Multispectral Imagery

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Throughout your studies, we've looked at several different types of satellite imagery, each using a different wavelength of electromagnetic radiation to produce an image. Notably, each type of "conventional" satellite imagery (visible versus infrared, for example) uses a single wavelength (or "channel") to produce the image. But, as we already know, using different types of imagery (different "channels" from the satellite's imager) helps forecasters gain different information (about upper-level winds versus lower-level winds in the case of cloud-drift winds, for example). It turns out that combining multiple channels on the same image can sometimes help forecasters better identify certain features and cloud characteristics. Let's explore these "multi-channel," or more formally, multispectral images, which use multiple wavelengths of the electromagnetic spectrum.

Polar-orbiting satellites have long played a pivotal role in remote sensing of tropical weather systems using a multispectral approach. You may recall that polar orbiters fly at much lower altitudes (opens in a new window) than geostationary satellites and are "sun synchronous" (meaning that they ascend or descend over a given point on the Earth's surface at approximately the same time each day). Multiple fleets of polar orbiting satellites currently circle the Earth (more in the Explore Further section below). Instruments aboard these satellites collect data at multiple wavelengths ("channels") across the visibile and infrared portions of the electromagnetic spectrum, which allows us to collect information about day and nighttime cloud cover, snow and ice coverage, sea-surface temperatures, and land-water boundaries. However, with improvements in geostationary satellites, including better resolution, they're also a common source of multispectral satellite composites. By the way, if you're interested in exploring the topic of satellite resolution in more detail, check out the Explore Further section below.

One product that has long been common in tropical forecasting is a three-channel color composite like the one below captured by a polar orbiting satellite, showing Category 5 Hurricane Katrina at 2011Z on August 28, 2005. The yellow shading in Katrina's eye really stands out, doesn't it? 

That yellowish shading corresponds to low-topped, relatively warm clouds within Katrina's eye (remember that the eye of a hurricane often contains low clouds). Meanwhile the thick, tall convective clouds with cold tops surrounding the eye appear bright-white on this three-channel color composite, and high, thin cirrus clouds appear blue/white.

How does it work?

How are useful color composites like this one created? The process can vary depending on the type of multispectral image, but most commonly, three individual channels are combined, with a filter being applied to each. To illustrate, let's walk through the scheme used to make the image above. It's not really all that complex. First, we start with a standard grayscale visible image, near (or "shortwave") infrared, and infrared images (check out the top row of satellite images in the graphic below). Next, we apply a red filter to the visible image, a green filter to the near-infrared image, and a blue filter to the infrared image, and we get strange looking satellite images like the ones in the second row of the graphic below. But, if we combine those "false-color" images together, we get a three-channel color composite!

Credit: NOAA / David Babb @ Penn State is licensed under CC BY-NC 4.0 (opens in a new window) (opens in a new window)

Breaking down this three-channel color composite helps us to understand why high, thin clouds appear in blue on the final product like the image of Hurricane Katrina -- they're brightest on the infrared channel (which had blue hues added to it). Meanwhile, tall, thick convective clouds that show up bright white on the final product are bright on the individual images from all three channels, and low clouds appear yellow because they're brightest on the visible (red) and near-infrared (green) images. The combination of green and red provides the yellow shading (if you're curious about why yellow results, you may want to read about additive color models (opens in a new window)).

This final color scheme (yellow for low clouds, white for tall, thick convective clouds, and blue for thinner cirrus) isn't universal, however. For another example of a three-channel color composite from a geostationary satellite, check out the image below. This particular image shows Tropical Storm Arthur near the Texas Coast on June 17, 2026. This composite was created using two different visible channels and one infrared channel. Using these channels, this type of composite (formally called a "Day Cloud Phase RGB") helps to discern between low clouds composed of liquid water (in blue), clouds where ice crystals are starting to form (in green), tall, convective clouds (yellow), and thinner cirrus clouds (reddish-orange). Given the presence of thick convective clouds and cirrus in the large convective cluster over the Gulf, the clouds take on a a reddish-yellowish appearance.

Credit: College of DuPage

At this time, Arthur was a highly sheared system, with all of the deep convection displaced well to the east of the low-level center (the surface center of low pressure is marked with an "L."). The fact that lower clouds comprised of liquid drops appear in blue on this type of image helps forecasters quickly recognize the displacement between the low-level center and the deep convective clouds (appearing reddish yellow) over the Gulf. At the time, Arthur was obviously not a "healthy" storm.

The multispectral approach ultimately made it much easier to diagnose cloud types and discern the low-level circulation from the rest of the storm than any single visible image or infrared image. The image slider below helps to illustrate this. The first image is a conventional visible image of Arthur at the same time as the multispectral image above. If you advance the slider to the second image, you'll see the corresponding enhanced infrared image, on which it's very difficult to pick out circulation in the low clouds. Finally, advancing the slider to the final image shows the multispectral image above for comparison. The striking color contrasts help forecasters get a quick read on the sheared structure of the storm.

I should point out that many types of multispectral images exist, beyond three-channel color composites like the ones I've already described. Indeed, another example is so-called "sandwich" imagery, which combines one visible channel and one infrared channel to provide the high-resolution details of visible imagery, along with color enhancements to accentuate cold cloud tops (from infrared imagery). The result can be a visually spectacular image where you can see the bubbly texture of cumulonimbus clouds along with infrared color enhancement (see Arthur's corresponding sandwich image (opens in a new window), for example). So, I've just scratched the surface here (we haven't covered all types of multispectral images), but hopefully you can see how the multispectral approach can make it easier to pick out specific details of tropical cyclone structure and discern details about cloud type and altitude, even though color conventions may vary.

Specifically, when a tropical cyclone is highly sheared like Arthur was, the color scheme of three-channel color composites can really expose the structure of the storm. For another striking example, check out this loop of three-channel color composite images of a weak tropical depression (opens in a new window) in the Atlantic. Not long after the storm was classified as a depression, the deep convective clouds (bright white) got displaced to the northwest thanks to strong southeasterly wind shear. The yellow swirl of clouds left behind clearly marks the storm's low-level circulation. The completely exposed low-level circulation signaled that intensification wasn't in the cards for this storm (in fact, it never did become a tropical storm and dissipated entirely a couple of days later). 

There's no doubt that this multispectral approach to satellite imagery can produce some striking and very insightful images, and in case you're wondering, the false-color approach of multispectral images has a number of other applications. The Hubble and James Webb Space Telescopes (opens in a new window) employ a similar approach, as do polar-orbiting satellites that study features on the Earth's surface. But, for analyzing tropical cyclones, the uses of multiple wavelengths of electromagnetic radiation don't stop with what we've covered here. It turns out that other remote sensing equipment aboard polar-orbiting satellites can detect things like rainfall rates, temperatures, and wind speeds by employing different wavelengths of radiation. We'll begin our investigation of those topics in the next section.