images are the result of multiple satellites passes on different days (to capture the entire scene and ensure there are no clouds), they have also been digitally fused to smooth out the transitions between one image to another and make it appear as one large image to the users.
Satellites also capture information in the non-visible spectrum of light. In each band, different features are reflected differently, such as rock, bare soil, vegetation, burned ground, snow, sediment-rich water, etc. This is called a 'spectral signature'.
False-colour composite images are created by substituting one or more of the RGB bands for others, such as infrared or near-infrared, which are not visible to the human eye.
Mathematical models can also be applied to the data to create a new kind of processed image, known as an index, which better distinguishes between features and highlights changes over time.
For more information on satellite imagery, read our Ultimate Satellite Imagery Guide.