With satellite imagery, there is far more than meets the eye. To the untrained observer, a satellite photo is just a picture of the Earth. But behind every pixel lies a deliberate balancing act of satellite engineering.

When designing and operating an optical constellation, providers must constantly optimize across three core dimensions of resolution:

• Spatial: The surface area that each pixel represents, which determines how sharp details appear.
• Temporal: How often the satellite can return back to an area of interest and capture a new image.
• Spectral: The ability of the sensor to distinguish differences in the electromagnetic spectrum. This can refer to the number and the width of the specific wavelengths the sensor measures.

Currently, no single satellite maximizes all three of these resolutions at once. So when you’re deciding which satellite imagery is best for your use case or analysis, it’s important to understand these technical characteristics, and how they work together.

Spatial Resolution in Satellite Imagery

Spatial resolution is the most common way to describe the level of detail in a satellite image. It refers to the area on the ground represented by a single pixel in a digital image. So if a sensor has a resolution of three meters, it means each pixel covers a three-meter by three-meter square of the Earth’s surface.

And as you would expect, the higher resolution, the greater the detail. High resolution can allow users to distinguish between individual buildings, roads, ships, or vessels. Lower resolution imagery usually appears more blurred, so it’s better suited to observing broad regional patterns rather than specific objects.

This is demonstrated in the images below, which compare an image of Newry, Northern Ireland, from three different satellites. The Sentinel-2 data on the left has the lowest resolution at 10 m per pixel, and is a bit blurry. While the Planet SkySat® image on the right, with the highest, at .5 m per pixel, is the sharpest.

While it may seem like a no-brainer to use the highest resolution available for all analyses, there are some trade-offs to consider. High-resolution sensors typically cover smaller areas, demand more storage and processing power to analyze, and can cost more, due to specialized instrumentation. So it really is important to know what resolution is “enough” for your needs.

Temporal Resolution in Satellite Imagery

The temporal resolution, also known as the revisit rate, refers to how frequently a specific area on the Earth is captured. This depends on the number of satellites in a constellation and their orbital paths. A single satellite might pass over the same point every few weeks, while a large fleet of small satellites can provide near-daily coverage of the entire Earth.

The image below compares the temporal resolution of two satellite constellations.

A high frequency is critical for tracking fast-moving events like floods, harvests, or construction progress. When multiple sensors work together, they create a continuous stream of information that reduces the gaps between observations. And constant monitoring ensures that change is detected as it happens, rather than weeks after an event has occurred.

Spectral Resolution in Satellite Imagery

Spectral resolution is the ability of satellite sensors to distinguish between different wavelengths, or bands, of light. ​​Common spectral bands used in Earth observation include visible light, near-infrared (NIR), and shortwave infrared (SWIR).

Visible bands capture red, green, and blue light to create images that look familiar to the human eye. Beyond that, NIR light allows satellites to see information beyond the visible spectrum. For example, healthy plants strongly reflect NIR light, much more than visible light. So, multispectral satellite sensors that capture NIR are valuable for tracking vegetation health.

Bands in the SWIR part of the spectrum can detect heat or moisture levels in the soil. And Planet’s Tanager hyperspectral satellite images over 400 spectral bands, allowing for precise measurements of atmospheric emissions, like methane and ammonia. The graphic below shows two methane plumes detected using hyperspectral data from the Planet Tanager-1 satellite.

Learn More

You can read more about the specifications and resolutions of Planet satellites on the Planet website. And on the Planet documentation site you can find more specific information and sample datasets that demonstrate the power of each of Planet’s constellations.

In addition, to learn more about satellite imagery, consider visiting Planet University to take the free Introduction to Remote Sensing course.