What resolution do I need when using satellite Earth observation data?

Trick question: it depends on what you are trying to do.

What does resolution mean?

When it comes to Earth observation, you might hear about spatial resolution, spectral resolution, and temporal resolution. While all three need to be considered when looking for the satellite data, most often, when people ask about resolution, they mean “spatial resolution”.

Spatial resolution is the size of one pixel on the ground. Pixel stands for 'picture element' – the smallest individual 'block' that makes up the image. With a finer spatial resolution, 30 cm for example  – where each pixel represents a 30 x 30 cm area, for optical data – you would be able to distinguish details, such as houses or cars. With a coarser resolution, an image of a similar digital size would cover a much larger surface on Earth and smaller features become harder to distinguish.

Note from the SkyWatch SAR expert: The above definition only applies to optical data. Synthetic aperture radar data (SAR) is not acquired at nadir like optical data but rather on a slant. Therefore the data is in slant range and the pixels on the ground are not square.

To better illustrate, here are two satellite pictures of the same location (Burj Khalifa, Dubai) taken with different spatial resolution sensors. On the left, a 30 cm resolution from Triple Sat Constellation, on the right, a 15 m resolution from Landsat-8.

A side-by-side comparison of the Burj Khalifa, as imaged by the Triple Sat Constellation at 30 centimeters spatial resolution and by Landsat-8, at 15 meters spatial resolution. The image taken at the coarser resolution makes it hard to distinguish smaller features on the ground, like roads, trees, or buildings.

Spectral resolution is related to the granularity of the breadth of coverage of the electromagnetic spectrum captured by the satellite sensors. A finer spectral resolution can discriminate between narrower bands of wavelength, differentiating, for example, between red, green, and blue bands and allowing for coloured images.

A chart of the different spectral bands and their wavelength: gamma rays, X-rays, UV, visible light, infrared, radar, FM, TV, shortwave, and AM.

Satellite sensors are able to capture data that would be invisible to the naked eye and a higher spectral resolution can provide us with a different view of objects and landscapes. For example, the shortwave infrared ranges enable highly effective geological mapping, because rocks and minerals have their own spectral pattern in these bands.

Temporal resolution refers to the time elapsed between viewings of the same area on Earth at the same angle. It can range from continuous coverage for geostationary platforms – such as a weather satellite, set at a fixed point over the Earth’s surface – to several days between revisits for low earth orbiting platforms (LEO). A higher temporal resolution means a shorter revisit time.


Spatial resolution for Earth observation satellites in the early 1980s  was around 80 m – as was available on Landsat-4. Now, you can find remote-sensing data to purchase with spatial resolutions as low as 30 cm. For open data, some of the finer sensor can capture up to 15 m resolution images.

The spectral resolution also improved drastically over the past few decades, as sensors were refined and more bands became available for study. Some of the most recent satellites sensors can now capture information on more than 1,000 different spectral bands.

As for temporal resolution, it is still very much varies based on the satellite. However, if you are interested in data regarding one specific area, the sheer amount of satellites that were launched has increased your chances of obtaining multiple, non cloud-covered, usable pictures.


Our advice, when doing remote-sensing data analysis or building a space app: think first about what you are trying to achieve and what resolutions you need to solve your business problem.The most detailed spatial resolution may not always be the best.

For example, a consortium, led by the Joint Research Centre (JRC) has successfully combined data from multiple coarser resolution satellites to monitor forest fires, using each satellite to compensate for the deficiencies from the other sensor – cloud perturbations for Sentinel-2 and sensitivity to ground moisture for Sentinel-1.

Continuous improvements in sensors, as well as the higher amount of available satellite data have helped dramatically expand the applications of satellite imagery and the possibilities are now almost limitless.

What resolution do I need when using satellite Earth observation data?

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