Satellite imagery has many practical applications in a number of industries. The technology behind satellite-based maps capable of classifying land cover type, detect change or assess the health of crops is called remote sensing.
Satellites use remote sensors to obtain information about diverse features of the Earth's surface, including vegetation cover, buildings, water surfaces, air temperatures, ground elevations, and many other characteristics.
Satellite remote sensors can be tuned to detect light in wavelengths that are invisible to a naked eye. Each
spectral band corresponds to a specific range of wavelengths that convey specific information about a feature of
interest. Combining images in visible range with different satellite bands helps us visualize otherwise
undetectable features. Thus, for example, in the middle-infrared range, the moisture content in vegetation,
forest canopy, and soil is highlighted in an image. On the other hand, to assess the health of a plant, it is
better to use the near-infrared range since healthy vegetation has a brighter reflectance in this spectrum.
Infrared and ultraviolet satellite bands can be used to measure chlorophyll in plants and tracking forest
fires, among other applications. Modern radar sensors can produce comprehensive 3D models of the Earth,
regardless of the cloud cover. This enables a more precise detection of changes in surface features.
Light reflectance measured across multiple spectral bands can be visualized on a graph as a so-called spectral
response curve. It allows you to see how bright the reflectance of the same feature or object in different
satellite bands is. For example, water surfaces mostly reflect light in the visible spectrum and very little to
none – in the NIR. On a graph, this would appear as a curve spiking in the Red, Green, and Blue spectral bands,
and dropping abruptly as it enters the near-infrared band column.
Let’s take a closer look at some of the most common satellite bands:
Satellite bands can be combined in multiple ways, depending on the feature that has to be highlighted in the
image. Since human eyes can only see within the visible range, most images are made up of combinations of Red,
Green, and Blue bands, to mimic the visible spectrum. This spectral band combination is known as the “true” or
When a particular surface feature needs to be highlighted in the image, satellite bands above and below the
visible range are used. Raw data undergoes processing to reduce the noise in the image. The result is a
so-called “false” color image that can highlight healthy vegetation, clouds, soil moisture, hot spots, and other
features. Using specially developed mathematical algorithms, the highlighted features can also be measured to
provide quantitative data. For example, we can calculate the amount of moisture in the soil, chlorophyll content
in the leaves, and more.
To increase the precision of data retrieved from a combination of multiple spectral bands, a coefficient-based
transformation can be applied to the image to produce an index.
NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) are indices commonly used to measure how healthy vegetation is. Other indices measure the degree of burn severity, presence of certain minerals, density of snow, water stress level, and more. New indices are continuously developed to generate more knowledge about our planet.
Other advanced analytical techniques are employed to extract even more data from satellite imagery in different
The result is a map (elevation map, land cover type map, productivity map, crop classification map, thermal
anomalies map, water stress map, and many other types) that visualizes data in a convenient way to generate
valuable insights into a specific challenge to be solved.
At EOSDA, we possess all of the technological and
human resources necessary for coming up with new advanced analytical satellite imagery solutions on request.
Contact us to learn more about our custom projects.
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